A method and device for detecting slurry concentration, storage medium, and computer equipment

Abstract

The present application discloses a method and device for detecting slurry concentration, a storage medium, and a computer device. The method includes: obtaining a historical pressure difference signal, a historical pressure signal, and a historical slurry concentration test value corresponding to a historical sampling time; determining a pressure difference compensation coefficient, a density compensation amount, a pressure compensation coefficient, and a concentration compensation amount based on the historical pressure difference signal, the historical pressure signal, and the historical slurry concentration test value; obtaining a slurry concentration detection model based on the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount, and an initial concentration detection model; obtaining a slurry concentration detection value based on the collected target pressure difference original signal, the target pressure original signal, and the slurry concentration detection model. The present application can detect slurry concentration quickly and accurately in real time while considering multiple influencing factors.

Description

Technical Field

[0001] The present application relates to the field of industrial process technology, and in particular to a method and device for detecting slurry concentration, a storage medium, and a computer device. Background Art

[0002] In mineral processing, pulp concentration is a key process parameter in grinding and classification, magnetic separation and concentration and filtration. Its detection accuracy will have a great impact on the classification efficiency of the hydrocyclone, the particle size distribution of the hydrocyclone overflow, the recovery efficiency of the magnetic separator and the concentrate grade, the concentration and filtration effect, etc. If the concentration detection accuracy does not meet the process requirements, it often causes the concentrate grade, metal recovery rate, auxiliary agent dosage and equipment energy consumption to decline.

[0003] In the prior art, when determining the concentration of ore pulp, a differential pressure concentration meter is usually used to determine it. The differential pressure concentration meter has become the best choice for online detection of ore pulp concentration due to its advantages such as no radiation hazard, simple installation, stable measurement results, and high cost performance. Under non-flowing and steady-state conditions, the ore pulp concentration is directly related to the pressure difference. Therefore, when the ore pulp is in a static and steady state, the differential pressure concentration meter has a better concentration detection effect. However, the actual mineral processing production process is often disturbed by internal and external dynamic uncertainties, and the ore pulp in the pipeline is always in a dynamic flow state. Differential pressure concentration meters and other types of concentration detection instruments are generally based on a single factor, without considering that the ore pulp concentration is related to multiple variables and is always in a dynamic change process. Its dynamic characteristics have a comprehensive complexity of strong nonlinearity and strong coupling, which makes it difficult for differential pressure concentration meters and the like to be used for a long time in a mining environment with many process change factors, relatively harsh working environment and conditions, and the accuracy of determining the ore pulp concentration by differential pressure concentration meters and the like in actual situations is not high.

[0004] Therefore, how to solve the problems of low detection accuracy and fixed instrument parameters of concentration detection instruments such as differential pressure concentration meters in the prior art has become a technical problem that urgently needs to be solved. Summary of the invention

[0005] In view of this, the present application provides a method and device for detecting slurry concentration, a storage medium, and a computer device, which can detect slurry concentration in real time, quickly, and accurately while taking into account multiple influencing factors.

[0006] According to one aspect of the present application, a method for detecting slurry concentration is provided, comprising:

[0007] Obtain the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to the historical sampling time;

[0008] Determine a pressure difference compensation coefficient, a density compensation amount, a pressure compensation coefficient, and a concentration compensation amount based on the historical pressure difference signal, the historical pressure signal, and the historical slurry concentration test value;

[0009] Obtaining a slurry concentration detection model according to the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model;

[0010] Based on the collected target pressure difference original signal, target pressure original signal, and the slurry concentration detection model, a slurry concentration detection value is obtained.

[0011] According to another aspect of the present application, a slurry concentration detection device is provided, comprising:

[0012] An acquisition module is used to obtain historical differential pressure signals, historical pressure signals and historical slurry concentration test values ​​corresponding to historical sampling moments;

[0013] A value determination module, used to determine a pressure difference compensation coefficient, a density compensation amount, a pressure compensation coefficient and a concentration compensation amount based on the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value;

[0014] A model determination module, used for obtaining a slurry concentration detection model according to the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model;

[0015] The detection value determination module is used to obtain the slurry concentration detection value based on the collected target pressure difference original signal, the target pressure original signal, and the slurry concentration detection model.

[0016] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned slurry concentration detection method is implemented.

[0017] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned slurry concentration detection method when executing the program.

[0018] By means of the above technical scheme, the present application provides a method and device for detecting slurry concentration, a storage medium, and a computer device. By applying the technical scheme of this embodiment, first, the historical pressure difference signal, the historical pressure signal, and the historical slurry concentration test value corresponding to the historical sampling time can be obtained. Further, the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, and the concentration compensation amount in the initial concentration detection model can be determined based on the historical pressure difference signal, the historical pressure signal, and the historical slurry concentration test value corresponding to each historical sampling time. After obtaining the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, and the concentration compensation amount, the slurry concentration detection model can be obtained based on the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount, and the initial concentration detection model. After obtaining the slurry concentration detection model, the slurry concentration detection value can be obtained by calculating the slurry concentration detection model according to the target pressure difference original signal and the target pressure original signal collected by the sensor. The embodiment of the present application determines the slurry concentration detection model, and then calculates the slurry concentration detection value based on the target pressure difference original signal and the target pressure original signal collected by the sensor. The slurry concentration can be detected quickly and accurately in real time while considering multiple influencing factors.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 A schematic diagram of a flow chart of a method for detecting slurry concentration provided in an embodiment of the present application is shown;

[0022] Figure 2 A schematic diagram of a flow chart of another method for detecting slurry concentration provided in an embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram showing the detection effect of a slurry concentration detection method provided in an embodiment of the present application is shown;

[0024] Figure 4 A schematic diagram of the structure of a slurry concentration detection device provided in an embodiment of the present application is shown;

