Hydraulic support concentrated solution state on-line monitoring intelligent supplementing system

Abstract

The invention relates to the technical field of hydraulic support auxiliary equipment monitoring, in particular to a hydraulic support concentrated solution state online monitoring intelligent supplementing system which comprises a basic monitoring unit, a core calibration unit and an intelligent supplementing control unit. A basic monitoring unit collects multi-position flow velocity signals and sampling point concentration original data through a circulating pipeline built-in sensor, and a core calibration unit constructs a layered flow velocity weight model based on pipeline fluid dynamic characteristics, divides a central high flow velocity area and a near-wall low flow velocity area, dynamically distributes concentration weight coefficients of the two areas, and performs calibration on the flow velocity signals and the sampling point concentration original data. Data are processed through a weighted fusion algorithm, an actual concentration estimated value is output to eliminate detection negative deviation caused by flow velocity layering, an intelligent supplement control unit takes the value as a target threshold value to construct a trend prediction model, and when the concentration is continuously lower than the threshold value, a dual-constraint mechanism is started to generate a valve opening instruction; the concentration of the concentrated solution is stably maintained in an anti-rust and anti-foaming critical threshold range, and stable operation of the hydraulic support is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of monitoring technology for hydraulic support auxiliary equipment, and more specifically, to an intelligent online monitoring and replenishment system for the concentrated fluid status of hydraulic supports. Background Technology

[0002] Monitoring of hydraulic support auxiliary equipment is an important technology, specifically applied in the concentration control of hydraulic support concentrate in coal mine scenarios. The core is to ensure that the concentrate is maintained within the concentration range that meets the requirements for rust prevention and foam prevention by monitoring the concentrate status in real time and intelligently adjusting the replenishment amount. Existing hydraulic support concentrate monitoring and replenishment technologies face core problems in practical applications, such as distorted concentration monitoring and improper replenishment control, leading to unstable rust and foam prevention effects of hydraulic supports. The key reason for this problem is that existing technologies do not consider the stratification characteristics of flow velocity within the circulation pipeline, relying solely on concentration data collected from a single sampling point. However, the flow velocity is high and the concentrate is uniformly mixed in the central region of the pipeline, while the flow velocity is low near the wall, making it prone to additive deposition. A single sampling data point cannot reflect the true concentration distribution in different flow velocity zones, easily causing deviations in actual concentration estimation. Furthermore, replenishment control is based solely on fixed concentration thresholds, without constructing a mechanism to predict concentration change trends or adjusting the replenishment amount based on historical replenishment exceedances. When the concentration drops, either replenishment is not timely, resulting in a concentration below the rust and foam prevention threshold, or excessive replenishment leads to concentrate waste, making it difficult to meet the mine's requirements for stable operation of hydraulic supports. To solve this technical problem, we provide an intelligent online monitoring and replenishment system for hydraulic support concentrate. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent replenishment system for online monitoring of the concentrated fluid status of hydraulic supports, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, an intelligent online monitoring and replenishment system for hydraulic support concentrate is provided, comprising: Basic monitoring unit 1 acquires flow velocity signals at multiple locations and raw concentration data at sampling points through sensors built into the circulation pipeline; The core calibration unit 2 establishes a stratified velocity weighting model based on the fluid dynamics characteristics of the pipeline and divides the pipeline cross-section into a high-velocity zone in the center and a low-velocity zone near the wall. The concentration weighting coefficients of the two regions are dynamically allocated according to the velocity signals at multiple locations. The weighting coefficient of the high-velocity zone increases with the measured velocity value, while the weighting coefficient of the low-velocity zone decreases with the increase of the risk of additive deposition. Finally, the raw concentration data of the sampling points are processed by a weighted fusion algorithm to output the estimated value of the actual concentration and eliminate the negative detection bias caused by velocity stratification. The intelligent replenishment control unit 3 uses the estimated actual concentration as the target threshold and constructs a trend prediction model based on the rate of concentration change. When the concentration is continuously lower than the target threshold, a dual constraint mechanism is activated. The first constraint generates the basic replenishment amount based on the magnitude and duration of the negative concentration deviation. The second constraint superimposes the concentration change gradient suppression function and the historical replenishment over-limit compensation factor to compress the basic replenishment amount, form the theoretical replenishment amount, and generate a valve opening command to ensure that the concentration of the concentrate is stably maintained within the critical threshold range for rust prevention and foam prevention.

[0005] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention collects flow velocity signals and raw concentration data from sampling points at multiple locations in the circulating pipeline through a basic monitoring unit. The core calibration unit constructs a layered flow velocity weight model based on the pipeline's fluid dynamics characteristics to output accurate estimates of the actual concentration. The intelligent replenishment control unit uses this value as a target threshold to construct a trend prediction model. When the concentration is below the threshold, a dual constraint mechanism is activated to generate a valve opening command. This achieves the effects of accurate concentration monitoring of the concentrate, dynamic adaptation of the replenishment amount, and stable concentration maintenance within the critical threshold range for rust and foam prevention. It effectively solves the problem of unstable rust and foam prevention effects of hydraulic supports due to concentration monitoring distortion and improper replenishment control. Attached Figure Description

[0006] Figure 1 This is an overall block diagram of the present invention.

