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CN114894665B - A method and device for detecting slurry concentration, storage medium, and computer equipment - Google Patents

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

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CN114894665B
CN114894665B CN202210621822.6A CN202210621822A CN114894665B CN 114894665 B CN114894665 B CN 114894665B CN 202210621822 A CN202210621822 A CN 202210621822A CN 114894665 B CN114894665 B CN 114894665B
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compensation
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CN114894665A (en
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柴天佑
韩先尧
贾瑶
赵亮
杜雪蕾
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Northeastern University China
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
    • G01N9/26—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity by measuring pressure differences
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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Abstract

本申请公开了一种矿浆浓度检测方法及装置、存储介质、计算机设备，该方法包括：获取历史采样时刻对应的历史压差信号、历史压力信号以及矿浆历史浓度化验值；基于所述历史压差信号、所述历史压力信号以及所述矿浆历史浓度化验值，确定压差补偿系数、密度补偿量、压力补偿系数以及浓度补偿量；依据所述压差补偿系数、所述密度补偿量、所述压力补偿系数、所述浓度补偿量以及初始浓度检测模型，得到矿浆浓度检测模型；基于采集到的目标压差原始信号、目标压力原始信号，以及所述矿浆浓度检测模型，得到矿浆浓度检测值。本申请在考虑多种影响因素的同时，可以实时快速、准确地检测矿浆浓度。

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 moment; 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

Ore pulp concentration detection method and device, storage medium and computer equipment
Technical Field
The application relates to the technical field of industrial processes, in particular to a method and a device for detecting concentration of ore pulp, a storage medium and computer equipment.
Background
In mineral separation production, the concentration of ore pulp is used as a key technological parameter in the links of grinding classification, magnetic separation, concentration and filter pressing, and the like, and the detection precision of the ore pulp can greatly influence the classification efficiency of a cyclone, the overflow particle size distribution of the cyclone, the recovery efficiency of a magnetic separator, the concentrate grade, the concentration and filter pressing effect, and the like. If the concentration detection precision does not meet the process requirement, indexes such as concentrate grade, metal recovery rate, auxiliary agent consumption and equipment energy consumption are often reduced.
In the prior art, when determining the slurry concentration, a differential pressure concentration meter is typically used for determination. The differential pressure concentration meter is the optimal choice for the on-line detection of the pulp concentration due to the advantages of no radiation hazard, simple installation, stable measurement result, high cost performance and the like. In the non-flowing state and in the steady state, the pulp concentration is directly related to the pressure difference. Therefore, when the ore pulp is in static state and steady state, the differential pressure concentration meter has a good concentration detection effect. However, the actual mineral separation production process is often disturbed by dynamic uncertainty both inside and outside, and the slurry in the pipeline is always in a dynamic flow state. Various concentration detection instruments such as a differential pressure concentration meter and the like are generally based on a single factor, the concentration of ore pulp is not considered to be related to a plurality of variables and is always in a dynamic change process, the dynamic characteristics of the differential pressure concentration meter have strong nonlinearity and strong coupling comprehensive complexity, the differential pressure concentration meter and the like are difficult to apply for a long time in a mine environment with a plurality of flow change factors and relatively bad working environment and conditions, and the accuracy of determining the concentration of ore pulp in actual conditions through the differential pressure concentration meter and the like is not high.
Therefore, how to solve the problems of low detection precision and fixed instrument parameters of the concentration detection instruments such as the differential pressure concentration meter in the prior art becomes a technical problem to be solved currently.
Disclosure of Invention
In view of the above, the application provides a method and a device for detecting the concentration of ore pulp, a storage medium and computer equipment, which can rapidly and accurately detect the concentration of ore pulp in real time while considering various influencing factors.
According to one aspect of the present application, there is provided a pulp concentration detection method comprising:
Acquiring a historical pressure difference signal, a historical pressure signal and a pulp historical concentration assay value corresponding to the historical sampling moment;
determining a differential pressure compensation coefficient, a density compensation amount, a pressure compensation coefficient and a concentration compensation amount based on the historical differential pressure signal, the historical pressure signal and the pulp historical concentration assay value;
obtaining a pulp concentration detection model according to the differential pressure compensation coefficient, the density compensation quantity, the pressure compensation coefficient, the concentration compensation quantity and the initial concentration detection model;
And obtaining a pulp concentration detection value based on the collected target differential pressure original signal, the target pressure original signal and the pulp concentration detection model.
According to another aspect of the present application, there is provided a pulp concentration detection apparatus comprising:
The acquisition module is used for acquiring a historical pressure difference signal, a historical pressure signal and a pulp historical concentration assay value corresponding to the historical sampling moment;
The numerical value determining module is used for determining a differential pressure compensation coefficient, a density compensation quantity, a pressure compensation coefficient and a concentration compensation quantity based on the historical differential pressure signal, the historical pressure signal and the pulp historical concentration test value;
the model determining module is used for obtaining an ore pulp concentration detection model according to the differential pressure compensation coefficient, the density compensation quantity, the pressure compensation coefficient, the concentration compensation quantity and the initial concentration detection model;
The detection value determining module is used for obtaining a pulp concentration detection value based on the collected target differential pressure original signal, the target pressure original signal and the pulp concentration detection model.
According to a further aspect of the present application there is provided a storage medium having stored thereon a computer program which when executed by a processor implements the above-described pulp concentration detection method.
According to a further aspect of the present application there is provided a computer device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, the processor implementing the above pulp concentration detection method when executing the program.