[0025] Figure 5An industrial application hardware architecture diagram of a slurry concentration detection method provided in an embodiment of the present application is shown;

[0026] Figure 6 An industrial application software architecture diagram of a slurry concentration detection method provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0028] In this embodiment, a method for detecting slurry concentration is provided. Figure 1 As shown, the method includes:

[0029] Step 101, obtaining a historical pressure difference signal, a historical pressure signal and a historical slurry concentration test value corresponding to a historical sampling time;

[0030] The slurry concentration detection method provided in the embodiment of the present application can be applied to the scenario of the concentration control of the magnetic separation column feeding production in the ore dressing plant, and the slurry concentration can be determined in real time and accurately. First, the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value corresponding to the historical sampling time can be obtained, that is, each group of historical pressure difference signals, historical pressure signals and historical slurry concentration test values ​​correspond to a historical sampling time. Here, the historical pressure difference signal and the historical pressure signal can be signals directly collected by the pressure difference sensor and the pressure sensor installed in the slurry, or they can be processed signals. The slurry historical concentration test value can be obtained by the staff sampling the slurry at the historical sampling time and testing the slurry sample. Compared with the detection by the concentration detection instrument, the manual test has higher accuracy, but due to the long lag time of the manual test, it is unreasonable to adjust the current slurry concentration by the slurry concentration obtained by the test. Therefore, the embodiment of the present application helps to determine the slurry concentration detection model through the slurry historical concentration test value.

[0031] Step 102, determining a pressure difference compensation coefficient, a density compensation amount, a pressure compensation coefficient and a concentration compensation amount based on the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value;

[0032] In this embodiment, further, the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount can be determined based on the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to each historical sampling moment. Here, the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount are all related model parameters of the initial concentration detection model.

[0033] Step 103, obtaining a slurry concentration detection model according to the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model;

[0034] In this embodiment, after the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount are obtained, the slurry concentration detection model can be obtained according to the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient, concentration compensation amount and initial concentration detection model. That is, the slurry concentration detection model can be obtained through deformation operation, etc. using the obtained pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient, concentration compensation amount and initial concentration detection model.

[0035] Step 104, obtaining a slurry concentration detection value based on the collected target pressure difference original signal, target pressure original signal, and the slurry concentration detection model.

[0036] In this embodiment, after the slurry concentration detection model is obtained, the slurry concentration detection value can be obtained through calculation of the slurry concentration detection model according to the target pressure difference original signal and the target pressure original signal collected by the sensor.

[0037] By applying the technical solution of this embodiment, first, the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to the historical sampling time can be obtained. Further, the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount in the initial concentration detection model can be determined based on the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to each historical sampling time. After obtaining the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount, the slurry concentration detection model can be obtained based on the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient, concentration compensation amount and the initial concentration detection model. After obtaining the slurry concentration detection model, the slurry concentration detection value can be obtained by calculating the slurry concentration detection model according to the target pressure difference original signal and target pressure original signal collected by the sensor. The embodiment of the present application determines the slurry concentration detection model, and then calculates the slurry concentration detection value according to the target pressure difference original signal and target pressure original signal collected by the sensor, and can detect the slurry concentration in real time, quickly and accurately while considering multiple influencing factors.

[0038] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another slurry concentration detection method is provided, which includes:

[0039] Step 201, using the Laida criterion to perform signal filtering processing on the historical pressure difference original signal and the historical pressure original signal collected by the sensor, and performing first-order inertial filtering processing on the filtered historical pressure difference original signal and the historical pressure original signal to obtain a historical pressure difference signal and a historical pressure signal; determining the historical sampling time, and determining the historical concentration test value of the slurry, the historical pressure difference signal and the historical pressure signal corresponding to the historical sampling time, and obtaining the historical pressure difference signal, the historical pressure signal and the historical concentration test value of the slurry corresponding to each historical sampling time;

[0040] In this embodiment, the historical pressure difference original signal and the historical pressure original signal can be directly collected by the pressure difference sensor and the pressure sensor, and can be stored in the database after collection. Here, the historical pressure difference original signal can be ΔP(k), and the historical pressure original signal can be P(k), where k represents the historical sampling time. First, the historical pressure difference original signal and the historical pressure original signal stored in the database can be subjected to signal filtering processing using the Laida criterion. Specifically, the historical pressure difference original signal and the historical pressure original signal can be arranged in chronological order, and the outliers can be removed in two sliding time windows respectively using the Laida criterion. Here, the Bessel formula can be used to calculate the standard error σ in the Laida criterion (3σ): Among them, x i is the original signal, which can be either the original signal of historical pressure difference or the original signal of historical pressure. n is the sample size, which can be the sample size corresponding to the width of each sliding time window. is the mean of n groups of samples. Secondly, the original historical pressure difference signal and the original historical pressure signal after signal filtering can also be subjected to first-order inertial filtering. Here, the first-order inertial filtering function can be X(n)=αx(n)+(1-α)x(n-1), where α is the filtering coefficient, which can be 0.9, x(n) is the current sampling value, x(n-1) is the last filtering output value, and X(n) is the current filtering output value. After the Laida criterion and first-order inertial filtering, data outliers can be eliminated and the influence of data noise can be reduced, so that the subsequent slurry concentration detection value is closer to the true value.

[0041] Next, the historical concentration test values ​​of the slurry, the historical pressure difference signal, and the historical pressure signal can also be time-series benchmarked, that is, for each historical sampling moment, the historical pressure difference signal, historical pressure signal, and historical concentration test value of the slurry corresponding to the historical sampling moment can be found. Here, since the sampling time interval of the manual test is much longer than the sampling time interval of the sensor, the time when the manual sampling from the slurry is taken as the historical sampling moment, and the historical pressure difference signal and historical pressure signal corresponding to the historical sampling moment can be found and marked from the historical pressure difference signal and historical pressure signal, and then the historical pressure difference signal, historical pressure signal, and historical concentration test value of the slurry corresponding to each historical sampling moment can be obtained.