[0007] The meanings of the labels in the diagram are as follows: 1. Basic monitoring unit; 2. Core calibration unit; 3. Intelligent replenishment control unit. Detailed Implementation

[0008] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] This invention provides an intelligent online monitoring and replenishment system for hydraulic support concentrate. Please refer to [link / reference]. Figure 1 As shown, it includes: Basic monitoring unit 1 acquires flow velocity signals at multiple locations and raw concentration data at sampling points through sensors built into the circulation pipeline; The core calibration unit 2 establishes a stratified velocity weighting model based on the fluid dynamics characteristics of the pipeline and divides the pipeline cross-section into a high-velocity zone in the center and a low-velocity zone near the wall. The concentration weighting coefficients of the two regions are dynamically allocated according to the velocity signals at multiple locations. The weighting coefficient of the high-velocity zone increases with the measured velocity value, while the weighting coefficient of the low-velocity zone decreases with the increase of the risk of additive deposition. Finally, the raw concentration data of the sampling points are processed by a weighted fusion algorithm to output the estimated value of the actual concentration and eliminate the negative detection bias caused by velocity stratification. The intelligent replenishment control unit 3 uses the estimated actual concentration as the target threshold and constructs a trend prediction model based on the rate of concentration change. When the concentration is continuously lower than the target threshold, a dual constraint mechanism is activated. The first constraint generates the basic replenishment amount based on the magnitude and duration of the negative concentration deviation. The second constraint superimposes the concentration change gradient suppression function and the historical replenishment over-limit compensation factor to compress the basic replenishment amount, form the theoretical replenishment amount, and generate a valve opening command to ensure that the concentration of the concentrate is stably maintained within the critical threshold range for rust prevention and foam prevention.

[0010] The establishment of the stratified flow velocity weighting model in core calibration unit 2 further includes: Based on the inner diameter of the circulating pipe and the dynamic viscosity parameters of the concentrate, a flow regime discrimination module based on the Reynolds number is constructed. When the Reynolds number is lower than the critical turbulence threshold, the laminar flow compensation factor is automatically activated. This factor corrects the deposition risk coefficient in the low flow velocity region by quantifying the boundary layer thickness in the near-wall region. At the same time, empirical parameters of the settling rate of anti-wear additives are introduced, so that the weight allocation mechanism responds to both the fluid dynamics characteristics and the physical properties of the additives, thereby improving the compensation intensity of the weight coefficient in the high flow velocity region under low flow velocity conditions.

[0011] The flow regime discrimination module further defines the division of the pipe cross-section as follows: The high-velocity zone at the center is a concentric circle with the center of the pipe as the center and a radius equal to a set ratio of the pipe's inner diameter. The low-velocity zone near the wall is an annular zone extending inward from the inner wall of the pipe by a set distance. The boundary positions of the two zones are dynamically adjusted according to the real-time Reynolds number. In laminar flow, the near-wall zone is expanded and a deposition sensitivity coefficient is added. In turbulent flow, the near-wall zone is shrunk and a turbulent mixing gain coefficient is enabled to ensure that the zoning strategy adapts to changes in fluid motion state.

[0012] The concentration weighting coefficients for the two regions are dynamically assigned based on multi-location flow velocity signals, specifically including: Based on the maximum flow velocity value in the central area and the minimum flow velocity value near the wall area collected by multi-location flow velocity sensors, the flow velocity gradient ratio is calculated as the main variable for weight adjustment. When the flow velocity gradient ratio increases, the proportion of the weight coefficient of the high flow velocity area is linearly increased. At the same time, the empirical parameter of the additive sedimentation rate is input into the weight decay function to generate the weight coefficient of the low flow velocity area, which decreases nonlinearly with the increase of the deposition risk index. Finally, through the coupling effect of the linear increase dominated by the gradient ratio and the nonlinear decay dominated by the deposition risk, the weight coefficient is adjusted in a two-way coordinated manner.

[0013] The weight decay function, specifically includes the weight coefficient decay mechanism in the low-velocity region: A basic threshold for the deposition risk index is set. When the near-wall flow velocity is consistently lower than the critical deposition velocity for a period exceeding a set period, an exponential decay response is triggered. The decay rate is controlled by the sedimentation acceleration parameter matched to the additive type. At the same time, the weight coefficient enhancement mechanism for the high-velocity zone is associated with the velocity change rate in the central zone. When the velocity change rate is positive, an accelerated enhancement mode is activated, and when it is negative, a buffer enhancement mode is activated. The interference of velocity fluctuations on the weight allocation is offset through a two-way dynamic response.

[0014] The raw concentration data of the sampling points are processed using a weighted fusion algorithm, specifically including: The weight coefficients of high and low flow velocity zones are normalized to percentage weights. The original concentration data of the two zones within the same sampling period are filtered by time-dimensional moving average. Then, the filtered data are weighted and summed with normalized weights to output the estimated actual concentration value. The length of the sliding window is adaptively adjusted according to the flow velocity gradient ratio. The larger the gradient ratio, the shorter the window to enhance timeliness, and the smaller the gradient ratio, the longer the window to improve stability.

