A method for measuring automatic Baume degree in seawater brine production

Abstract

The present invention belongs to the technical field of seawater salt making industry, and discloses a method for automatically measuring the Baume degree of seawater brine making, including: obtaining environmental information, including real-time wind speed and wind direction; obtaining relevant information of the brine making pool, including the length, width, height and basic water level of the brine making pool; establishing coordinates along the length, width and height of the brine making pool, and dividing the brine making pool into a number of virtual points according to the preset division accuracy of each coordinate; determining the lowest water level point of the brine making pool under the current environment according to the wind speed, wind direction and basic water level, and selecting a number of virtual points with different coordinate values ​​and located below the lowest water level point as measurement points under each coordinate; controlling an underwater mobile robot equipped with a Baume meter to automatically measure the Baume degree value at each measurement point; and obtaining the final Baume degree value by a regression fitting method according to the measured Baume degree value. The present method proposes an effective measurement scheme and solves the problems of low efficiency and low safety of traditional methods.

Description

Technical Field

[0001] The invention belongs to the technical field of seawater salt production industry, and specifically relates to an automatic Baume degree measurement method for seawater brine production. Background Art

[0002] The sea salt production process includes five parts: tidal intake, brine preparation, crystallization, salt collection, and transportation. The traditional sea salt production operation area is large, with many equipments, scattered distribution, low production efficiency, poor working environment, and shortage of technical personnel. Various data in the sea salt production process are mainly collected, uploaded, and analyzed manually, which not only has low work efficiency, but also is not convenient for data preservation and production research and judgment. At the same time, a large number of outdoor operations require workers to face extreme weather such as heat, strong winds, heavy rain, and blizzards, and there are many dangerous factors.

[0003] In recent years, with the accelerated innovation of computer Internet and other technical equipment, the social production mode has changed dramatically, and intelligent manufacturing has penetrated into various traditional production activities, giving new vitality to traditional industries. Modern technologies such as automation, Internet of Things, and data analysis have changed the production and operation mode of traditional manufacturing, and the integration of traditional industries and information technology has become the mainstream of development. The addition of information technology makes data processing more scientific, accurate, and visual. However, for the measurement of Baume degrees, there are still many problems in the integration of traditional industries and information technology, especially the problem of insufficient accuracy. Summary of the invention

[0004] The purpose of the present invention is to propose an automatic Baume degree measurement method for seawater brine production in view of the shortcomings of the current technology. The traditional Baume degree measurement method is to obtain the Baume degree value by manually reading the Baume meter, which is not only inefficient, but also short of manpower, and the safety of workers in harsh working environments cannot be effectively guaranteed. This method uses an underwater mobile robot to automatically measure the Baume degree, while considering the impact of harsh weather on the Baume degree measurement, proposes an effective measurement scheme, and solves the problems of low efficiency and low safety of the traditional method.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for measuring the automatic Baume degree of brine produced by seawater, comprising:

[0007] Step S1, obtaining environmental information, wherein the environmental information includes real-time wind speed and wind direction;

[0008] Step S2, obtaining information related to the brine pool, wherein the information related to the brine pool includes the length, width, height and basic water level of the brine pool;

[0009] Step S3, establishing coordinates along the length, width and height of the brine pool, and dividing the brine pool into a number of virtual points according to the preset division accuracy of each coordinate;

[0010] Step S4, determining the lowest water level point of the brine pool under the current environment according to the wind speed, wind direction and basic water level, and selecting several virtual points with different coordinate values ​​and located below the lowest water level point as measurement points at each coordinate;

[0011] Step S5, controlling the underwater mobile robot equipped with the Baume meter to automatically measure the Baume value at each measuring point;

[0012] Step S6: Obtain the final Baume value using a regression fitting method according to the measured Baume value.

[0013] Several optional methods are also provided below, but they are not intended to be additional limitations on the above-mentioned overall solution, but are merely further supplements or preferences. Under the premise that there are no technical or logical contradictions, each optional method can be combined with the above-mentioned overall solution separately, and multiple optional methods can also be combined.