[0042] Step 202, obtaining a historical pressure difference signal, a historical pressure signal and a historical slurry concentration test value corresponding to a historical sampling time;

[0043] In this embodiment, the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to the historical sampling time can be obtained, that is, each group of historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponds to a historical sampling time.

[0044] Step 203, determining the pressure difference signal input order and the pressure signal input order; sequentially taking the historical pressure difference signals corresponding to each of the historical sampling moments as target historical pressure difference signals, determining a first target number of matching pressure difference signals based on each of the target historical pressure difference signals, and determining a pressure difference signal average value based on each of the target historical pressure difference signals and the corresponding matching pressure difference signals, taking the pressure difference signal average value as a processed historical pressure difference signal corresponding to the target historical pressure difference signal, wherein the first target number is obtained based on the pressure difference signal input order;

[0045] In this embodiment, the pressure difference signal input order and the pressure signal input order can be determined first, and then the historical pressure difference signal and the historical pressure signal can be processed according to the pressure difference signal input order and the pressure signal input order. Specifically, the historical pressure difference signals corresponding to each historical sampling moment can be taken as the target historical pressure difference signal in turn according to the time sequence or other order, and then, based on each target historical pressure difference signal, the first target number of matching pressure difference signals can be determined from the historical pressure difference signal. The matching pressure difference signal can be the historical pressure difference signal corresponding to each historical sampling moment of the first target number from the historical sampling moment corresponding to the target historical pressure difference signal. After determining the matching pressure difference signal corresponding to the target historical pressure difference signal, the average value of the pressure difference signal can be determined based on the target historical pressure difference signal and the corresponding matching pressure difference signal, and the average value of the pressure difference signal can be used as the processed historical pressure difference signal corresponding to the target historical pressure difference signal. Here, the first target number can be equal to the pressure difference signal input order, for example, if the pressure difference signal input order is 5, then the first target number can also be 5; or it can be obtained based on the mapping relationship with the pressure difference signal input order. Specifically, ΔP′(k) can represent the processed historical pressure difference signal, which can be expressed by the formula: Processing is performed, wherein n2 is a first target number determined based on the input order of the pressure difference signal.

[0046] Step 204, sequentially taking the historical pressure signals corresponding to the respective historical sampling moments as target historical pressure signals, determining a second target number of matching pressure signals based on each target historical pressure signal, and determining a pressure signal average value based on each target historical pressure signal and the corresponding matching pressure signal, taking the pressure signal average value as a processed historical pressure signal corresponding to the target historical pressure signal, and the second target number is obtained based on the pressure signal input order;

[0047] In this embodiment, the historical pressure signals corresponding to each historical sampling moment can be used as the target historical pressure signal in turn in chronological order or other order. Then, based on each target historical pressure signal, a second target number of matching pressure signals can be determined from the historical pressure signal. The matching pressure signal can be the historical pressure signal corresponding to each historical sampling moment of the second target number from the historical sampling moment corresponding to the target historical pressure signal. After determining the matching pressure signal corresponding to the target historical pressure signal, the average value of the pressure signal can be determined based on the target historical pressure signal and the corresponding matching pressure signal, and the average value of the pressure signal can be used as the processed historical pressure signal corresponding to the target historical pressure signal. Here, the second target number can be equal to the input order of the pressure signal. For example, if the input order of the pressure signal is 5, then the second target number can also be 5; or it can be obtained based on a mapping relationship with the input order of the pressure signal. Specifically, P′(k) can represent the processed historical pressure signal, which can be obtained by the formula Processing is performed, wherein n1 is a second target number determined based on the pressure signal input order. The embodiment of the present application processes the historical pressure difference signal and the historical pressure signal according to the pressure difference signal input order and the pressure signal input order, which can make the signal smoother and help to eliminate abnormal values.

[0048] Step 205, constructing a first target parameter estimation equation based on the initial density detection model;

[0049] In this embodiment, the first target parameter estimation equation corresponding to the initial density detection model can be constructed based on the initial density detection model. Here, the first target parameter estimation equation can include unknown numbers corresponding to the pressure difference compensation coefficient and the density compensation amount.

[0050] In the embodiment of the present application, the initial density detection model can be Among them, ΔP′(k) is the processed historical differential pressure signal, g is the gravity acceleration g=9.8N / kg, △h is the installation height of the differential pressure transmitter, B is the differential pressure compensation coefficient, and B1 is the density compensation amount.

[0051] In addition, the parameters of the slurry density detection model can be identified based on any system identification method. Specifically, the least squares system identification method can be selected. First, the initial density detection model is based on the minimization of the mean square error to estimate the parameters, that is, the corresponding first target parameter estimation equation can be in, ρ i The historical density test value of the slurry can be calculated by the formula: c α(i) is the historical concentration test value of the slurry, δ0 is the true density of the original ore, and m is the number of historical concentration test values ​​of the slurry. Here, the number of historical concentration test values ​​of the slurry is equal to the number of historical pressure difference signals and historical pressure signals.

[0052] Step 206, deriving the first target parameter estimation equation according to the pressure difference compensation coefficient and the density compensation amount, respectively, to obtain a first derivative and a second derivative, and determining the pressure difference compensation coefficient and the density compensation amount corresponding to when the first derivative and the second derivative are zero based on the historical pressure difference signal and the historical slurry concentration test value;

[0053] In this embodiment, since the first target parameter estimation equation includes unknowns corresponding to the pressure difference compensation coefficient and the density compensation amount, the first target parameter estimation equation can be differentiated. Specifically, the first derivative corresponding to the pressure difference compensation coefficient and the density compensation amount can be derived respectively to obtain the first derivative corresponding to the pressure difference compensation coefficient and the second derivative corresponding to the density compensation amount.