[0015] The construction of the trend prediction model in the intelligent replenishment control unit 3 further includes: The actual concentration estimate output from multiple consecutive calibration cycles is used as the input sequence. The concentration change rate is calculated by first-order difference. Then, the mass conservation equation is established by combining the real-time flow data of the concentrate in the pipeline. The concentration decay curve in the future set time period is derived. When the intersection time of the curve with the target threshold is less than the warning threshold, the dual constraint mechanism is activated in advance. The mass conservation equation introduces an additive consumption rate correction term, which is dynamically updated by the support action frequency signal.

[0016] The primary constraint generates the base replenishment amount based on the magnitude and duration of the negative concentration deviation, specifically including: The negative concentration deviation is divided into multiple deviation levels, each corresponding to a basic supplementary amount. A time accumulation coefficient is introduced based on the duration of the negative deviation. This coefficient increases in segments according to the different time intervals into which the duration falls. Finally, the basic supplementary amount is multiplied by the time accumulation coefficient to generate the initial supplementary amount. The deviation level threshold and the time interval boundary value are optimized in reverse based on the distribution of historical concentration exceedance events.

[0017] The secondary constraint superimposed with the concentration change gradient suppression function and the historical replenishment excess compensation factor compresses the basic replenishment amount, specifically including: The initial replenishment amount is compressed for the first time using a concentration change gradient suppression function. This function takes the concentration change rate output by the trend prediction model as input. When the rate is negative and the absolute value is greater than the suppression threshold, a compression ratio positively correlated with the absolute value of the rate is generated.

[0018] The theoretical replenishment amount is generated and the valve opening command is produced, specifically including: The historical over-limit compensation factor is applied to the first compression result. This factor counts the number of times the concentration exceeds the critical threshold for rust and foam prevention after recent additions. The amount of secondary compression is calculated according to the proportion of the number of over-limit additions. Finally, the result of the secondary compression is used as the theoretical amount of addition and converted into the duration of the valve opening command.

[0019] Further explanation is needed regarding the specific implementation methods for establishing the stratified flow velocity weighting model, dividing the pipeline cross-section, and dynamically allocating the concentration weighting coefficients in the core calibration unit 2. After the basic monitoring unit 1 acquires flow velocity signals from multiple locations and raw concentration data from sampling points through the built-in sensors in the circulating pipeline, the core calibration unit 2 needs to first establish a stratified flow velocity weighting model. This model is crucial for resolving concentration detection deviations caused by flow velocity stratification within the pipeline. Ignoring flow velocity stratification and directly using data from a single sampling point will underestimate the actual concentration. Therefore, it is necessary to combine fluid dynamics characteristics and additive properties to construct the model. The specific implementation method is as follows: Firstly, the establishment of the stratified velocity weighting model requires using the inner diameter of the circulating pipe and the dynamic viscosity of the concentrate as basic parameters, focusing on the synergy between flow regime discrimination and weight compensation. The inner diameter of the circulating pipe is the diameter of the pipe's cross-section, and the dynamic viscosity of the concentrate is a parameter reflecting the flow resistance of the concentrate. These two parameters directly affect the fluid flow state. Based on these two parameters and the average velocity from the multi-location velocity signals, the system calculates the Reynolds number. The Reynolds number is the core benchmark for determining whether a fluid is laminar or turbulent. A flow regime discrimination module based on the Reynolds number is then constructed. This module presets a critical turbulence threshold. When the calculated Reynolds number is below this threshold, the fluid is in a laminar state, with obvious velocity stratification and extremely slow velocity near the wall. The module automatically activates the laminar flow compensation factor. The core function is to correct the deposition risk coefficient in the low-velocity region. It quantifies the boundary layer thickness near the wall. In laminar flow, a boundary layer with a velocity approaching zero exists near the wall, and its thickness increases with viscosity. The module adjusts the deposition risk coefficient; a thicker boundary layer results in a higher risk, thus avoiding misjudgments of deposition risk due to boundary layer influence. Simultaneously, to consider the additive's own characteristics, the module introduces an empirical parameter for the anti-wear additive's settling rate. This