[0014] Preferably, determining the lowest water level of the brine pool under the current environment according to the wind speed, wind direction and base water level comprises:

[0015] Step S41: define a concept of wind sum according to the real-time wind speed and wind direction within a preset time length, and the definition is as follows:

[0016]

[0017] Where V SUM =(v SUM ,ω SUM ) represents the total wind speed during time t, v SUM represents the total wind speed within time t, ω SUM represents the total wind direction within time t, t is the total action time of all directions of wind, n is the total number of winds in different directions within time t, i is the wind in the i-th direction, t i is the action time of the wind in the ith direction, V A_i =(v i ,ω i ) represents the state of the wind in the i-th direction, v i represents the wind speed in the i-th direction, ω i Indicates the wind direction of the i-th direction;

[0018] Step S42: During the wind action process, the water level difference ΔH generated by the total wind is calculated. The calculation method is as follows:

[0019]

[0020] Where g is the acceleration due to gravity;

[0021] Step S43: Obtain the current water level difference ΔH in the brine pool now , ΔH now A positive value indicates that the current water level is higher than the base water level, ΔH now If it is negative, it means the current water level is lower than the base water level, then calculate the first water level H ′ for:

[0022] H′=H+ΔH now -ΔH

[0023] Step S44, after considering the change in water level difference caused by inertia, the second water level H″ is calculated as follows:

[0024]

[0025] In the formula, ΔH peak is the maximum water level difference that affects the previous water level drop or water level rise, Δt peak is the time of the previous water increase and fall or water reduction and rise, Δt now The time when the current water level difference occurs;

[0026] Step S45, determining the lowest water level point is as follows:

[0027] The lowest water level = max(H″,H min )

[0028] Where, H is the basic water level, H min It is the minimum water level in the brine pool.

[0029] Preferably, the step of selecting a plurality of virtual points with different coordinate values ​​and located below the lowest water level point as measurement points at each coordinate includes:

[0030] First, a preset number of coordinate points are selected in the length and width directions of the brine pool;

[0031] Then, at each coordinate point, several different heights below the lowest water level point are selected, and the selected virtual points are determined as measurement points.

[0032] Preferably, obtaining the final Baume degree value by a regression fitting method according to the measured Baume degree value comprises:

[0033] Step S61: Take the point where the Baume value to be predicted is P(x p ,y p 、z p ), calculate the three-dimensional Euclidean distance from the predicted point P to each measured point:

[0034]

[0035] In the formula, d(d i ,d p ) represents the distance from the i-th measured point to the predicted point P, (x i ,y i 、z i ) is the coordinate of the i-th measurement point;

[0036] Step S62: copy multiple copies of the distance value at the origin of the XOY plane of the coordinate system established along the brine pool so that the total number of distances reaches a preset number, and i ,d p ) The distance values ​​are arranged from small to large, the first 50% of the distance values ​​after sorting are taken as the first batch of data and are assigned the first weight, the first 50%-80% of the distance values ​​after sorting are taken as the second batch of data and are assigned the second weight, and the last 20% of the distance values ​​after sorting are taken as the third batch of data and are assigned the third weight;

[0037] S63: Constructing Gauss-Markov model:

[0038] F1=Aβ+ε

[0039] F2=Aβ′+ε′

[0040] F3=Aβ″+ε″

[0041]

[0042]

[0043]

[0044] F p =ω1F1+ω2F2+ω3F3

[0045] Among them, F1, F2, and F3 are 3×1 matrices, which are the observed Baume value vectors. F1, F2, and F3 are the observed Baume value vectors obtained from the first, second, and third batches of data, respectively. x1 、f y1 、f z1 represents the fitting equation corresponding to the observed Baume values ​​obtained from the first batch of data as the X, Y, and Z coordinates change in the X, Y, and Z directions, respectively, f x2 、f y2 、f z2 represents the fitting equation corresponding to the observed Baume values ​​obtained from the second batch of data as the X, Y, and Z coordinates change in the X, Y, and Z directions, respectively, f x3 、f y3 、f z3represents the fitting equations corresponding to the observed Baume values ​​obtained from the third batch of data, which change with the changes of X, Y, and Z coordinates in the X, Y, and Z directions respectively; A is a 3×3 matrix containing the independent variables of the observed data, and (x, y, z) is the coordinate of the observed data; β, β′, β″ are 3×1 matrix of parameters to be fitted, and β0, …, β8 are parameters to be fitted; ε, ε′, ε″ are error term vectors; F p is the weighted observed Baume value vector, ω1, ω2, ω3 are the first weight, the second weight and the third weight respectively;

[0046] S64: Use the least squares method to find the estimates of β0, ..., β8 and calculate the sum of squares of deviations:

[0047]