[0054] Specifically, solve B and B1 so that Minimize Divide into B, B1 derivative, get the first derivative and the second derivative

[0055] After obtaining the first derivative corresponding to the pressure difference compensation coefficient and the second derivative corresponding to the density compensation amount, the pressure difference compensation coefficient and the density compensation amount corresponding to when the first derivative and the second derivative are zero can be obtained based on the historical pressure difference signal after the above processing and the historical slurry concentration test value.

[0056] Specifically, setting the above formula to zero, we can get the solutions of B and B1 respectively: as well as in, is the processed historical pressure difference signal ΔP i Then, the pressure difference compensation coefficient and density compensation amount can be obtained.

[0057] Step 207, substituting the pressure difference compensation coefficient and the density compensation amount into the initial density detection model to obtain a slurry density detection model;

[0058] In this embodiment, after the pressure difference compensation coefficient and the density compensation amount are obtained, the pressure difference compensation coefficient and the density compensation amount can be substituted into the initial density detection model, and then the slurry density detection model can be obtained. That is, the corresponding unknown quantities in the initial density detection model are replaced with the obtained pressure difference compensation coefficient and the density compensation amount, and the slurry density detection model can be obtained.

[0059] Step 208, constructing a second target parameter estimation equation based on the initial concentration detection model, wherein the initial concentration detection model is determined based on the slurry density detection model;

[0060] In this embodiment, the second target parameter estimation equation corresponding to the initial concentration detection model can be constructed based on the initial concentration detection model. Here, the second target parameter estimation equation can include unknowns corresponding to the pressure compensation coefficient and the concentration compensation amount. The second target parameter estimation equation can be constructed based on the slurry density detection model.

[0061] In the embodiment of the present application, the initial concentration detection model can be Among them, P′(k) is the processed historical pressure signal, K is the pressure compensation coefficient, and K1 is the concentration compensation amount.

[0062] In addition, the parameters of the slurry concentration detection model can be identified based on any system identification method. Specifically, the least squares system identification method can be selected. First, the initial concentration detection model is based on the minimization of the mean square error to estimate the parameters, that is, the corresponding second target parameter estimation equation can be in, c α (i) is the historical concentration of the slurry, and δ0 is the true density of the original ore.

[0063] Step 209, respectively deriving the second target parameter estimation equation according to the pressure compensation coefficient and the concentration compensation amount to obtain a third derivative and a fourth derivative, and determining the pressure compensation coefficient and the concentration compensation amount corresponding to when the third derivative and the fourth derivative are zero based on the historical pressure signal and the slurry density detection model;

[0064] In this embodiment, since the second target parameter estimation equation includes unknowns corresponding to the pressure compensation coefficient and the concentration compensation amount, the second target parameter estimation equation can be differentiated. Specifically, the second target parameter estimation equation can be differentiated according to the pressure compensation coefficient and the concentration compensation amount, respectively, to obtain the third derivative corresponding to the pressure compensation coefficient and the fourth derivative corresponding to the concentration compensation amount.

[0065] Specifically, solve K and K1 so that Minimize Divide into K, K1 derivative, get the third derivative and the fourth derivative

[0066] After obtaining the third derivative corresponding to the pressure compensation coefficient and the fourth derivative corresponding to the concentration compensation amount, the pressure compensation coefficient and concentration compensation amount corresponding to when the third derivative and the fourth derivative are zero can be obtained based on the historical pressure signal after the above processing and the historical concentration test value of the slurry.

[0067] Specifically, setting the above formula to zero, we can get the solutions of K and K1 respectively: as well as in, is the historical pressure signal P i Then, the pressure compensation coefficient and concentration compensation amount can be obtained.

[0068] Step 210, substituting the pressure compensation coefficient, the concentration compensation amount and the slurry density detection model into the initial concentration detection model to obtain a slurry concentration detection model;

[0069] In this embodiment, after obtaining the pressure compensation coefficient and the concentration compensation amount, the pressure compensation coefficient, the concentration compensation amount and the above-obtained slurry density detection model can be substituted into the initial concentration detection model to obtain the corresponding slurry concentration detection model.

[0070] Step 211, based on the collected target pressure difference original signal, target pressure original signal, and the slurry concentration detection model, obtain the slurry concentration detection value.

[0071] In this embodiment, after obtaining the slurry concentration detection model, the target pressure difference original signal and the target pressure original signal collected by the sensor can be used as the basis, and the slurry concentration detection value can be detected in real time through the calculation of the slurry concentration detection model. Here, the target pressure difference original signal and the target pressure original signal collected by the sensor can also be processed by the Laida criterion, the first-order inertial filter, etc., and then input into the slurry concentration detection model to obtain the slurry concentration detection value.

[0072] In an embodiment of the present application, optionally, after the "obtaining the slurry concentration detection model" in step 210, the method further includes: determining a first concentration test value corresponding to a target sampling time, and based on the target sampling time, determining a plurality of second concentration test values ​​from historical concentration test values; determining a plurality of target concentration detection values ​​corresponding to the first concentration test value and the second concentration test value, and determining a target deviation value based on the first concentration test value, the second concentration test value and the plurality of target concentration detection values; when the target deviation value is greater than a preset deviation threshold, determining a pressure difference compensation update coefficient, a density compensation update amount, a pressure compensation update coefficient and a concentration compensation update amount of the slurry concentration detection model, and updating the slurry concentration detection model based on the pressure difference compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount.