parameter is a fixed value based on historical experimental data, used to characterize the additive's deposition trend at low flow rates. This ensures the weighting mechanism responds to both the pipe's fluid dynamics and the additive's properties. Ultimately, under low-velocity conditions (laminar flow), the synergistic effect of the laminar flow compensation factor and the settling rate parameter improves... The compensation strength of the weighting coefficient for the high-velocity region ensures that the true concentration data in the high-velocity region dominates the final calculation. After completing the construction of the flow regime discrimination module, it is necessary to further clarify the division rules of the pipe cross-section by the flow regime discrimination module. Only by accurately defining the velocity regions under different flow regimes can a clear spatial range be provided for subsequent weight allocation. The division adopts a dual-region structure of concentric circles and annular rings. The central high-velocity region is defined as a concentric circle region with the center of the pipe as the center and a radius equal to a set ratio of the pipe's inner diameter. This ratio is set according to the pipe's flow regime characteristics. In this region, the fluid is less affected by pipe wall friction, and the flow velocity is stable and relatively high. The near-wall low-velocity region is an annular region extending inward from the inner wall of the pipe by a set distance. This distance is also set based on initial empirical values, that is, from the inner wall of the pipe towards the inner wall. A 20mm annular region extends from the center. Within this region, the fluid experiences high frictional resistance from the pipe wall, resulting in low flow velocity and a high risk of additive deposition. Crucially, the boundary between these two regions dynamically adjusts based on the real-time Reynolds number. When the Reynolds number is below the critical turbulence threshold (in a laminar flow state), the boundary of the near-wall region expands towards the center, and a deposition sensitivity coefficient is applied. This is because in laminar flow, velocity stratification is more pronounced, the near-wall region is larger, and the deposition risk is higher, necessitating an expanded region to cover all low-velocity areas. Conversely, when the Reynolds number is above the critical turbulence threshold (in a turbulent flow state), the fluid distribution becomes more uniform due to turbulent mixing, and the near-wall region boundary contracts towards the pipe wall. Simultaneously, a turbulent mixing gain coefficient is activated because turbulence disrupts the additive deposition trend.The near-wall region is narrowed, and the concentration data is more reliable. This dynamic zoning strategy ensures that the region division can adapt to changes in fluid motion, providing a precise spatial basis for subsequent weight allocation. After clarifying the zoning rules of the pipe cross-section, the concentration weight coefficients of the two regions can be dynamically allocated based on the multi-location velocity signals. This process needs to consider both velocity gradient and deposition risk to achieve bidirectional coordinated adjustment of weights. First, the main variable for weight adjustment needs to be determined. From the signals collected by the multi-location velocity sensors, the maximum velocity value of the high-velocity zone in the center and the minimum velocity value of the low-velocity zone near the wall are extracted, and the ratio of the two is calculated as the velocity gradient ratio. The larger the velocity gradient ratio, the more significant the velocity difference between the center and the near wall, and the higher the risk of distortion in the near-wall concentration data. The weight of the high-velocity zone needs to be increased. The system sets a linear correlation between the velocity gradient ratio and the weight coefficient of the high-velocity zone. When the velocity gradient ratio increases, the proportion of the high-velocity zone weight coefficient increases linearly, ensuring that the reliable data of the high-velocity zone can be used to adjust the concentration weight. Concentration calculation requires the introduction of empirical parameters regarding additive settling rates, which are input into a weight decay function. This function is the core function for adjusting the weights of low-velocity regions. It calculates a deposition risk index based on the additive settling rate; the faster the settling rate, the higher the deposition risk index. It generates a low-velocity region weight coefficient that decreases non-linearly with the deposition risk index. This non-linear decay is because deposition risk is cumulative; when the risk exceeds a certain threshold, the reliability of low-velocity region data drops sharply, necessitating a rapid reduction in its weight. Ultimately, a coupling effect is achieved between the linear increase in high-velocity regions dominated by velocity gradient ratios and the non-linear decay in low-velocity regions dominated by deposition risk. When velocity differences are large and deposition risk is high, the weight of high-velocity regions increases significantly, while the weight of low-velocity regions decreases substantially. When velocity differences are small and deposition risk is low, the weights are more balanced, achieving bidirectional synergistic adjustment of the weight coefficients. This lays the foundation for subsequent weighted fusion algorithms to process the raw concentration data.