[0048]

[0049]

[0050] In the formula, Q1, Q2, Q3 are the sum of squares of deviations, a i1 and a i2 is the element in matrix A, f j1 is the element in matrix F1, f j2 is the element in matrix F2, f j3 are the elements in matrix F3;

[0051] Use the least square method to find β0, β1, β2 to minimize Q1, use the least square method to find β3, β4, β5 to minimize Q2, and use the least square method to find β6, β7, β8 to minimize Q3;

[0052] S65: The (x p ,y p 、z p ) coordinates are substituted into the matrix A to solve the equation, and F1, F2, and F3 are obtained. Then, according to the weights ω1, ω2, and ω3, the weighted observed Baume value vector F is obtained. p , F p The average of the three elements of the matrix is ​​used to obtain the Baume value of the point P to be predicted.

[0053] The automatic Baume degree measurement method for seawater brine production provided by the present invention has the following beneficial effects compared with the prior art:

[0054] (1) Combine automation with the traditional salt-making industry in light of the actual brine pool operation requirements and harsh environmental conditions. Use a mobile robot in the water to efficiently and quickly obtain the Baume value of a specific point in the brine pool and send the data. At the same time, a measurement plan for bad weather is proposed, and a method for calculating the lowest water level under wind is given to make the measured data more reliable. This saves costs while improving operational safety.

[0055] (2) According to the specific requirements for measuring the Baume value of brine in the seawater salt production industry and the actual operating environment of the salt field, a method for automatically measuring the Baume value is given to replace manual work. The concise and powerful mathematical principle of the Gauss-Markov model is used to accurately capture the change pattern of the brine Baume value with the location of the brine production pool, so as to have a deeper understanding of the spatial distribution characteristics of the brine Baume value and provide a scientific basis for the production regulation of the salt field. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of an automatic Baume degree measurement method for seawater brine production according to the present invention;

[0057] Figure 2 It is a schematic diagram of the coordinate construction of the brine pool of the present invention;

[0058] Figure 3 This is a schematic diagram of the water level lowest point calculation model of the present invention;

[0059] Figure 4 This is a schematic diagram of the sampling of measurement points of the present invention;

[0060] Figure 5 It is a schematic structural diagram of the water surface mobile robot of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0063] The present embodiment provides an automatic Baume degree measurement method for seawater brine production. First, the salt pan is divided according to its actual area, and specific coordinates are assigned to each area. These coordinates are communicated to the underwater mobile robot so that the robot can accurately locate the measurement point. By using sensor equipment and meteorological station data, combined with historical data of the salt pan and the workers' operating experience, the current meteorological information is obtained, including important parameters such as wind direction, wind speed and water level. The underwater mobile robot starts from the automatic charging pile and arrives at the measurement point one by one. It measures the Baume degree value below the water level and saves the data for further processing. After arriving at the measurement point, the underwater mobile robot will measure the Baume degree value below the lowest point of the water level. After the measurement, the regression fitting method is used for fitting to obtain the Baume degree value at any position.

[0064] like Figure 1 As shown, this embodiment provides a method for measuring the automatic Baume degree of seawater brine production, comprising:

[0065] Step S1, obtaining environmental information, the environmental information including real-time wind speed v and wind direction ω.

[0066] Step S2, obtaining information related to the brine pool, including the length a, width b, height h and basic water level H of the brine pool, all in meters. The basic water level is usually understood as the distance from the bottom of the brine pool to the calm water surface when there are no waves on the water surface during this measurement.

[0067] Step S3, establishing coordinates along the length, width and height of the brine pool, and dividing the brine pool into a number of virtual points according to the preset division accuracy of each coordinate.

[0068] like Figure 2 As shown, the point in the upper left corner of the brine pool when viewed from a bird's-eye view is taken as the origin of the coordinate system, the X-axis is established along the long side of the brine pool, the Y-axis is established along the wide side of the brine pool, the Z-axis is established along the high side of the brine pool, and the brine pool is located on the positive half axis of the coordinate system.

[0069] In actual operation, the horizontal plane of a single brine pool is a rectangle with a size of about 40 meters * 60 meters, and a depth of 2 meters. Take the northwest vertex as the origin, extend eastward as the X direction, extend southward as the Y direction, and extend to the bottom of the brine pool as the Z direction. The X and Y directions are divided into coordinates with 1 meter as 1 unit, with a total of 60 units in the X direction and 40 units in the Y direction. The Z direction is divided into 1 decimeter as a unit, with a total of 20 units in the Z direction. The horizontal plane of the brine pool is divided into 48,000 cells to describe the position coordinates.