[0073] In this embodiment, after determining the slurry concentration detection model, subsequent staff can also sample the slurry according to the target sampling time interval and test the samples to update the slurry concentration detection model according to the test value. Here, the time when the staff samples the slurry each time can be used as the target sampling time, and a first concentration test value can be determined correspondingly at each target sampling time. The first concentration test value is the concentration test value obtained after testing the slurry sample at the target sampling time. After determining the first concentration test value, multiple second concentration test values ​​can also be determined from the historical concentration test values ​​based on the target sampling time. Then, the target concentration detection value corresponding to the first concentration test value and the multiple target concentration detection values ​​corresponding to the multiple second concentration test values ​​can be determined. Here, the target concentration detection value is the concentration value calculated by the slurry concentration detection model at the same time as the concentration test value. Afterwards, the target deviation value is calculated by the first concentration test value, the second concentration test value, and the multiple corresponding target concentration test values. For example, the cumulative sliding time window width corresponds to (M+1) concentration test values Among them, c α (T) may be the first concentration assay value, c α (T-1)……c α (TM) can be multiple second concentration test values, and the Laida criterion (3σ) error correction mechanism is introduced to eliminate the influence of abnormal test values. Target concentration detection value When the target deviation value θ(T) exceeds the preset deviation threshold δ, that is, θ(T)>δ, the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount can be determined, and the slurry concentration detection model can be updated through the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount, that is, the model parameters can be corrected, which can be achieved through the parameter on-site / remote correction mode, where the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount are the update parameters of the slurry concentration detection model. Here, θ(T) can be: The embodiment of the present application determines the target deviation value based on the first concentration test value corresponding to the target sampling time, and when the target deviation value is greater than the preset deviation threshold, the slurry concentration detection model is updated at any time, which can make the accuracy of the slurry concentration detection model higher. In this embodiment, as the artificial concentration test value increases, the model parameters in the slurry concentration detection model can be corrected on-site / remotely. When the target deviation value is less than or equal to the preset deviation threshold, it means that the accuracy of the slurry concentration detection model is relatively high, and the slurry concentration detection model can be kept unchanged and continued to be used.

[0074] In an embodiment of the present application, optionally, the "determining the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount of the slurry concentration detection model" includes: determining the first pressure difference original signal and the first pressure original signal corresponding to the first concentration test value, and the second pressure difference original signal and the second pressure original signal corresponding to each of the historical concentration test values; determining the pressure difference compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount based on the first concentration test value, the first pressure difference original signal, the first pressure original signal, and the historical concentration test value, the second pressure difference original signal, and the second pressure original signal.

[0075] In this embodiment, when determining the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount, the above-mentioned method for determining the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount can also be used. Specifically, the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount can be determined based on the first concentration test value, the first pressure difference original signal corresponding to the first concentration test value, the first pressure original signal, and each historical concentration test value, the second pressure difference original signal corresponding to each historical concentration test value, and the second pressure original signal. Then, the corresponding parameters in the slurry concentration detection model can be updated to obtain an updated slurry concentration detection model.

[0076] Figure 2 A schematic diagram of the execution flow of a slurry concentration detection method provided in an embodiment of the present application is shown, such as Figure 2 As shown, the process can be described as follows:

[0077] The differential pressure signal △P(k) collected by the differential pressure sensor and the pressure signal P(k) collected by the pressure sensor are obtained, and the differential pressure signal and the pressure signal are sent to the data acquisition and communication module. The data acquisition and communication module sends the differential pressure signal and the pressure signal to the data storage module, and the data storage module stores them. Then, the data processing module processes the differential pressure signal and the pressure signal according to the Laida criterion, the first-order inertial filtering, the input order of the differential pressure signal and the input order of the pressure signal, etc., and outputs ΔP′(k) and P′(k). Finally, the parameter identification model determines the model parameters (differential pressure compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount) of the slurry concentration detection model according to ΔP′(k) and P′(k), and then substitutes the obtained model parameters into the initial concentration detection model to obtain the slurry concentration detection model. Subsequently, the intelligent concentration detection value can be obtained according to the differential pressure signal, the pressure signal and the slurry concentration detection model collected by the sensor.

[0078] In addition, after obtaining the slurry concentration detection model, the slurry concentration detection model can be updated in real time. Specifically, one of the two methods, local or remote, can be selected to update the model parameters. For example, when updating the model parameters, the pressure difference signal △P(k) collected by the pressure difference sensor, the pressure signal P(k) collected by the pressure sensor, and the artificial concentration test value c α (T) is sent to the remote correction module, which then can realize remote automatic correction of the model parameters and send the new model parameters to the identification model to obtain an updated slurry concentration detection model.

[0079] The slurry concentration detection method provided in the embodiment of the present application has high detection accuracy. The detection effect and the instrument effect are compared by introducing evaluation indicators. Root mean square error: the expected value of the square root of the difference between the intelligent detection value (or instrument value) and the test value at the corresponding test time. Qualified rate index |P|: the percentage of samples with an absolute error between the intelligent detection value (or instrument value) and the concentration test value within ±2% (|P|<2%) and ±1% (|P|<1%) at the corresponding test time, accounting for the total number of samples. Rising trend accuracy: the percentage of the number of intelligent detection values ​​(or instrument values) with the same rising trend as the test value at the corresponding test time accounting for the number of rising test values. Downward trend accuracy: the percentage of the number of intelligent detection values ​​(or instrument values) with the same downward trend as the test value at the corresponding test time accounting for the number of falling test values. By Figure 3It can be seen that through long-term observation of actual concentration operation, the root mean square error of the slurry concentration detection value of this application compared with the manual test value is 0.8268; the qualified rate |P|<2% is as high as 97.09%, and |P|<1% is 76.16%; the accuracy of intelligent detection of rising trends is 81.04%; the accuracy of intelligent detection of falling trends is 77.92%. The slurry concentration detection method of this application has lower computing power requirements, can achieve accurate and fast online detection, is more suitable for deployment in the field environment, and provides a basis for realizing the operation optimization control of the mineral processing production process.