[0020] The specific implementation methods for the weighted decay function decay mechanism, weighted fusion algorithm data processing, and intelligent replenishment trend prediction model construction are as follows: After the core calibration unit 2 completes the initial coordinated adjustment of the concentration weight coefficients in the high and low flow rate zones based on the flow rate gradient ratio and deposition risk index, in order to further accurately address the concentration data distortion caused by additive deposition in the low flow rate zone, it is necessary to clarify the specific decay mechanism of the low flow rate zone weight coefficient in the weighted decay function. At the same time, the partitioned concentration data is transformed into true concentration estimates through a weighted fusion algorithm. After obtaining reliable actual concentration estimates, the intelligent replenishment control unit 3 needs to construct a trend prediction model to predict concentration changes and initiate the replenishment mechanism in advance. The specific implementation methods are as follows: Firstly, the attenuation mechanism of the weight coefficient in the low-velocity region within the weight attenuation function focuses on the dynamic response of additive deposition under low flow rates. Simultaneously, it offsets flow rate fluctuations through the coordinated adjustment of the weight in the high-velocity region. The system first sets a basic threshold for the deposition risk index. This threshold is a critical value based on statistical data from historical deposition events. For example, if statistics show that the near-wall flow rate is below 0.2 m / s for more than 30 seconds, the probability of additive deposition exceeds 80%. Therefore, "flow rate ≤ 0.2 m / s" is set as the critical deposition flow rate, and 30 seconds is set as the setting period. Both together constitute the basic threshold for the deposition risk index, used to trigger... The criterion for triggering the attenuation response is as follows: when the near-wall velocity sensor in the basic monitoring unit 1 detects a flow velocity consistently below 0.2 m / s for more than 30 seconds, the system determines that the risk of additive deposition has significantly increased and immediately triggers an exponential attenuation response of the weighting coefficient in the low-velocity zone. This exponential attenuation means that the weighting coefficient decreases rapidly with the duration of deposition, rather than linearly, avoiding data distortion caused by the weight not being adjusted in time after deposition intensifies. Furthermore, the attenuation rate is controlled by the settling acceleration parameter matched to the additive type; for example, for anti-wear additives, the empirical settling acceleration parameter is 0.1 mm / s². 2 This was obtained through laboratory simulations of sedimentation experiments with different additives under laminar flow conditions. The greater the sedimentation acceleration, the faster the decay rate. For example, an acceleration of 0.1 mm / s²... 2 At that time, it takes 20 seconds for the weighting coefficient to decrease from 0.4 to 0.1, with an acceleration of 0.2 mm / s². 2In just 10 seconds, the weighting mechanism for high-velocity zones is linked to the velocity change rate in the central zone. The velocity change rate is the ratio of the velocity difference between two consecutive sampling periods in the central zone to the time interval. For example, if the velocity in the previous period is 1.5 m / s and in the next period is 1.7 m / s, the change rate is positive; if the next period is 1.3 m / s, the change rate is negative. When the change rate is positive, the accelerated boosting mode is activated, increasing the weighting coefficient more rapidly and quickly amplifying the impact of reliable data from high-velocity zones. When the change rate is negative, the buffer boosting mode is activated, slowing down the weighting coefficient increase to avoid excessive weight adjustment due to a temporary drop in the central velocity. By employing a two-way response of exponential attenuation in the low-velocity zone and dynamic enhancement in the high-velocity zone, the system can accurately address additive deposition while offsetting the interference of velocity fluctuations on weight allocation. This ensures that the weight coefficients always match the actual flow and deposition states. After dynamically adjusting the weight coefficients in the low-velocity zone, a weighted fusion algorithm is used to process the raw concentration data from the sampling points. This integrates the discrete concentration data from the high and low velocity zones into an estimated actual concentration reflecting the overall state of the concentrate within the pipeline. This process is a crucial final step in eliminating velocity stratification detection bias. The system normalizes the previously calculated weight coefficients for the high and low velocity zones into percentage weights. For example, the weighting coefficient for the high-velocity zone is 0.8 and for the low-velocity zone is 0.2. After normalization, these correspond to 80% and 20% respectively, ensuring that the total weighting percentage of the two categories is 100%. This avoids data redundancy or missing data during weighting. The original concentration data for both regions within the same sampling period are subjected to time-dimensional moving average filtering. The sampling period is a fixed data acquisition interval for basic monitoring unit 1, such as 10 seconds / cycle. Five original concentration data points are collected for each of the high and low velocity zones within each cycle. Moving average filtering smooths out instantaneous fluctuations in the original data by averaging multiple consecutive data points, preventing single-point abnormal data from affecting the final result. The key lies in the moving average. The sliding window length is adaptively adjusted based on the velocity gradient ratio. The sliding window length is the number of data points involved in the averaging calculation. When the velocity gradient ratio is large, the window length is set to a short window to enhance data timeliness. When the velocity gradient ratio is small (e.g., 2, with uniform velocity distribution), the window length is set to a long window to improve data stability. Finally, the filtered data is weighted and summed using normalized percentage weights. For example, if the filtered concentration in the high-velocity region is 15.2% (weight 80%) and the filtered concentration in the low-velocity region is 13.5% (weight 20%), then the estimated actual concentration = 15.2% × 80% + 13.5% × 20% = 14.86%, this value fully integrates the effective concentration information of the two regions, eliminating the negative detection bias caused by flow velocity stratification. After the core calibration unit 2 continuously outputs accurate actual concentration estimates, the intelligent replenishment control unit 3 needs to build a trend prediction model based on this value. By predicting the concentration decay trend, it can start replenishment in advance to avoid the concentration falling below the critical threshold for rust prevention and foam prevention. The specific construction process needs to combine the concentration change law and the fluid substance conservation logic. The model first uses the actual concentration estimates output from multiple consecutive calibration cycles as the input sequence. The calibration cycle is consistent with the weight calculation cycle of the core calibration unit 2 to ensure that the input data can reflect the continuous change trend of concentration. The concentration change rate is calculated through first-order difference. The first-order difference is the ratio of the difference between the actual concentration estimates of two adjacent calibration cycles to the cycle length. This rate directly reflects the speed of concentration decrease or increase. Combined with the real-time flow data of the concentrate in the pipeline, a model is built. A mass conservation equation is established, the core logic of which is that the concentration change of the concentrate in the pipeline = concentration change due to additive consumption + concentration change due to replenishment. The equation is used to derive the concentration decay curve over a future set time period. To improve the accuracy of the derivation, the mass conservation equation introduces an additive consumption rate correction term. This correction term is dynamically updated by the support operation frequency signal. The support operation frequency is the number of extension and retraction movements of the hydraulic support; the higher the operation frequency, the faster the additive is consumed. This ensures that the equation closely reflects the additive consumption pattern under actual working conditions. Finally, the system determines the intersection time between the concentration decay curve and the target threshold. If the intersection time is 4 minutes, and the preset warning threshold is 5 minutes, a 1-minute advance warning indicates that the concentration will fall below the threshold in 4 minutes. This immediately activates the dual constraint mechanism, allowing time for accurate calculation of the replenishment amount and avoiding concentration fluctuations caused by passive replenishment.