[0070] Step S4, determine the lowest water level point of the brine pool under the current environment according to the wind speed, wind direction and basic water level, and select several virtual points with different coordinate values ​​and located below the lowest water level point as measurement points at each coordinate.

[0071] Determine the lowest point of the water level: The change of water level is caused by the action of wind. Over a period of time, the wind from a certain direction continues to act, and the brine pool produces a water level difference. The water level difference is always a positive number, indicating the distance the water level deviates from the basic water level. The reason for the water level difference is the change of water potential energy. The square of the wind speed can reflect the size of the wind energy. Therefore, the water level difference is related to the square of the wind speed and the wind direction. When there is no wind, the water level difference is zero.

[0072] Due to the influence of gravity and inertia, after the water level difference is generated, there is a phenomenon of water level increase and then fall, and water level decrease and then rise, so the water level difference will not be infinitely large or small. The minimum water level value H should be determined based on historical data and brine pool observation data. min The minimum water level is understood as the distance from the lowest point of the water level to the bottom of the brine pool. The lowest point of the water level is the calculated water level H″ and the minimum water level H min The specific calculation process is as follows:

[0073] Step S41: Set the vector V according to the real-time wind speed and wind direction within a preset time length (e.g., within two hours). A (v,ω), define a concept of wind sum, the definition is as follows:

[0074]

[0075] Where V SUM =(v SUM ,ω SUM ) represents the total wind speed during time t, v SUM represents the total wind speed within time t, ω SUM represents the total wind direction within time t, t is the total action time of all directions of wind, n is the total number of winds in different directions within time t, i is the wind in the i-th direction, t i is the action time of the wind in the ith direction, V A_i =(v i ,ω i ) represents the state of the wind in the i-th direction, v i represents the wind speed in the i-th direction, ω i Indicates the wind direction of the i-th direction; the winds collected from all directions are vector-summed and the weight is calculated according to the duration of action.

[0076] Step S42: During the wind action process, Figure 3 The simplified model shown, g is the gravitational acceleration, and the water level difference ΔH caused by the sum of wind is calculated as follows:

[0077]

[0078] Step S43: Obtain the current water level difference ΔH in the brine pool now ,, in this embodiment, the underwater mobile robot can detect the water level difference ΔH of the current water level from the basic water level of the brine pool through ultrasonic wave. now , water level difference ΔH now Take the maximum value of the water level detected by ultrasound that deviates from the basic water level of the brine pool within 15 minutes. now A positive value indicates that the current water level is higher than the base water level, ΔH now If it is negative, it means the current water level is lower than the base water level. Then the first water level H′ is calculated as:

[0079] H′=H+ΔH now -ΔH

[0080] Step S44: Affected by gravity and inertia, the water level difference change caused by inertia needs to be taken into account. After considering the water level difference change caused by inertia, the second water level H″ is calculated as follows:

[0081]

[0082] In the formula, ΔH peak is the maximum water level difference that affects the previous water level drop or water level rise, Δt peak is the time of the previous water increase and fall or water reduction and rise, Δt now The time when the current water level difference occurs.

[0083] Step S45, determining the lowest water level point is as follows:

[0084] The lowest water level = max(H″,H min )

[0085] Where, H is the basic water level, H min The first water level H′ and the second water level H″ both represent the distance between the water surface and the bottom of the brine pool.

[0086] When selecting the measuring points, first select a preset number of coordinate points in the length and width direction of the brine pool; then, at each coordinate point, select several different heights below the lowest water level point, and determine the selected virtual points as measuring points. Figure 4As shown, 9 measuring points distributed on the horizontal plane of the brine pool can be taken as follows: (0, 0), (15, 0), (30, 0), (45, 0), (60, 0), (0, 10), (0, 20), (0, 30), (0, 40). After determining the X and Y axis coordinates of the measuring points, the Baume degree measurement is then performed below the lowest water level. Each measuring point is measured every 15 cm in the Z direction, for a total of 5 measurements, to obtain a coordinate-Baume degree corresponding data set. Figure 4 Among them, 1-9 represent nine Baume measurement points; 10 represents the underwater mobile robot; 11 represents the brine pool; 12 represents the automatic charging station.