[0080] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a slurry concentration detection device, such as Figure 4 As shown, the device comprises:

[0081] An acquisition module is used to obtain historical differential pressure signals, historical pressure signals and historical slurry concentration test values ​​corresponding to historical sampling moments;

[0082] A value determination module, used to determine a pressure difference compensation coefficient, a density compensation amount, a pressure compensation coefficient and a concentration compensation amount based on the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value;

[0083] A model determination module, used for obtaining a slurry concentration detection model according to the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model;

[0084] The detection value determination module is used to obtain the slurry concentration detection value based on the collected target pressure difference original signal, the target pressure original signal, and the slurry concentration detection model.

[0085] Optionally, the value determination module includes:

[0086] A first equation building unit, used to build a first target parameter estimation equation based on an initial density detection model;

[0087] a derivation unit, configured to derive the first target parameter estimation equation according to the pressure difference compensation coefficient and the density compensation amount, respectively, to obtain a first derivative and a second derivative, and to determine the pressure difference compensation coefficient and the density compensation amount corresponding to zero when the first derivative and the second derivative are zero based on the historical pressure difference signal and the historical slurry concentration test value;

[0088] A substitution unit, used for substituting the pressure difference compensation coefficient and the density compensation amount into the initial density detection model to obtain a slurry density detection model;

[0089] A second equation construction unit is used to construct a second target parameter estimation equation based on the initial concentration detection model, wherein the initial concentration detection model is determined based on the slurry density detection model;

[0090] The derivation unit is further used to derive the second target parameter estimation equation according to the pressure compensation coefficient and the concentration compensation amount respectively to obtain a third derivative and a fourth derivative, and determine the pressure compensation coefficient and the concentration compensation amount corresponding to when the third derivative and the fourth derivative are zero based on the historical pressure signal and the slurry density detection model.

[0091] Optionally, the device further comprises:

[0092] An order determination module, used for determining the input order of the pressure difference signal and the input order of the pressure signal before constructing the first target parameter estimation equation based on the initial density detection model;

[0093] A first averaging module is used to sequentially use the historical pressure difference signals corresponding to each of the historical sampling moments as target historical pressure difference signals, determine a first target number of matching pressure difference signals based on each of the target historical pressure difference signals, and determine an average value of the pressure difference signals according to each of the target historical pressure difference signals and the corresponding matching pressure difference signals, and use the average value of the pressure difference signals as a processed historical pressure difference signal corresponding to the target historical pressure difference signal, wherein the first target number is obtained based on the input order of the pressure difference signals;

[0094] The second averaging module is used to sequentially use the historical pressure signals corresponding to each of the historical sampling moments as the target historical pressure signals, determine a second target number of matching pressure signals based on each of the target historical pressure signals, and determine a pressure signal average value based on each of the target historical pressure signals and the corresponding matching pressure signals, and use the pressure signal average value as the processed historical pressure signal corresponding to the target historical pressure signal. The second target number is obtained based on the input order of the pressure signal.

[0095] Optionally, the model determination module is also used to: after determining the pressure compensation coefficient and the concentration compensation amount corresponding to when the third derivative and the fourth derivative are zero, substitute the pressure compensation coefficient, the concentration compensation amount and the slurry density detection model into the initial concentration detection model to obtain the slurry concentration detection model.

[0096] Optionally, the device further comprises:

[0097] A signal processing module, which is used for performing signal filtering processing on the historical pressure difference original signal and the historical pressure original signal collected by the sensor respectively by using the Laida criterion before obtaining the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value corresponding to the historical sampling moment, and performing first-order inertial filtering processing on the filtered historical pressure difference original signal and the historical pressure original signal to obtain the historical pressure difference signal and the historical pressure signal;

[0098] The detection value determination module is also used to determine the historical sampling time, and determine the historical slurry concentration test value, the historical pressure difference signal and the historical pressure signal corresponding to the historical sampling time, and obtain the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value corresponding to each historical sampling time.

[0099] Optionally, the device further comprises:

[0100] An assay value determination module, for determining a first concentration assay value corresponding to a target sampling time after obtaining the slurry concentration detection model, and determining a plurality of second concentration assay values ​​from historical concentration assay values ​​based on the target sampling time;

[0101] a deviation value determination module, configured to determine a plurality of target concentration detection values ​​corresponding to the first concentration test value and the second concentration test value, and determine a target deviation value based on the first concentration test value, the second concentration test value and the plurality of target concentration test values;

[0102] A model updating module is used to determine the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount of the slurry concentration detection model when the target deviation value is greater than a preset deviation threshold, and update the slurry concentration detection model based on the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount.

[0103] Optionally, the model updating module is used to:

[0104] Determine the first pressure difference original signal and the first pressure original signal corresponding to the first concentration test value, and the second pressure difference original signal and the second pressure original signal corresponding to each of the historical concentration test values; determine the pressure difference compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount based on the first concentration test value, the first pressure difference original signal, the first pressure original signal, and the historical concentration test value, the second pressure difference original signal, and the second pressure original signal.

[0105] It should be noted that for other corresponding descriptions of the functional units involved in the slurry concentration detection device provided in the embodiment of the present application, reference can be made to Figure 1 The corresponding description in the method will not be repeated here.