By means of the technical scheme, the pulp concentration detection method and device, the storage medium and the computer equipment can obtain the historical pressure difference signal, the historical pressure signal and the pulp historical concentration assay value corresponding to the historical sampling moment. Further, the differential pressure 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 differential pressure signal, the historical pressure signal and the pulp historical concentration test value corresponding to each historical sampling time. After the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient and the concentration compensation amount are obtained, the ore pulp concentration detection model can be obtained based on the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model. After the ore pulp concentration detection model is obtained, the ore pulp concentration detection value can be obtained by calculating the ore pulp concentration detection model according to the target pressure difference original signal and the target pressure original signal acquired by the sensor. According to the embodiment of the application, the pulp concentration detection value can be calculated according to the target differential pressure original signal and the target pressure original signal acquired by the sensor by determining the pulp concentration detection model, and the pulp concentration can be rapidly and accurately detected in real time while various influencing factors are considered.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 shows a schematic flow chart of a method for detecting pulp concentration according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of another method for detecting pulp concentration according to an embodiment of the present application;
fig. 3 shows a schematic diagram of a detection effect of a pulp concentration detection method according to an embodiment of the present application;
Fig. 4 is a schematic structural diagram of an apparatus for detecting pulp concentration according to an embodiment of the present application;
fig. 5 shows an industrial application hardware architecture diagram of a pulp concentration detection method according to an embodiment of the present application;
Fig. 6 shows an industrial application software architecture diagram of a pulp concentration detection method according to an embodiment of the present application.
Detailed Description
The application will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
In this embodiment, a method for detecting a concentration of pulp is provided, as shown in fig. 1, and the method includes:
step 101, acquiring a historical pressure difference signal, a historical pressure signal and a pulp historical concentration assay value corresponding to a historical sampling moment;
The ore pulp concentration detection method provided by the embodiment of the application can be applied to the scene of controlling the ore feeding production concentration of the magnetic separation column of the concentrating mill, and can accurately determine the ore pulp concentration in real time. First, a historical differential pressure signal, a historical pressure signal, and a pulp historical concentration assay value corresponding to a historical sampling time may be obtained, that is, each set of the historical differential pressure signal, the historical pressure signal, and the pulp historical concentration assay value corresponds to one historical sampling time. The historical differential pressure signal and the historical pressure signal can be signals directly collected through a differential pressure sensor and a pressure sensor arranged in ore pulp, can also be processed signals, and the historical concentration test value of the ore pulp can be obtained by testing after the ore pulp is sampled by a worker at the historical sampling moment. Compared with detection by a concentration detection instrument, the manual assay accuracy is higher, but due to the longer lag time of the manual assay, the adjustment of the current pulp concentration by the pulp concentration obtained by the assay is not reasonable. Therefore, the embodiment of the application helps to determine the pulp concentration detection model through the pulp historical concentration assay value.
Step 102, determining a differential pressure compensation coefficient, a density compensation amount, a pressure compensation coefficient and a concentration compensation amount based on the historical differential pressure signal, the historical pressure signal and the pulp historical concentration test value;
In this embodiment, further, the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, and the concentration compensation amount may be determined based on the historical differential pressure signal, the historical pressure signal, and the pulp historical concentration assay value corresponding to each of the historical sampling timings, respectively. Here, the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, and the concentration compensation amount are all related model parameters of the initial concentration detection model.
Step 103, obtaining a pulp concentration detection model according to the differential pressure compensation coefficient, the density compensation quantity, the pressure compensation coefficient, the concentration compensation quantity and an initial concentration detection model;
in this embodiment, after the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, and the concentration compensation amount are obtained, the pulp concentration detection model may be obtained according to the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount, and the initial concentration detection model. That is, the obtained differential pressure compensation coefficient, density compensation amount, pressure compensation coefficient, concentration compensation amount and initial concentration detection model are utilized to obtain the ore pulp concentration detection model through deformation operation and the like.
And 104, obtaining a pulp concentration detection value based on the collected target pressure difference original signal, the target pressure original signal and the pulp concentration detection model.
In the embodiment, after the pulp concentration detection model is obtained, the pulp concentration detection value can be obtained by calculating the pulp concentration detection model according to the target differential pressure original signal and the target pressure original signal acquired by the sensor.
Through the technical scheme of the embodiment, firstly, a historical pressure difference signal, a historical pressure signal and an ore pulp historical concentration test value corresponding to the historical sampling moment can be obtained. Further, the differential pressure 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 differential pressure signal, the historical pressure signal and the pulp historical concentration test value corresponding to each historical sampling time. After the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient and the concentration compensation amount are obtained, the ore pulp concentration detection model can be obtained based on the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model. After the ore pulp concentration detection model is obtained, the ore pulp concentration detection value can be obtained by calculating the ore pulp concentration detection model according to the target pressure difference original signal and the target pressure original signal acquired by the sensor. According to the embodiment of the application, the pulp concentration detection value can be calculated according to the target differential pressure original signal and the target pressure original signal acquired by the sensor by determining the pulp concentration detection model, and the pulp concentration can be rapidly and accurately detected in real time while various influencing factors are considered.
Further, as a refinement and extension of the specific implementation of the foregoing embodiment, for a complete description of the specific implementation of the embodiment, another method for detecting a pulp concentration is provided, where the method includes:
Step 201, performing signal filtering processing on a historical pressure difference original signal and the historical pressure original signal acquired by a sensor by utilizing a Laida criterion, 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 moment, and determining the pulp historical concentration assay value, the historical pressure difference signal and the historical pressure signal corresponding to the historical sampling moment to obtain the historical pressure difference signal, the historical pressure signal and the pulp historical concentration assay value corresponding to each historical sampling moment;
In this embodiment, the historical differential pressure raw signal and the historical pressure raw signal may be directly acquired through a differential pressure sensor and a pressure sensor, and may be stored in a database after being acquired, where the historical differential pressure raw signal may be Δp (k), the historical pressure raw signal may be P (k), and k represents a historical sampling time. First, the historical pressure difference raw signal and the historical pressure raw signal stored in the database can be subjected to signal filtering processing by utilizing the Laida criterion. Specifically, the historical differential pressure original signal and the historical pressure original signal may be arranged according to a time sequence, and outlier rejection is performed in two sliding time windows respectively through the radon criterion, where the standard error sigma of the radon criterion (3σ) may be calculated by using a bessel formula: Wherein x i is an original signal, which can be a historical pressure difference original signal or a historical pressure original signal, n is a sample size, specifically, the sample size corresponding to the width of each sliding time window, Is the mean of n groups of samples. And secondly, performing first-order inertial filtering on the historical differential pressure original signal and the historical pressure original signal after the signal filtering processing, wherein the first-order inertial filtering function can be X (n) =alpha X (n) + (1-alpha) X (n-1), wherein alpha is a filtering coefficient, specifically can be 0.9, X (n) is a sampling value of this time, X (n-1) is a last filtering output value, and X (n) is a current filtering output value. After the Laida criterion and the first-order inertia filtering treatment, the data abnormal value can be removed, and the influence of data noise is reduced, so that the subsequent pulp concentration detection value is more close to the true value.