[0087] Step S5, controlling the underwater mobile robot equipped with the Baume meter to automatically measure the Baume value at each measuring point.

[0088] This embodiment directly controls the underwater (including the surface and underwater) mobile robot to reach the measurement point to measure the Baume value. The structure or operation mode of the underwater mobile robot is not limited. Considering the height difference of the water surface, it is recommended to use an underwater mobile robot to measure the Baume value. The underwater mobile robot can determine the distance from the wall (side and bottom) of the brine pool through a distance sensor, so as to reach the measurement point at the specified position to measure the Baume value. At the same time, in order to reduce costs, this embodiment also provides a surface mobile robot such as Figure 5 shown.

[0089] The surface mobile robot comprises a vessel body, a control module 13, a driving motor 14, an ultrasonic level meter 15, a gyroscope 18, a Baume meter 16, a distance sensor and a connecting rod 17. The distance sensor is installed at the bottom of the vessel body, and the gyroscope is installed on the vessel body; the ultrasonic level meter is installed on the vessel body, and the measuring surface of the ultrasonic level meter faces the water surface; the housing of the driving motor is connected to the vessel body, and the transmission shaft of the driving motor is connected to the connecting rod, the connecting rod is arranged perpendicular to the vessel body, the Baume meter is installed at the bottom of the connecting rod, and the driving motor, the ultrasonic level meter, the gyroscope, the Baume meter, and the distance sensor are electrically connected to the control module.

[0090] The ultrasonic level meter 15 measures the distance to the water surface and feeds it back to the control module 13 of the surface mobile robot. The control module 13 determines the current water level difference ΔH according to the measured distance. now And calculate the lowest point of the water level (if it is an underwater mobile robot, the ultrasonic level meter 15 can be used to measure the distance to the water surface when the underwater mobile robot has not submerged in the water body), and output a control command to drive the motor to drive the connecting rod 17 equipped with the Baume meter 16 to move up and down, move the Baume meter 16 below the lowest point of the water level, and measure the Baume value.

[0091] The surface mobile robot can move freely in the brine pool and is equipped with a distance sensor, which can obtain the current position coordinates and the distance from the Baume meter to the bottom of the brine pool in real time. At the same time, the surface mobile robot is equipped with a gyroscope. When the wind is strong and the height difference of the water surface is large, causing the robot body to tilt, the tilt angle of the body can be detected and corrected in time to ensure that the robot can perform the measurement task normally.

[0092] Step S6: Obtain the final Baume value using a regression fitting method according to the measured Baume value.

[0093] From the measured coordinate-Baume value data set, the Gauss-Markov model fitting equation is used to find the function with the smallest error with the actual observed data. The 45 Baume values ​​measured are fitted with the X, Y and Z directions to show the Baume value changing with the coordinates. Among them, the Baume measurement value at the origin of the XOY plane is shared by the X, Y and Z directions.

[0094] Step S61: Take the point where the Baume value to be predicted is P(x p ,y p 、z p ), calculate the three-dimensional Euclidean distance from the predicted point P to each measured point:

[0095]

[0096] In the formula, d(d i ,d p ) represents the distance from the i-th measured point to the predicted point P, (x i ,y i 、z i ) is the coordinate of the ith measurement point.

[0097] Step S62: copy multiple copies of the distance value at the origin of the XOY plane of the coordinate system established along the brine pool so that the total number of distances reaches a preset number, and i ,d p ) The distance values ​​are arranged from small to large, the first 50% of the distance values ​​after sorting are taken as the first batch of data and assigned the first weight, the first 50%-80% of the distance values ​​after sorting are taken as the second batch of data and assigned the second weight, and the last 20% of the distance values ​​after sorting are taken as the third batch of data and assigned the third weight.

[0098] In this embodiment, Figure 4 As an example, the measurement points shown in the figure are 45 d(d i ,d p ) distance values ​​are arranged from small to large, d(d i ,d p) is smaller, the closer the measurement point is to the predicted point P, and the closer the measurement point is, the greater the weight will be given. Consider the five measurement points shared by the X and Y directions at the origin of the XOY plane, and divide the 45 d(d i ,d p ) distance values ​​are expanded to 50, of which the expanded 5 are copied from the data at the origin of the XOY plane. These 50 measurement points are divided into d(d i ,d p ) The first 25 are divided into the first batch, and the weight ω1=0.5 is assigned; the 26th to the 40th are divided into the second batch, and the weight ω2=0.3 is assigned; the 41st to the 50th are divided into the third batch, and the weight ω3=0.2 is assigned. This process means that the closer the measurement point is to the predicted point, the more it can reflect the Baume value of the predicted point, and the more important it should be to the result.