[0106] Further, optionally, in the embodiment of the present application, if Figure 5 As shown in the figure, the industrial application hardware platform of the slurry concentration detection model may include: differential pressure sensor, pressure sensor, instrument box, signal isolator, logic controller (PLC), switch, touch screen, communication equipment, etc. Among them, probe 1 and probe 2 are differential pressure probes, and probe 3 is a pressure probe. The installation positions of probe 2 and probe 3 can be exchanged according to the actual production conditions. The signal isolator, PLC, switch, touch screen and communication module are deployed in the instrument box. PLC is used to realize intelligent real-time detection of slurry concentration based on multiple input information; the touch screen is used to realize the functions of local display, parameter setting, local parameter update, and local / remote parameter correction mode switching of slurry concentration detection value; the communication module is based on the communication between the switch, touch screen and PLC, and is used to realize data reading and remote model update parameter writing, that is, to realize remote parameter correction of the slurry concentration detection model. At the same time, PLC is connected to the on-site DCS to realize real-time display and data management of slurry concentration detection value.

[0107] Industrial sensors (differential pressure sensors, pressure sensors) are connected to the transmitter through pressure pipes, and the transmitter is connected to the isolator in the instrument box through hard lines. The isolator transmits the signal to the PLC to realize communication from the transmitter to the PLC; the PLC, touch screen, and communication module in the concentration meter instrument box are in the same local area network, and data is transmitted to each other through the TCP / IP protocol; the PLC communicates with the on-site DCS through hard lines; the remote correction module communicates with the device in the on-site instrument box through a wireless network, so that the data and test results of the industrial site can be uploaded to the "cloud" and centralized data management can be achieved.

[0108] Optionally, in the embodiment of the present application, Figure 6As shown, the industrial application software platform of the slurry concentration detection model can include two parts: software front-end human-computer interaction and background function realization. Specifically, the WinCC Flexible Smart application software platform can be used to build a front-end human-computer interaction system; STEP 7-MicroWIN SMART and Python software platforms can be used to build data acquisition and communication, multi-input information slurry concentration intelligent detection and remote correction system. Among them, the front-end human-computer interaction interface (touch screen) is developed based on the WinCC Flexible Smart application software platform to realize human-computer interaction; the background PLC is edited using the STEP 7-MicroWIN SMART software platform to realize the intelligent detection of slurry concentration based on multi-input information; the remote parameter correction function is developed using the Python software platform, and the software platform communicates with the PLC and touch screen through the communication module to read key data and write model update parameters.

[0109] Design and develop industrial application software. The application software platform has developed application modules such as on-site real-time display of slurry concentration, parameter setting, on-site parameter update, and parameter on-site / remote correction mode switching, realizing human-computer interaction and on-site parameter update. The background software mainly includes application modules such as system communication, data acquisition and storage, data processing, and intelligent detection, realizing intelligent detection technology for slurry concentration with multiple input information and remote parameter correction function.

[0110] Based on the above Figure 1 The method shown in the embodiment of the present application accordingly provides a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned Figure 1 The slurry concentration detection method shown.

[0111] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0112] Based on the above Figure 1 The method shown, and Figure 4 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides a computer device, which can be a personal computer, a server, a network device, etc. The computer device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 The slurry concentration detection method shown.

[0113] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0114] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not limit the computer device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0115] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and saves the hardware and software resources of the computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between the components inside the storage medium, and communication with other hardware and software in the physical device.

[0116] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the technical solution of this embodiment, first, the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to the historical sampling time can be obtained. Further, the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount in the initial concentration detection model can be determined based on the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to each historical sampling time. After obtaining the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient and concentration compensation amount, the slurry concentration detection model can be obtained based on the pressure difference compensation coefficient, density compensation amount, pressure compensation coefficient, concentration compensation amount and initial concentration detection model. After obtaining the slurry concentration detection model, the slurry concentration detection value can be obtained by calculating the slurry concentration detection model according to the target pressure difference original signal and target pressure original signal collected by the sensor. The embodiment of the present application determines the slurry concentration detection model, and then calculates the slurry concentration detection value based on the target pressure difference original signal and the target pressure original signal collected by the sensor. The slurry concentration can be detected quickly and accurately in real time while considering multiple influencing factors.

[0117] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0118] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for detecting slurry concentration, characterized in that: include: Obtain the historical pressure difference signal, historical pressure signal and historical slurry concentration test value corresponding to the historical sampling time; Determine a pressure difference compensation coefficient, a density compensation amount, a pressure compensation coefficient, and a concentration compensation amount based on the historical pressure difference signal, the historical pressure signal, and the historical slurry concentration test value; A slurry concentration detection model is obtained according to the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model, wherein the parameters of the slurry concentration detection model are identified based on an arbitrary system identification method; Based on the collected target pressure difference original signal, target pressure original signal, and the slurry concentration detection model, a slurry concentration detection value is obtained; The method for identifying any system includes a least squares system multiplication identification method; when the least squares system identification method is adopted, the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient and the concentration compensation amount are determined based on the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value, including: Based on the initial density detection model, a first target parameter estimation equation is constructed; The first target parameter estimation equation is derived according to the pressure difference compensation coefficient and the density compensation amount to obtain a first derivative and a second derivative, and based on the historical pressure difference signal and the historical slurry concentration test value, the pressure difference compensation coefficient and the density compensation amount corresponding to zero are determined; Substituting the pressure difference compensation coefficient and the density compensation amount into the initial density detection model to obtain a slurry density detection model; Based on the initial concentration detection model, constructing a second target parameter estimation equation, wherein the initial concentration detection model is determined based on the slurry density detection model; The second target parameter estimation equation is derived according to the pressure compensation coefficient and the concentration compensation amount to obtain a third derivative and a fourth derivative, and based on the historical pressure signal and the slurry density detection model, the pressure compensation coefficient and the concentration compensation amount corresponding to zero when the third derivative and the fourth derivative are determined; The initial density detection model is: ； ； in, is the processed historical differential pressure signal, g is the gravity acceleration, △h is the installation height of the differential pressure transmitter, B is the differential pressure compensation coefficient, B1 is the density compensation amount, and n2 is the first target number determined based on the input order of the differential pressure signal; The initial concentration detection model is: ； ； in, is the true density of the original ore, is the processed historical pressure signal, K is the pressure compensation coefficient, K1 is the concentration compensation amount, and n1 is the second target quantity determined based on the pressure signal input order.