Then, the historical concentration test value, the historical pressure difference signal and the historical pressure signal of the ore pulp can be subjected to time sequence standard comparison treatment, namely, the historical pressure difference signal, the historical pressure signal and the historical concentration test value of the ore pulp corresponding to each historical sampling time can be found. Because the sampling time interval of the manual test is far longer than that of the sensor, the time of manual sampling from the ore pulp can be used as the historical sampling time, and the historical pressure difference signal and the historical pressure signal corresponding to the historical sampling time are respectively found and marked from the historical pressure difference signal and the historical pressure signal, so that the historical pressure difference signal, the historical pressure signal and the ore pulp historical concentration test value corresponding to each historical sampling time are obtained.
Step 202, acquiring a historical pressure difference signal, a historical pressure signal and a pulp historical concentration assay value corresponding to a historical sampling moment;
in this embodiment, a historical differential pressure signal, a historical pressure signal, and a pulp historical concentration assay value corresponding to a historical sampling time may be obtained, i.e., each set of the historical differential pressure signal, the historical pressure signal, and the pulp historical concentration assay value corresponds to one historical sampling time.
Step 203, determining a differential pressure signal input order and a pressure signal input order, sequentially taking the historical differential pressure signals corresponding to each historical sampling moment as target historical differential pressure signals, respectively determining a first target number of matched differential pressure signals based on each target historical differential pressure signal, determining a differential pressure signal average value according to each target historical differential pressure signal and the corresponding matched differential pressure signals, taking the differential pressure signal average value as a processed historical differential pressure signal corresponding to the target historical differential pressure signals, and obtaining the first target number based on the differential pressure signal input order;
In this embodiment, the differential pressure signal input order and the pressure signal input order may be determined first, and then the historical differential pressure signal and the historical pressure signal may be processed according to the differential pressure signal input order and the pressure signal input order, respectively. Specifically, the historical differential pressure signals corresponding to the historical sampling moments may be alternately used as the target historical differential pressure signals according to a time sequence or other sequences, then, a first target number of matching differential pressure signals may be determined from the historical differential pressure signals based on each target historical differential pressure signal, and the matching differential pressure signals may be the historical differential pressure signals corresponding to the first target number of the historical sampling moments from the historical sampling moments corresponding to the target historical differential pressure signals. After the matching differential pressure signal corresponding to the target historical differential pressure signal is determined, the differential pressure signal average value can be determined based on the target historical differential pressure signal and the corresponding matching differential pressure signal, and the differential pressure signal average value is used as the processed historical differential pressure signal corresponding to the target historical differential pressure signal. Here, the first target number may be equal to the differential pressure signal input order, for example, the differential pressure signal input order is 5, and then the first target number may be 5, or may be obtained based on a mapping relationship with the differential pressure signal input order. Specifically, ΔP' (k) may represent the post-processing historical differential pressure signal, and may be represented by the formula Processing is performed wherein n 2 is a first target number determined based on the differential pressure signal input order.
Step 204, sequentially taking the historical pressure signals corresponding to each historical sampling time as target historical pressure signals, respectively determining a second target number of matched pressure signals based on each target historical pressure signal, determining a pressure signal average value according to each target historical pressure signal and the corresponding matched pressure signal, taking the pressure signal average value as a processed historical pressure signal corresponding to the target historical pressure signal, wherein the second target number is obtained based on the pressure signal input order;
In this embodiment, the historical pressure signals corresponding to the respective historical sampling moments may be alternately set as the target historical pressure signals in time series or in other series, and then, a second target number of matching pressure signals may be determined from the historical pressure signals on a per-target historical pressure signal basis, and the matching pressure signals may be the historical pressure signals corresponding to the respective historical sampling moments of the second target number onward from the historical sampling moments corresponding to the target historical pressure signals. After determining the matching pressure signal corresponding to the target historical pressure signal, a pressure signal average value may be determined based on the target historical pressure signal and the corresponding matching pressure signal, and the pressure signal average value may be used as the processed historical pressure signal corresponding to the target historical pressure signal. Here, the second target number may be equal to the pressure signal input order, for example, the pressure signal input order is 5, and then the second target number may be 5, or may be obtained based on a mapping relationship with the pressure signal input order. In particular, P' (k) may represent the processed historical pressure signal, in particular by the formula Processing is performed wherein n 1 is a second target number determined based on the pressure signal input order. According to the embodiment of the application, the historical pressure difference signal and the historical pressure signal are processed according to the pressure difference signal input order and the pressure signal input order, so that the signals are smoother, and abnormal values can be removed easily.
Step 205, constructing a first target parameter estimation equation based on the initial density detection model;
In this embodiment, the first target parameter estimation equation corresponding to the initial density detection model may be constructed based on the initial density detection model. Here, the first target parameter estimation equation may include an unknown number corresponding to the differential pressure compensation coefficient and the density compensation amount.
In an embodiment of the present application, the initial density detection model may beWherein Δp' (k) is the processed historical differential pressure signal, g is the gravitational acceleration g=9.8n/kg, Δh is the installation height of the differential pressure transmitter, B is the differential pressure compensation coefficient, and B 1 is the density compensation amount.
In addition, parameters of the pulp density detection model can be identified based on any system identification method. Specifically, a least squares system identification method may be selected. First, the initial density detection model performs parameter estimation based on mean square error minimization, i.e. the corresponding first target parameter estimation equation may beWherein, The ρ i pulp history density assay value can be specifically calculated by the formula: c α (i) is the pulp historical concentration test value, delta 0 is the true density of the raw ore, and m is the number of pulp historical concentration test values, wherein the number of pulp historical concentration test values is equal to the number of historical pressure difference signals and historical pressure signals.