[0099] S63: Constructing Gauss-Markov model:

[0100] F1=Aβ+ε

[0101] F2=Aβ′+ε′

[0102] F3=Aβ″+ε″

[0103]

[0104]

[0105]

[0106] F p =ω1F1+ω2F2+ω3F3

[0107] Among them, F1, F2, and F3 are 3×1 matrices, which are the observed Baume value vectors. F1, F2, and F3 are the observed Baume value vectors obtained from the first, second, and third batches of data, respectively. x1 、f y1 、f z1 represents the fitting equation corresponding to the observed Baume values ​​obtained from the first batch of data as the X, Y, and Z coordinates change in the X, Y, and Z directions, respectively, f x2 、f y2 、f z2 represents the fitting equation corresponding to the observed Baume values ​​obtained from the second batch of data as the X, Y, and Z coordinates change in the X, Y, and Z directions, respectively, f x3 、f y3 、f z3represents the fitting equations corresponding to the observed Baume values ​​obtained from the third batch of data, which change with the changes of X, Y, and Z coordinates in the X, Y, and Z directions respectively; A is a 3×3 matrix containing the independent variables of the observed data, and (x, y, z) is the coordinate of the observed data; β, β′, β″ are 3×1 matrix of parameters to be fitted, and β0, …, β8 are parameters to be fitted; ε, ε′, ε″ are error term vectors; F p is the weighted observed Baume value vector, ω1, ω2, ω3 are the first weight, the second weight and the third weight respectively.

[0108] S64: Use the least squares method to find the estimates of β0, ..., β8 and calculate the sum of squares of deviations:

[0109]

[0110]

[0111]

[0112] In the formula, Q1, Q2, Q3 are the sum of squares of deviations, a i1 and a i2 is the element in matrix A, f j1 is the element in matrix F1, f j2 is the element in matrix F2, f j3 are the elements in matrix F3.

[0113] Use the least squares method to find β0, β1, and β2 to minimize Q1, use the least squares method to find β3, β4, and β5 to minimize Q2, and use the least squares method to find β6, β7, and β8 to minimize Q3.

[0114] S65: The (x p ,y p 、z p ) coordinates are substituted into the matrix A to solve the equation, and F1, F2, and F3 are obtained. Then, according to the weights ω1, ω2, and ω3, the weighted observed Baume value vector F is obtained. p , F p The numbers in the three elements of the matrix are averaged to obtain the Baume value of the predicted point P. The method of fitting the data and then calculating the Baume value of a specific position can not only reduce the error caused by the measurement, but also obtain the Baume value of any position.