[0021] The specific implementation method for generating the basic replenishment amount under the first-level constraint, compressing the replenishment amount under the second-level constraint, and generating valve opening commands in the intelligent replenishment control unit 3 is as follows: After deriving the concentration decay curve from the trend prediction model of the intelligent replenishment control unit 3 and determining that the dual constraint mechanism needs to be activated in advance, the replenishment amount needs to be calculated step by step according to the logic of determining the basic amount under the first-level constraint and controlling the accuracy under the second-level constraint. Finally, it is converted into valve operation commands. The first-level constraint focuses on the core influencing factors of the negative concentration deviation to generate the initial replenishment amount, while the second-level constraint optimizes the replenishment amount through dynamic suppression and historical compensation to avoid insufficient or excessive replenishment. The specific implementation method is as follows: The first step involves a primary constraint that generates a base replenishment amount based on the magnitude and duration of the negative concentration deviation. This constraint is the core foundation for replenishment calculation and must consider both the severity and sustained impact of the deviation to avoid replenishment imbalances caused by a single-dimensional judgment. The magnitude of the negative concentration deviation refers to the difference between the current estimated actual concentration and the critical threshold for rust and foam prevention. The larger the difference, the further the concentration deviates from the safe range. The system will divide this negative deviation magnitude into multiple deviation levels, each corresponding to a fixed base replenishment amount. For example, a slight deviation (negative deviation of 0.1% to 0.3%) corresponds to a base replenishment amount of... The base values ​​are 5L (each liter of concentrate can increase the concentration of a certain volume of circulating liquid), 8L for moderate deviation (negative deviation 0.3% to 0.5%), and 12L for severe deviation (negative deviation greater than 0.5%). The classification is based on the linear correlation experiment between concentrate concentration and replenishment amount. However, relying solely on the deviation level is insufficient to reflect the cumulative effect of long-term deviations. Therefore, a time accumulation coefficient is introduced based on the duration of the negative deviation. The duration of the negative deviation is the time from when the concentration first falls below the critical threshold to the current moment. The system divides this duration into different time intervals, and the coefficient is applied according to these intervals. Segmented increments: For example, a deviation lasting 1-3 minutes corresponds to a coefficient of 1.0 (no additional accumulation), 3-5 minutes corresponds to a coefficient of 1.2 (requiring an additional 20% replenishment), 5-8 minutes corresponds to a coefficient of 1.5 (requiring an additional 50% replenishment), and more than 8 minutes corresponds to a coefficient of 1.8 (requiring an additional 80% replenishment). This segmented increment logic is because the longer the deviation persists, the more thoroughly the additive in the circulating fluid is consumed, requiring a larger replenishment amount to restore the concentration. Ultimately, the initial replenishment amount is generated by multiplying the base replenishment amount by the time accumulation coefficient. For example, a moderate deviation (base 8L) lasting 4 minutes (coefficient 1.2). The initial replenishment amount is 8L × 1.2 = 9.6L. The threshold values ​​for deviation levels (e.g., 0.3%, 0.5%) and the boundary values ​​for time intervals (e.g., 3 minutes, 5 minutes) are not fixed values, but are optimized in reverse based on the distribution of historical concentration exceedance events. For example, it was found that 60% of the past minor deviation exceedance events were due to the original minor deviation threshold of 0.2% being set too loosely, resulting in untimely replenishment. Therefore, the minor deviation threshold was lowered to 0.1%. If it was found that 20% of the concentrations were still not up to standard after replenishment for deviation events within 3 minutes, the coefficient corresponding to 3 minutes was increased from 1.0 to 1.1. Ensure parameter settings closely match actual operating conditions. After generating the initial replenishment amount under the first-level constraints, the concentration change gradient suppression function under the second-level constraints needs to be used for the first compression. The core purpose of this compression is to adapt to the dynamic changes in the concentration decrease rate and avoid over-replenishment due to the initial replenishment amount not considering the slowdown in concentration decrease. The concentration change gradient suppression function is an adjustment function specifically designed for the concentration decrease rate. Its input is the concentration change rate output by the trend prediction model. The negative sign represents a concentration decrease, and the absolute value represents the rate of decrease. The system will first preset a suppression threshold. When the concentration change rate is negative and the absolute value is greater than the suppression threshold, it indicates that the concentration... If the concentration is still decreasing rapidly, a smaller compression ratio is generated to ensure that the replenishment amount can keep up with the rate of decrease. If the rate of change of concentration is negative but the absolute value is less than the inhibition threshold, it indicates that the concentration decrease has slowed down or may even be about to stabilize. In this case, a larger compression ratio is generated that is positively correlated with the absolute value of the rate. The smaller the absolute value of the rate, the larger the compression ratio. For example, when the rate of change is -0.005% / second, the compression ratio is increased to 20%. Through this dynamic compression logic, the replenishment amount can accurately match the real-time concentration decrease trend, avoiding the waste of replenishing too much when the decrease is slow. For example, if the initial replenishment amount is 9.6L and the concentration change rate is -0.008% / second (small absolute value), the compression ratio can be increased to 20%. (At the suppression threshold), with a compression ratio of 15%, the replenishment amount after the first compression is 9.6L × (1-15%) = 8.16L. After the first compression, a historical replenishment over-limit compensation factor needs to be introduced for a second compression to finally form the theoretical replenishment amount and convert it into a valve opening command. This step is to avoid the inertia problem of historical replenishment over-limit and ensure that the current replenishment amount will not repeat the same mistake. The historical replenishment over-limit compensation factor is an adjustment coefficient based on the number of times the concentration after replenishment in the recent period (e.g., the past 24 hours) has exceeded the critical threshold for rust prevention and foam prevention. If there are no recent over-limit records (0 times), the factor is set to 1.0 (no secondary compression). For 1-2 instances of exceeding the limit, the factor is set to 0.9 (10% for secondary compression). For 3 or more instances of exceeding the limit, the factor is set to 0.8 (20% for secondary compression). The more instances of exceeding the limit, the greater the compression ratio, thus correcting any potential tendency for excessive replenishment. For example, if the replenishment amount after the first compression is 8.16L, and there is one recent instance of exceeding the limit (factor 0.9), the replenishment amount after the second compression will be 8.16L × 0.9 = 7.344L. This value is the final theoretical replenishment amount. The theoretical replenishment amount needs to be converted into the valve opening command duration. The system will first preset a fixed valve opening for replenishment, and then calculate the required duration based on the theoretical replenishment amount. The final valve opening command is sent to the supplemental valve controller. The controller will accurately execute the opening and duration operations to ensure that the amount of concentrated liquid injected is consistent with the theoretical supplementation amount, thereby stabilizing the concentration of the circulating liquid within the critical threshold range for rust prevention and foam prevention.