Step 206, deriving the first target parameter estimation equation according to the differential pressure compensation coefficient and the density compensation amount to obtain a first derivative and a second derivative, and determining the differential pressure compensation coefficient and the density compensation amount corresponding to zero of the first derivative and the second derivative based on the historical differential pressure signal and the pulp historical concentration assay value;
In this embodiment, since the first target parameter estimation equation includes the differential pressure compensation coefficient and the unknown number corresponding to the density compensation amount, the first target parameter estimation equation may be derived, specifically, may be derived according to the differential pressure compensation coefficient and the density compensation amount, respectively, to obtain a first derivative corresponding to the differential pressure compensation coefficient and a second derivative corresponding to the density compensation amount.
Specifically, B, B 1 is solved for causingMinimizing, willDividing into B, B 1 to obtain a first derivativeSecond derivative
After obtaining the first derivative corresponding to the differential pressure compensation coefficient and the second derivative corresponding to the density compensation quantity, the differential pressure compensation coefficient and the density compensation quantity corresponding to zero of the first derivative and the second derivative can be obtained according to the processed historical differential pressure signal and the pulp historical concentration assay value.
Specifically, let the above formula be zero, the solutions that can be obtained for B, B 1 are respectively: And Wherein, Is the mean of the processed historical differential pressure signal ΔP i'. And thus a differential pressure compensation coefficient and a density compensation amount can be obtained.
Step 207, substituting the differential pressure compensation coefficient and the density compensation amount into the initial density detection model to obtain a pulp density detection model;
In this embodiment, after the differential pressure compensation coefficient and the density compensation amount are obtained, the differential pressure compensation coefficient and the density compensation amount may be all substituted into the initial density detection model, so that the pulp density detection model may be obtained. Namely, the obtained differential pressure compensation coefficient and the density compensation quantity are used for replacing the corresponding unknown quantity in the initial density detection model, and the ore pulp density detection model can be obtained.
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 pulp density detection model;
In this embodiment, a second target parameter estimation equation corresponding to the initial concentration detection model may be constructed based on the initial concentration detection model. Here, the second target parameter estimation equation may include an unknown number corresponding to the pressure compensation coefficient and the concentration compensation amount. The second target parameter estimation equation may be constructed based on a pulp density detection model.
In an embodiment of the present application, the initial concentration detection model may beWherein P' (K) is the processed historical pressure signal, K is the pressure compensation coefficient, and K 1 is the concentration compensation quantity.
In addition, parameters of the pulp concentration detection model can be identified based on any system identification method. Specifically, a least squares system identification method may be selected. First, the initial concentration detection model performs parameter estimation based on mean square error minimization, i.e. the corresponding second target parameter estimation equation may beWherein, C α (i) is an ore pulp historical concentration assay value, and delta 0 is the true density of the raw ore.
Step 209, 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 zero of the third derivative and the fourth derivative based on the historical pressure signal and the pulp density detection model;
in this embodiment, since the second target parameter estimation equation includes the pressure compensation coefficient and the unknown number corresponding to the concentration compensation amount, the second target parameter estimation equation may be derived, specifically, may be derived 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.
Specifically, K, K 1 is solved for causingMinimizing, willDividing into K, K 1 derivatives to obtain a third derivativeFourth derivative
After the third derivative corresponding to the pressure compensation coefficient and the fourth derivative corresponding to the concentration compensation quantity are obtained, the pressure compensation coefficient and the concentration compensation quantity corresponding to zero of the third derivative and the fourth derivative can be obtained according to the processed historical pressure signal and the pulp historical concentration assay value.
Specifically, let the above formula be zero, the solutions that can be obtained for K, K 1 are respectively: And Wherein, Is the mean value of the historical pressure signal P i'. And then the pressure compensation coefficient and the concentration compensation quantity can be obtained.
Step 210, substituting the pressure compensation coefficient, the concentration compensation amount and the pulp density detection model into the initial concentration detection model to obtain a pulp concentration detection model;
In this embodiment, after the pressure compensation coefficient and the concentration compensation amount are obtained, the pressure compensation coefficient, the concentration compensation amount, and the pulp density detection model obtained as described above may be substituted into the initial concentration detection model, and then the corresponding pulp density detection model may be obtained.
Step 211, obtaining a pulp concentration detection value based on the collected target differential pressure original signal, the target pressure original signal and the pulp concentration detection model.
In the embodiment, after the pulp concentration detection model is obtained, the pulp concentration detection value can be obtained through real-time detection by calculating the pulp concentration detection model on the basis of the target differential pressure original signal and the target pressure original signal acquired by the sensor. The target differential pressure original signal and the target pressure original signal acquired by the sensor can be processed by the Laida criterion, the first-order inertial filtering and the like, and then input into the ore pulp concentration detection model to obtain an ore pulp concentration detection value.
In an embodiment of the present application, optionally, after the "obtaining the pulp concentration detection model" in step 210, the method further includes determining a first concentration assay value corresponding to a target sampling time, determining a plurality of second concentration assay values from the historical concentration assay values based on the target sampling time, determining a plurality of target concentration detection values corresponding to the first concentration assay value and the second concentration assay value, and determining a target deviation value based on the first concentration assay value, the second concentration assay value and the plurality of target concentration detection values, and determining a differential pressure compensation update coefficient, a density compensation update amount, a pressure compensation update coefficient, and a concentration compensation update amount of the pulp concentration detection model when the target deviation value is greater than a preset deviation threshold, and updating the pulp concentration detection model based on the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient, and the concentration compensation update amount.