2. The method according to claim 1, characterized in that Before constructing the first target parameter estimation equation based on the initial density detection model, the method further includes: Determine the input order of the differential pressure signal and the input order of the pressure signal; Sequentially taking the historical pressure difference signals corresponding to the respective historical sampling moments as target historical pressure difference signals, determining a first target number of matching pressure difference signals based on each target historical pressure difference signal, and determining a pressure difference signal average value according to each target historical pressure difference signal and the corresponding matching pressure difference signal, taking the pressure difference signal average value as a processed historical pressure difference signal corresponding to the target historical pressure difference signal, wherein the first target number is obtained based on the input order of the pressure difference signal; The historical pressure signals corresponding to each of the historical sampling moments are taken in turn as target historical pressure signals, and a second target number of matching pressure signals is determined based on each of the target historical pressure signals. The pressure signal average value is determined based on each of the target historical pressure signals and the corresponding matching pressure signals, and the pressure signal average value is taken as the processed historical pressure signal corresponding to the target historical pressure signal. The second target number is obtained based on the pressure signal input order.

3. The method according to claim 1, characterized in that After determining the pressure compensation coefficient and the concentration compensation amount corresponding to when the third derivative and the fourth derivative are zero, the method further includes: The pressure compensation coefficient, the concentration compensation amount and the slurry density detection model are substituted into the initial concentration detection model to obtain a slurry concentration detection model.

4. The method according to claim 1, characterized in that: Before obtaining the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value corresponding to the historical sampling time, the method further includes: The historical pressure difference original signal and the historical pressure original signal collected by the sensor are respectively subjected to signal filtering processing by using the Laida criterion, and the filtered historical pressure difference original signal and the historical pressure original signal are subjected to first-order inertial filtering processing to obtain a historical pressure difference signal and a historical pressure signal; Determine the historical sampling moment, and determine the historical slurry concentration test value, the historical pressure difference signal and the historical pressure signal corresponding to the historical sampling moment, and obtain the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value corresponding to each historical sampling moment.

5. The method according to claim 1, characterized in that After obtaining the slurry concentration detection model, the method further includes: Determine a first concentration test value corresponding to a target sampling time, and determine a plurality of second concentration test values ​​from historical concentration test values ​​based on the target sampling time; Determine a plurality of target concentration detection values ​​corresponding to the first concentration test value and the second concentration test value, and determine a target deviation value based on the first concentration test value, the second concentration test value, and the plurality of target concentration test values; When the target deviation value is greater than a preset deviation threshold, the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount of the slurry concentration detection model are determined, and the slurry concentration detection model is updated based on the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount.

6. The method according to claim 5, characterized in that The step of determining the pressure difference compensation update coefficient, density compensation update amount, pressure compensation update coefficient and concentration compensation update amount of the slurry concentration detection model includes: Determine a first pressure difference original signal and a first pressure original signal corresponding to the first concentration test value, and a second pressure difference original signal and a second pressure original signal corresponding to each of the historical concentration test values; Based on the first concentration test value, the first pressure difference original signal, the first pressure original signal, and the historical concentration test value, the second pressure difference original signal, and the second pressure original signal, the pressure difference compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient, and the concentration compensation update amount are determined.

7. A slurry concentration detection device, characterized in that: include: An acquisition module is used to obtain historical differential pressure signals, historical pressure signals and historical slurry concentration test values ​​corresponding to historical sampling moments; A value determination module, used to determine a pressure difference compensation coefficient, a density compensation amount, a pressure compensation coefficient and a concentration compensation amount based on the historical pressure difference signal, the historical pressure signal and the historical slurry concentration test value; A model determination module, for obtaining a slurry concentration detection model according to the pressure difference compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model, wherein the parameters of the slurry concentration detection model are identified based on an arbitrary system identification method; A detection value determination module, used to obtain a slurry concentration detection value based on the collected target pressure difference original signal, the target pressure original signal, and the slurry concentration detection model; The method for identifying any system includes a least squares system multiplication identification method; when the least squares system identification method is adopted, the numerical determination module includes: A first equation building unit, used to build a first target parameter estimation equation based on an initial density detection model; a derivation unit, configured to derive the first target parameter estimation equation according to the pressure difference compensation coefficient and the density compensation amount, respectively, to obtain a first derivative and a second derivative, and to determine the pressure difference compensation coefficient and the density compensation amount corresponding to zero when the first derivative and the second derivative are zero based on the historical pressure difference signal and the historical slurry concentration test value; A substitution unit, used for substituting the pressure difference compensation coefficient and the density compensation amount into the initial density detection model to obtain a slurry density detection model; A second equation building unit, configured to build a second target parameter estimation equation based on the initial concentration detection model, wherein the initial concentration detection model is determined based on the slurry density detection model; The derivation unit is further used to derive the second target parameter estimation equation according to the pressure compensation coefficient and the concentration compensation amount respectively to obtain a third derivative and a fourth derivative, and determine the pressure compensation coefficient and the concentration compensation amount corresponding to zero when the third derivative and the fourth derivative are zero based on the historical pressure signal and the slurry density detection model; The initial density detection model is: ； ； in, is the processed historical differential pressure signal, g is the gravity acceleration, △h is the installation height of the differential pressure transmitter, B is the differential pressure compensation coefficient, B1 is the density compensation amount, and n2 is the first target number determined based on the input order of the differential pressure signal; The initial concentration detection model is: ； ； in, is the true density of the original ore, is the processed historical pressure signal, K is the pressure compensation coefficient, K1 is the concentration compensation amount, and n1 is the second target quantity determined based on the pressure signal input order.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

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