[0115] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for measuring the automatic Baume degree of seawater brine production, characterized in that: The method for automatically measuring Baume degree in seawater brine production comprises: Step S1, obtaining environmental information, wherein the environmental information includes real-time wind speed and wind direction; Step S2, obtaining information related to the brine pool, wherein the information related to the brine pool includes the length, width, height and basic water level of the brine pool; Step S3, establishing coordinates along the length, width and height of the brine pool, and dividing the brine pool into a number of virtual points according to the preset division accuracy of each coordinate; Step S4, determining the lowest water level point of the brine pool under the current environment according to the wind speed, wind direction and basic water level, and selecting several virtual points with different coordinate values ​​and located below the lowest water level point as measurement points at each coordinate; Wherein, determining the lowest water level of the brine pool under the current environment according to the wind speed, wind direction and basic water level includes: Step S41: define a concept of wind sum according to the real-time wind speed and wind direction within a preset time length, and the definition is as follows: Where V SUM =(v SUM ,ω SUM ) represents the total wind speed during time t, v SUM represents the total wind speed within time t, ω SUM represents the total wind direction within time t, t is the total action time of all directions of wind, n is the total number of winds in different directions within time t, i is the wind in the i-th direction, t i is the action time of the wind in the ith direction, V A_i =(v i ,ω i ) represents the state of the wind in the i-th direction, v i represents the wind speed in the i-th direction, ω i Indicates the wind direction of the i-th direction; Step S42: During the wind action process, the water level difference ΔH generated by the total wind is calculated. The calculation method is as follows: Where g is the acceleration due to gravity; Step S43: Obtain the current water level difference ΔH in the brine pool now , ΔH now A positive value indicates that the current water level is higher than the base water level, ΔH now If it is negative, it means the current water level is lower than the base water level. Then the first water level H′ is calculated as: H′=H+ΔH now -ΔH Step S44, after considering the change in water level difference caused by inertia, the second water level H″′ is calculated as follows: In the formula, ΔH peak is the maximum water level difference that affects the previous water level drop or water level rise, Δt peak is the time of the previous water increase and fall or water reduction and rise, Δt now The time when the current water level difference occurs; Step S45, determining the lowest water level point is as follows: The lowest water level = max(H″, H min ) Where, H is the basic water level, H min is the minimum water level of the brine pool; The step of selecting a plurality of virtual points with different coordinate values ​​and located below the lowest water level point as measurement points at each coordinate includes: First, a preset number of coordinate points are selected in the length and width directions of the brine pool; Then, at each coordinate point, several different heights below the lowest water level point are selected, and the selected several virtual points are determined as measurement points; Step S5, controlling the underwater mobile robot equipped with the Baume meter to automatically measure the Baume value at each measuring point; Step S6, according to the measured Baume degree value, using a regression fitting method to obtain a final Baume degree value, including: Step S61: Take the point where the Baume value to be predicted is P(x p ,y p 、z p ), calculate the three-dimensional Euclidean distance from the predicted point P to each measured point: In the formula, d(d i , d p ) represents the distance from the i-th measured point to the predicted point P, (x i ,y i 、z i ) is the coordinate of the i-th measurement point; Step S62: copy multiple copies of the distance value at the origin of the XOY plane of the coordinate system established along the brine pool so that the total number of distances reaches a preset number, and i , d p ) The distance values ​​are arranged from small to large, the first 50% of the distance values ​​after sorting are taken as the first batch of data and are assigned the first weight, the first 50%-80% of the distance values ​​after sorting are taken as the second batch of data and are assigned the second weight, and the last 20% of the distance values ​​after sorting are taken as the third batch of data and are assigned the third weight; Step S63: construct a Gauss-Markov model: F1=Aβ+ε F2=Aβ′+ε′ F3=Aβ″+ε″ F p =ω1F1+ω2F2+ω3F3 Among them, F1, F2, and F3 are 3×1 matrices, which are the observed Baume value vectors. F1, F2, and F3 are the observed Baume value vectors obtained from the first, second, and third batches of data, respectively. x1 、f y1 、f z1 represents the fitting equation corresponding to the observed Baume values ​​obtained from the first batch of data as the X, Y, and Z coordinates change in the X, Y, and Z directions, respectively. x2 、f y2 、f z2 represents the fitting equation corresponding to the observed Baume values ​​obtained from the second batch of data as the X, Y, and Z coordinates change in the X, Y, and Z directions, respectively, f x3 、f y3 、f z3 represents the fitting equations corresponding to the observed Baume values ​​obtained from the third batch of data, which change with the changes of X, Y, and Z coordinates in the X, Y, and Z directions respectively; A is a 3×3 matrix containing the independent variables of the observed data, and (x, y, z) is the coordinate of the observed data; β, β′, β″ are 3×1 matrix of parameters to be fitted, and β0, …, β8 are parameters to be fitted; ε, ε′, ε″ are error term vectors; F p is the weighted observed Baume value vector, ω1, ω2, ω3 are the first weight, the second weight and the third weight respectively; Step S64, use the least square method to find the estimated values ​​of β0, ..., β8, and calculate the sum of squares of deviations: In the formula, Q1, Q2, Q3 are the sum of squares of deviations, a i1 and a i2 is the element in matrix A, f j1 is the element in matrix F1, f j2 is the element in matrix F2, f j3 are the elements in matrix F3; Use the least square method to find β0, β1, β2 to minimize Q1, use the least square method to find β3, β4, β5 to minimize Q2, and use the least square method to find β6, β7, β8 to minimize Q3; Step S65: Set the (x p ,y p 、z p ) coordinates are substituted into the matrix A to solve the equation, and F1, F2, and F3 are obtained. Then, according to the weights ω1, ω2, and ω3, the weighted observed Baume value vector F is obtained. p , F p The average of the three elements of the matrix is ​​used to obtain the Baume value of the point P to be predicted.

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