In this embodiment, after determining the pulp concentration detection model, a subsequent operator may also sample the pulp at a target sampling time interval and test the samples to update the pulp concentration detection model according to the test value. Here, the time of each time the worker samples the pulp may be taken as a target sampling time, and a first concentration assay value, that is, a concentration assay value obtained after the pulp sampling at the target sampling time is assayed, may be correspondingly determined at each target sampling time. After determining the first concentration assay value, a plurality of second concentration assay values may also be determined from the historical concentration assay values based on the target sampling instant. Next, a target concentration detection value corresponding to the first concentration assay value and a plurality of target concentration detection values corresponding to the plurality of second concentration assay values may be determined, where the target concentration detection value is a concentration value calculated by the pulp concentration detection model at the same time as the concentration assay value. Then, a target deviation value is calculated by the first concentration assay value, the second concentration assay value, and a plurality of corresponding target concentration detection values. For example, the cumulative sliding time window width corresponds to (M+1) concentration assay valuesWherein c α (T) may be a first concentration assay value and c α(T-1)……cα (T-M) may be a plurality of second concentration assay values, while introducing a laida criterion (3σ) error correction mechanism to exclude the effects of outlier assay values. When the concentration is assayedAnd the target concentration detection valueWhen the target deviation value theta (T) exceeds the preset deviation threshold value delta, namely theta (T) > delta, the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount can be determined, and the ore pulp concentration detection model is updated through the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount, namely the model parameter is corrected, and the correction can be realized through a parameter in-situ/remote correction mode, wherein the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount are update parameters of the ore pulp concentration detection model. Here, θ (T) may be: According to the embodiment of the application, the target deviation value is determined based on the first concentration assay value corresponding to the target sampling moment, and when the target deviation value is larger than the preset deviation threshold value, the ore pulp concentration detection model is updated at any time, so that the accuracy of the ore pulp concentration detection model is higher. In this embodiment, as the artificial concentration assay value increases, the model parameters in the slurry concentration detection model may be corrected in an on-site/remote manner. When the target deviation value is smaller than or equal to the preset deviation threshold value, the accuracy of the ore pulp concentration detection model is higher, and the ore pulp concentration detection model can be kept unchanged and used continuously.
In the embodiment of the application, optionally, the step of determining the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount of the pulp concentration detection model comprises the steps of determining a first differential pressure original signal, a first pressure original signal and a second differential pressure original signal and a second pressure original signal which correspond to the first concentration test value, and determining the differential pressure 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 differential pressure original signal, the first pressure original signal, the historical concentration test value, the second differential pressure original signal and the second pressure original signal.
In this embodiment, when the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient, and the density compensation update amount are determined, the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, and the density compensation amount may be similarly determined by the above-described method. Specifically, the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient, and the density compensation update amount may be determined based on the first concentration assay value, the first differential pressure original signal corresponding to the first concentration assay value, the first pressure original signal, each of the historical concentration assay values, the second differential pressure original signal corresponding to each of the historical concentration assay values, and the second pressure original signal. And then corresponding parameters in the ore pulp concentration detection model can be updated to obtain an updated ore pulp concentration detection model.
Fig. 2 is a schematic diagram showing an execution flow of a pulp concentration detection method according to an embodiment of the present application, and as shown in fig. 2, the flow may be described as follows:
The method comprises the steps of obtaining a differential pressure signal delta P (k) collected by a differential pressure sensor and a pressure signal P (k) collected by the pressure sensor, sending the differential pressure signal and the pressure signal to a data collection and communication module, sending the differential pressure signal and the pressure signal to a data storage module by the data collection and communication module, storing the differential pressure signal and the pressure signal by the data storage module, carrying out Laida criterion processing, first-order inertia filtering processing, inputting order according to the differential pressure signal and inputting order of the pressure signal and the like by the data processing module, outputting delta P '(k) and P' (k), finally determining model parameters (differential pressure compensation coefficient, density compensation quantity, pressure compensation coefficient and concentration compensation quantity) of a pulp concentration detection model by a parameter identification model according to delta P '(k) and P' (k), substituting the obtained model parameters into an initial concentration detection model, and obtaining an intelligent concentration detection value according to the differential pressure signal, the pressure signal and the pulp concentration detection model collected by the sensor.
In addition, after the ore pulp concentration detection model is obtained, the ore pulp concentration detection model can be updated in real time later, and particularly, one of the two modes of local and remote can be selected for updating the model parameters. For example, when model parameters are updated, the differential pressure signal Δp (k) collected by the differential pressure sensor, the pressure signal P (k) collected by the pressure sensor, and the artificial concentration assay value c α (T) may be sent to the remote calibration module, and then the remote calibration module may implement remote automatic calibration of the model parameters, and send the new model parameters to the identification model, so as to obtain an updated pulp concentration detection model.
The ore pulp concentration detection method provided by the embodiment of the application has higher detection precision. And comparing the detection effect with the instrument effect by introducing an evaluation index. Root mean square error is the expected value of the 2 nd root of the square of the difference between the intelligent detection value (or meter value) and the assay value at the corresponding assay time. The qualification rate index |P| is the percentage of the number of samples in the total number of samples within +/-2% (|P| < 2%) and +/-1% (|P| < 1%) of the absolute error between the intelligent detection value (or instrument value) and the concentration assay value at the corresponding assay time. The rising trend accuracy is that the number of the rising trend of the intelligent detection value (or the instrument value) at the corresponding test moment is the same as the rising trend of the test value, and the number is the percentage of the rising number of the test value. The accuracy of the descending trend is that the number of the intelligent detection value (or instrument value) descending trend at the corresponding test moment is the same as the descending trend of the test value, and the number of the intelligent detection value (or instrument value) descending trend is the percentage of the descending number of the test value. As can be seen from fig. 3, through long-term observation of actual concentration operation, the root mean square error of the pulp concentration detection value of the application is 0.8268, the qualification rate |p| <2% is up to 97.09%, the |p| <1% is up to 76.16%, the accuracy of the intelligent detection rising trend is 81.04%, and the accuracy of the intelligent detection falling trend is 77.92%. The ore pulp concentration detection method has lower calculation force requirement, can realize accurate and quick online detection, is more suitable for being deployed in site environment, and provides a basis for realizing operation optimization control of the ore dressing production process.
Further, as a specific implementation of the method of fig. 1, an embodiment of the present application provides a device for detecting a pulp concentration, as shown in fig. 4, where the device includes:
The acquisition module is used for acquiring a historical pressure difference signal, a historical pressure signal and a pulp historical concentration assay value corresponding to the historical sampling moment;
The numerical value determining module is used for determining a differential pressure compensation coefficient, a density compensation quantity, a pressure compensation coefficient and a concentration compensation quantity based on the historical differential pressure signal, the historical pressure signal and the pulp historical concentration test value;
the model determining module is used for obtaining an ore pulp concentration detection model according to the differential pressure compensation coefficient, the density compensation quantity, the pressure compensation coefficient, the concentration compensation quantity and the initial concentration detection model;
The detection value determining module is used for obtaining a pulp concentration detection value based on the collected target differential pressure original signal, the target pressure original signal and the pulp concentration detection model.