[0022] In this invention, the basic monitoring unit 1 collects flow velocity signals and raw concentration data at multiple locations through sensors built into the circulating pipeline. The core calibration unit 2 constructs a layered flow velocity weight model based on the fluid dynamics characteristics of the pipeline, divides the central high-velocity zone into a near-wall low-velocity zone, dynamically allocates the concentration weight coefficients of the two zones, processes the data through a weighted fusion algorithm, and outputs an estimated actual concentration value to eliminate the negative detection bias caused by flow velocity stratification. The intelligent replenishment control unit 3 constructs a trend prediction model with this value as the target threshold. When the concentration is continuously lower than the threshold, a dual constraint mechanism is activated to generate a valve opening command to ensure that the concentration of the concentrate is stably maintained within the critical threshold range for rust prevention and foam prevention, thus ensuring the stable operation of the hydraulic support.

[0023] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart online monitoring and replenishment system for hydraulic support concentrate, characterized in that, include: The basic monitoring unit (1) acquires multi-location flow velocity signals and raw concentration data at sampling points through sensors built into the circulating pipeline; The core calibration unit (2) establishes a layered flow velocity weight model based on the fluid dynamics characteristics of the pipeline and divides the pipeline cross section into a high-velocity zone in the center and a low-velocity zone near the wall. The concentration weight coefficients of the two regions are dynamically allocated according to the flow velocity signals at multiple locations. The weight coefficient of the high-velocity zone increases with the measured flow velocity value, while the weight coefficient of the low-velocity zone decreases with the increase of the additive deposition risk. Finally, the original concentration data of the sampling points are processed by a weighted fusion algorithm to output the estimated value of the actual concentration and eliminate the negative detection bias caused by the flow velocity layering. The intelligent replenishment control unit (3) uses the actual concentration estimate as the target threshold and constructs a trend prediction model based on the concentration change rate. When the concentration is continuously lower than the target threshold, a dual constraint mechanism is activated. The first constraint generates the basic replenishment amount based on the negative deviation amplitude and duration of the concentration. The second constraint superimposes the concentration change gradient suppression function and the historical replenishment over-limit compensation factor to compress the basic replenishment amount, form the theoretical replenishment amount, and generate the valve opening command to ensure that the concentration of the concentrate is stably maintained within the critical threshold range of rust prevention and foam prevention.

2. The intelligent online monitoring and replenishment system for concentrated hydraulic support fluid according to claim 1, characterized in that: The establishment of the hierarchical flow velocity weight model in the core calibration unit (2) further includes: Based on the inner diameter of the circulating pipe and the dynamic viscosity parameters of the concentrate, a flow regime discrimination module based on the Reynolds number is constructed. When the Reynolds number is lower than the critical turbulence threshold, the laminar flow compensation factor is automatically activated. This factor corrects the deposition risk coefficient in the low flow velocity region by quantifying the boundary layer thickness in the near-wall region. At the same time, empirical parameters of the settling rate of anti-wear additives are introduced, so that the weight allocation mechanism responds to both the fluid dynamics characteristics and the physical properties of the additives, thereby improving the compensation intensity of the weight coefficient in the high flow velocity region under low flow velocity conditions.