Optionally, the numerical value determining module includes:
the first equation construction unit is used for constructing a first target parameter estimation equation based on the initial density detection model;
the deriving unit is used for deriving the first target parameter estimation equation according to the differential pressure compensation coefficient and the density compensation quantity to obtain a first derivative and a second derivative, and determining the differential pressure compensation coefficient and the density compensation quantity corresponding to zero of the first derivative and the second derivative based on the historical differential pressure signal and the pulp historical concentration assay value;
the substituting unit is used for substituting the differential pressure compensation coefficient and the density compensation quantity into the initial density detection model to obtain an ore pulp density detection model;
The second equation construction unit is used for constructing a second target parameter estimation equation based on the initial concentration detection model, and the initial concentration detection model is determined based on the ore pulp density detection model;
The deriving unit is further configured to derive 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 determine, based on the historical pressure signal and the pulp density detection model, the pressure compensation coefficient and the concentration compensation amount corresponding to zero of the third derivative and the fourth derivative.
Optionally, the apparatus further comprises:
The order determining module is used for determining the differential pressure signal input order and the pressure signal input order before the first target parameter estimation equation is constructed based on the initial density detection model;
the first average module is configured to sequentially take the historical differential pressure signals corresponding to each historical sampling moment as target historical differential pressure signals, determine a first target number of matched differential pressure signals based on each target historical differential pressure signal, determine a differential pressure signal average value according to each target historical differential pressure signal and the corresponding matched differential pressure signal, and take the differential pressure signal average value as a processed historical differential pressure signal corresponding to the target historical differential pressure signal, where the first target number is obtained based on the differential pressure signal input order;
And the second average module is used for sequentially taking the historical pressure signals corresponding to each historical sampling moment as target historical pressure signals, determining a second target number of matched pressure signals based on each target historical pressure signal, determining a pressure signal average value according to each target historical pressure signal and the corresponding matched pressure signal, taking the pressure signal average value as a processed historical pressure signal corresponding to the target historical pressure signal, and obtaining the second target number based on the pressure signal input order.
Optionally, the model determining module is further configured to, after determining the pressure compensation coefficient and the concentration compensation amount corresponding to zero of the third derivative and the fourth derivative, substitute the pressure compensation coefficient, the concentration compensation amount, and the pulp density detection model into the initial concentration detection model to obtain a pulp density detection model.
Optionally, the apparatus further comprises:
The signal processing module is used for respectively carrying out signal filtering processing on the historical pressure difference original signal and the historical pressure original signal acquired by the sensor by utilizing the Laida criterion before the historical pressure difference signal, the historical pressure signal and the pulp historical concentration assay value corresponding to the historical sampling moment are acquired, and carrying out 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;
The detection value determining module is further configured to determine the historical sampling time, and determine the pulp historical concentration assay value, the historical pressure difference signal and the historical pressure signal corresponding to the historical sampling time, so as to obtain the historical pressure difference signal, the historical pressure signal and the pulp historical concentration assay value corresponding to each historical sampling time.
Optionally, the apparatus further comprises:
The test value determining module is used for determining a first concentration test value corresponding to a target sampling moment after the ore pulp concentration detection model is obtained, and determining a plurality of second concentration test values from the historical concentration test values based on the target sampling moment;
A bias value determining module configured to determine a plurality of target concentration detection values corresponding to the first concentration assay value and the second concentration assay value, and determine a target bias value based on the first concentration assay value, the second concentration assay value, and the plurality of target concentration detection values;
And the model updating module is used for determining a differential pressure compensation updating coefficient, a density compensation updating amount, a pressure compensation updating coefficient and a concentration compensation updating amount of the ore pulp concentration detection model when the target deviation value is larger than a preset deviation threshold value, and updating the ore pulp concentration detection model based on the differential pressure compensation updating coefficient, the density compensation updating amount, the pressure compensation updating coefficient and the concentration compensation updating amount.
Optionally, the model updating module is configured to:
Determining a first differential pressure original signal, a first pressure original signal and a second differential pressure original signal corresponding to each of the historical concentration assay values, and determining the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount based on the first concentration assay value, the first differential pressure original signal, the first pressure original signal, the historical concentration assay value, the second differential pressure original signal and the second pressure original signal.
It should be noted that, other corresponding descriptions of each functional unit related to the pulp concentration detection apparatus provided by the embodiment of the present application may refer to corresponding descriptions in the method of fig. 1, and are not repeated herein.
Further optionally, in the embodiment of the present application, as shown in fig. 5, the industrial application hardware platform of the pulp concentration detection model may include a differential pressure sensor, a pressure sensor, an instrument box, a signal isolator, a logic controller (PLC), a switch, a touch screen, a communication device, and the like. The probe 1 and the probe 2 are differential pressure probes, the probe 3 is a pressure probe, and the mounting positions of the probe 2 and the probe 3 can be changed according to actual production conditions. The signal isolator, the PLC, the switch, the touch screen and the communication module are arranged in the instrument box. The intelligent pulp concentration detection system comprises a PLC, a touch screen, a communication module, a switch, a touch screen and a remote model, wherein the PLC is used for realizing intelligent real-time detection of pulp concentration based on multiple input information, the touch screen is used for realizing functions of on-site display of pulp concentration detection values, parameter setting, on-site parameter updating, on-site/remote parameter correction mode switching and the like, and the communication module is used for realizing reading of data and writing of remote model updating parameters, namely remote parameter correction of a pulp concentration detection model based on the switch and the PLC. Meanwhile, the PLC is connected with the site DCS, so that real-time display and data management of the pulp concentration detection value are realized.
The industrial sensor (differential pressure sensor, pressure sensor) is connected with the transmitter through a pressure guiding pipe, the transmitter is connected with an isolator in the instrument box through a hard wire, the isolator transmits signals into the PLC to realize communication from the transmitter to the PLC, the touch screen and the communication module in the concentration meter box are positioned in the same local area network, mutual data transmission is realized through a TCP/IP protocol, the PLC and the site DCS are communicated through the hard wire, and the remote correction module is communicated with the site instrument box through a wireless network to realize 'cloud' on data and test results in the industrial site and realize centralized management of the data.
Alternatively, in the embodiment of the present application, as shown in fig. 6, the industrial application software platform of the pulp concentration detection model may include two parts of software front-end man-machine interaction and background function implementation. Specifically, a front-end man-machine interaction system can be built by adopting WinCC Flexible Smart application software platforms, and an intelligent ore pulp concentration detection and remote correction system for data acquisition and communication and multi-input information can be built by adopting STEP 7-MicroWIN SMART and Python software platforms. The front-end human-computer interaction interface (touch screen) is developed based on WinCC Flexible Smart application software platform to realize human-computer interaction, the background PLC is edited by STEP 7-MicroWIN SMART software platform to realize intelligent detection of ore pulp concentration based on multiple input information, the remote parameter correction function is developed by Python software platform, and the software platform is communicated with the PLC and the touch screen through a communication module to read key data and write model update parameters.
The background software mainly comprises application modules of system communication, data acquisition and storage, data processing, intelligent detection and the like, and realizes the intelligent detection technology of the ore pulp concentration of multi-input information and the remote parameter correction function.
Based on the method shown in fig. 1, correspondingly, the embodiment of the application also provides a storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the method for detecting the concentration of the ore pulp shown in fig. 1.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and includes several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective implementation scenario of the present application.
Based on the method shown in fig. 1 and the virtual device embodiment shown in fig. 4, in order to achieve the above objective, an embodiment of the present application further provides a computer device, which may specifically be a personal computer, a server, a network device, etc., where the computer device includes a storage medium and a processor, where the storage medium is used to store a computer program, and where the processor is used to execute the computer program to implement the pulp concentration detection method shown in fig. 1.
Optionally, the computer device may also include a user interface, a network interface, a camera, radio Frequency (RF) circuitry, sensors, audio circuitry, WI-FI modules, and the like. The user interface may include a Display screen (Display), an input unit such as a Keyboard (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 (e.g., bluetooth interface, WI-FI interface), etc.
It will be appreciated by those skilled in the art that the architecture of a computer device provided in the present embodiment is not limited to the computer device, and may include more or fewer components, or may combine certain components, or may be arranged in different components.
The storage medium may also include an operating system, a network communication module. An operating system is a program that manages and saves computer device hardware and software resources, supporting the execution of information handling programs and other software and/or programs. The network communication module is used for realizing communication among all components in the storage medium and communication with other hardware and software in the entity equipment.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general hardware platforms, or may be implemented by hardware. Through the technical scheme of the embodiment, firstly, a historical pressure difference signal, a historical pressure signal and an ore pulp historical concentration test value corresponding to the historical sampling moment can be obtained. Further, the differential pressure 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 differential pressure signal, the historical pressure signal and the pulp historical concentration test value corresponding to each historical sampling time. After the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient and the concentration compensation amount are obtained, the ore pulp concentration detection model can be obtained based on the differential pressure compensation coefficient, the density compensation amount, the pressure compensation coefficient, the concentration compensation amount and the initial concentration detection model. After the ore pulp concentration detection model is obtained, the ore pulp concentration detection value can be obtained by calculating the ore pulp concentration detection model according to the target pressure difference original signal and the target pressure original signal acquired by the sensor. According to the embodiment of the application, the pulp concentration detection value can be calculated according to the target differential pressure original signal and the target pressure original signal acquired by the sensor by determining the pulp concentration detection model, and the pulp concentration can be rapidly and accurately detected in real time while various influencing factors are considered.
Those skilled in the art will appreciate that the drawing is merely a schematic illustration of a preferred implementation scenario and that the modules or flows in the drawing are not necessarily required to practice the application. Those skilled in the art will appreciate that modules in an apparatus in an implementation scenario may be distributed in an apparatus in an implementation scenario according to an implementation scenario description, or that corresponding changes may be located in one or more apparatuses different from the implementation scenario. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario. The foregoing disclosure is merely illustrative of some embodiments of the application, and the application is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the application.

Claims (9)

1. A method for pulp concentration detection comprising:
Acquiring a historical pressure difference signal, a historical pressure signal and a pulp historical concentration assay value corresponding to the historical sampling moment;
determining a differential pressure compensation coefficient, a density compensation amount, a pressure compensation coefficient and a concentration compensation amount based on the historical differential pressure signal, the historical pressure signal and the pulp historical concentration assay value;
obtaining an ore pulp concentration detection model according to the differential pressure compensation coefficient, the density compensation quantity, the pressure compensation coefficient, the concentration compensation quantity and the initial concentration detection model, wherein parameters of the ore pulp concentration detection model are identified based on any system identification method;
Acquiring an ore pulp concentration detection value based on the acquired target differential pressure original signal, the target pressure original signal and the ore pulp concentration detection model;
The method for identifying any system comprises a least square system identification method, wherein when the least square system identification method is adopted, the method for determining the differential pressure compensation coefficient, the density compensation quantity, the pressure compensation coefficient and the concentration compensation quantity based on the historical differential pressure signal, the historical pressure signal and the pulp historical concentration assay value comprises the following steps:
constructing a first target parameter estimation equation based on the initial density detection model;
The first target parameter estimation equation is respectively derived according to the differential pressure compensation coefficient and the density compensation quantity to obtain a first derivative and a second derivative, and the differential pressure compensation coefficient and the density compensation quantity corresponding to zero of the first derivative and the second derivative are determined based on the historical differential pressure signal and the pulp historical concentration assay value;
Substituting the differential pressure compensation coefficient and the density compensation quantity into the initial density detection model to obtain an ore pulp density detection model;
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 pulp density detection model;
The second target parameter estimation equation is respectively derived according to the pressure compensation coefficient and the concentration compensation quantity to obtain a third derivative and a fourth derivative, and the pressure compensation coefficient and the concentration compensation quantity corresponding to zero of the third derivative and the fourth derivative are determined based on the historical pressure signal and the pulp density detection model;
the initial density detection model is as follows:
;
;
Wherein, For the processed historical differential pressure signal, g is the gravitational acceleration, deltah is the installation height of the differential pressure transmitter, B is the differential pressure compensation coefficient, B 1 is the density compensation quantity, and n 2 is the first target quantity determined based on the input order of the differential pressure signal;
The initial concentration detection model is as follows:
;
;
Wherein, Is the true density of the raw ore, the natural ore is processed into the high-density raw ore,For the processed historical pressure signal, K is the pressure compensation coefficient, K1 concentration compensation amount, n 1 is the second target number determined based on the pressure signal input order.