3. The intelligent online monitoring and replenishment system for concentrated hydraulic support fluid according to claim 2, characterized in that: The flow regime discrimination module further defines the division of the pipe cross-section as follows: The high-velocity zone at the center is a concentric circle with the center of the pipe as the center and a radius equal to a set ratio of the pipe's inner diameter. The low-velocity zone near the wall is an annular zone extending inward from the inner wall of the pipe by a set distance. The boundary positions of the two zones are dynamically adjusted according to the real-time Reynolds number. In laminar flow, the near-wall zone is expanded and a deposition sensitivity coefficient is added. In turbulent flow, the near-wall zone is shrunk and a turbulent mixing gain coefficient is enabled to ensure that the zoning strategy adapts to changes in fluid motion state.

4. The intelligent online monitoring and replenishment system for hydraulic support concentrate according to claim 3, characterized in that: The dynamic allocation of concentration weighting coefficients between the two regions based on multi-location flow velocity signals specifically includes: Based on the maximum flow velocity value in the central area and the minimum flow velocity value near the wall area collected by multi-location flow velocity sensors, the flow velocity gradient ratio is calculated as the main variable for weight adjustment. When the flow velocity gradient ratio increases, the proportion of the weight coefficient of the high flow velocity area is linearly increased. At the same time, the empirical parameter of the additive sedimentation rate is input into the weight decay function to generate the weight coefficient of the low flow velocity area, which decreases nonlinearly with the increase of the deposition risk index. Finally, through the coupling effect of the linear increase dominated by the gradient ratio and the nonlinear decay dominated by the deposition risk, the weight coefficient is adjusted in a two-way coordinated manner.

5. The intelligent replenishment system for online monitoring of the concentrated fluid status of a hydraulic support according to claim 4, characterized in that: The weight decay function, specifically includes the following weight coefficient decay mechanism in the low-velocity region: A basic threshold for the deposition risk index is set. When the near-wall flow velocity is consistently lower than the critical deposition velocity for a period exceeding a set period, an exponential decay response is triggered. The decay rate is controlled by the sedimentation acceleration parameter matched to the additive type. At the same time, the weight coefficient enhancement mechanism for the high-velocity zone is associated with the velocity change rate in the central zone. When the velocity change rate is positive, an accelerated enhancement mode is activated, and when it is negative, a buffer enhancement mode is activated. The interference of velocity fluctuations on the weight allocation is offset through a two-way dynamic response.

6. The intelligent replenishment system for online monitoring of the concentrated fluid status of a hydraulic support according to claim 5, characterized in that: The raw concentration data of the sampling points are processed using a weighted fusion algorithm, specifically including: The weight coefficients of high and low flow velocity zones are normalized to percentage weights. The original concentration data of the two zones within the same sampling period are filtered by time-dimensional moving average. Then, the filtered data are weighted and summed with normalized weights to output the estimated actual concentration value. The length of the sliding window is adaptively adjusted according to the flow velocity gradient ratio. The larger the gradient ratio, the shorter the window to enhance timeliness, and the smaller the gradient ratio, the longer the window to improve stability.

7. The intelligent replenishment system for online monitoring of the concentrated fluid status of a hydraulic support according to claim 1, characterized in that: The construction of the trend prediction model in the intelligent replenishment control unit (3) further includes: The actual concentration estimate output from multiple consecutive calibration cycles is used as the input sequence. The concentration change rate is calculated by first-order difference. Then, the mass conservation equation is established by combining the real-time flow data of the concentrate in the pipeline. The concentration decay curve in the future set time period is derived. When the intersection time of the curve with the target threshold is less than the warning threshold, the dual constraint mechanism is activated in advance. The mass conservation equation introduces an additive consumption rate correction term, which is dynamically updated by the support action frequency signal.

8. The intelligent replenishment system for online monitoring of the concentrated fluid status of a hydraulic support according to claim 7, characterized in that: The primary constraint generates a base replenishment amount based on the magnitude and duration of the negative concentration deviation, specifically including: The negative concentration deviation is divided into multiple deviation levels, each corresponding to a basic supplementary amount. A time accumulation coefficient is introduced based on the duration of the negative deviation. This coefficient increases in segments according to the different time intervals into which the duration falls. Finally, the basic supplementary amount is multiplied by the time accumulation coefficient to generate the initial supplementary amount. The deviation level threshold and the time interval boundary value are optimized in reverse based on the distribution of historical concentration exceedance events.

9. The intelligent replenishment system for online monitoring of the concentrated fluid status of a hydraulic support according to claim 8, characterized in that: The secondary constraint superimposed with the concentration change gradient suppression function and the historical supplementary over-limit compensation factor compresses the basic supplementary amount, specifically including: The initial replenishment amount is compressed for the first time using a concentration change gradient suppression function. This function takes the concentration change rate output by the trend prediction model as input. When the rate is negative and the absolute value is greater than the suppression threshold, a compression ratio positively correlated with the absolute value of the rate is generated.

10. The intelligent online monitoring and replenishment system for concentrated hydraulic support fluid according to claim 9, characterized in that, The theoretical replenishment amount is generated and the valve opening command is produced, specifically including: The historical over-limit compensation factor is applied to the first compression result. This factor counts the number of times the concentration exceeds the critical threshold for rust and foam prevention after recent additions. The amount of secondary compression is calculated according to the proportion of the number of over-limit additions. Finally, the result of the secondary compression is used as the theoretical amount of addition and converted into the duration of the valve opening command.

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