2. The method of claim 1, wherein prior to constructing the first target parameter estimation equation based on the initial density detection model, the method further comprises:
determining a differential pressure signal input order and a pressure signal input order;
Sequentially taking the history pressure difference signals corresponding to each history sampling moment as target history pressure difference signals, respectively determining a first target number of matched pressure difference signals based on each target history pressure difference signal, determining a pressure difference signal average value according to each target history pressure difference signal and the corresponding matched pressure difference signal, taking the pressure difference signal average value as a processed history pressure difference signal corresponding to the target history pressure difference signal, wherein the first target number is obtained based on the pressure difference signal input order;
Sequentially taking the historical pressure signals corresponding to the historical sampling moments as target historical pressure signals, respectively determining a second target number of matched pressure signals based on each target historical pressure signal, determining a pressure signal average value according to each target historical pressure signal and the corresponding matched pressure signal, taking the pressure signal average value as a processed historical pressure signal corresponding to the target historical pressure signal, and obtaining the second target number based on the pressure signal input order.
3. The method of claim 1, wherein after the determining the pressure compensation coefficient and the concentration compensation amount corresponding to the third derivative and the fourth derivative being zero, the method further comprises:
substituting the pressure compensation coefficient, the concentration compensation quantity and the pulp density detection model into the initial concentration detection model to obtain a pulp concentration detection model.
4. The method of claim 1, wherein prior to the acquiring the historical differential pressure signal, the historical pressure signal, and the slurry historical concentration assay value corresponding to the historical sampling time, the method further comprises:
Respectively carrying out signal filtering treatment on the historical pressure difference original signal and the historical pressure original signal acquired by the sensor by utilizing the Laida criterion, and carrying out first-order inertial filtering treatment 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;
and determining the historical sampling moment, and determining the pulp historical concentration assay value, the historical pressure difference signal and the historical pressure signal corresponding to the historical sampling moment to obtain the historical pressure difference signal, the historical pressure signal and the pulp historical concentration assay value corresponding to each historical sampling moment.
5. The method of claim 1, wherein after the obtaining the pulp concentration detection model, the method further comprises:
Determining a first concentration assay value corresponding to a target sampling moment, and determining a plurality of second concentration assay values from the historical concentration assay values based on the target sampling moment;
Determining a plurality of target concentration detection values corresponding to the first concentration assay value and the second concentration assay value, and determining a target deviation value based on the first concentration assay value, the second concentration assay value, and the plurality of target concentration detection values;
when the target deviation value is larger than a preset deviation threshold value, determining a differential pressure compensation update coefficient, a density compensation update amount, a pressure compensation update coefficient and a concentration compensation update amount of the ore pulp concentration detection model, and updating the ore pulp concentration detection model based on the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient and the concentration compensation update amount.
6. The method of claim 5, wherein determining the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient, and the concentration compensation update amount of the pulp concentration detection model comprises:
Determining a first differential pressure raw signal and a first pressure raw signal corresponding to the first concentration assay value, and a second differential pressure raw signal and a second pressure raw signal corresponding to each historical concentration assay value;
Determining the differential pressure compensation update coefficient, the density compensation update amount, the pressure compensation update coefficient, and the concentration compensation update amount based on the first concentration assay value, the first differential pressure raw signal, the first pressure raw signal, and the historical concentration assay value, the second differential pressure raw signal, and the second pressure raw signal.
7. A pulp concentration detection apparatus, comprising:
The acquisition module is used for acquiring a historical pressure difference signal, a historical pressure signal and a pulp historical concentration assay value corresponding to the historical sampling moment;
The numerical value determining module is used for determining a differential pressure compensation coefficient, a density compensation quantity, a pressure compensation coefficient and a concentration compensation quantity based on the historical differential pressure signal, the historical pressure signal and the pulp historical concentration test value;
The model determining module is used for obtaining an ore pulp concentration detection model according to the differential pressure compensation coefficient, the density compensation quantity, the pressure compensation coefficient, the concentration compensation quantity and the initial concentration detection model, wherein parameters of the ore pulp concentration detection model are identified based on any system identification method;
the detection value determining module is used for obtaining a pulp concentration detection value based on the collected target differential pressure original signal, the target pressure original signal and the pulp concentration detection model;
The method for identifying any system comprises a least square system identification method, and when the least square system identification method is adopted, the numerical value determining module comprises the following steps:
the first equation construction unit is used for constructing a first target parameter estimation equation based on the initial density detection model;
the deriving unit is used for deriving the first target parameter estimation equation according to the differential pressure compensation coefficient and the density compensation quantity to obtain a first derivative and a second derivative, and determining the differential pressure compensation coefficient and the density compensation quantity corresponding to zero of the first derivative and the second derivative based on the historical differential pressure signal and the pulp historical concentration assay value;
the substituting unit is used for substituting the differential pressure compensation coefficient and the density compensation quantity into the initial density detection model to obtain an ore pulp density detection model;
The second equation construction unit is used for constructing a second target parameter estimation equation based on the initial concentration detection model, and the initial concentration detection model is determined based on the ore pulp density detection model;
The deriving unit is further configured to derive 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 determine, based on the historical pressure signal and the pulp density detection model, the pressure compensation coefficient and the concentration compensation amount corresponding to zero of the third derivative and the fourth derivative;
the initial density detection model is as follows:
;
;
Wherein, For the processed historical differential pressure signal, g is the gravitational acceleration, deltah is the installation height of the differential pressure transmitter, B is the differential pressure compensation coefficient, B 1 is the density compensation quantity, and n 2 is the first target quantity determined based on the input order of the differential pressure signal;
The initial concentration detection model is as follows:
;
;
Wherein, Is the true density of the raw ore, the natural ore is processed into the high-density raw ore,For the processed historical pressure signal, K is the pressure compensation coefficient, K1 concentration compensation amount, n 1 is the second target number determined based on the pressure signal input order.
8. A storage medium having stored thereon a computer program, which when executed by a processor, implements the method of any of claims 1 to 6.
9. A computer device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, characterized in that the processor implements the method of any one of claims 1 to 6 when executing the computer program.
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