Fish and shrimp material puffing control method and device, electronic equipment and storage medium

Abstract

The invention discloses a fish and shrimp material puffing control method and device, electronic equipment and a storage medium, and relates to the field of feed production, and the method comprises the following steps: determining an evaluation score of an operation parameter based on a relative position of a current parameter value of the operation parameter in a corresponding historical data set; then determining a weight value of each operation parameter based on a historical data set; and performing weighted summation processing on the basis of the weight value of each operation parameter and the evaluation score to obtain an operation state score of the bulking machine. And when it is detected that the operating state score is lower than the preset threshold value, the current parameter value of the target adjustable parameter is modified based on the parameter average value of the target adjustable parameter, so that the current parameter value of the target adjustable parameter tends to the parameter average value, and the operating stability of the bulking machine is ensured. The evaluation of the bulking machine can be obtained based on the operation parameters of the bulking machine, and the adjustable parameters of the bulking machine can be adjusted based on the evaluation, so that the quality of feed can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of feed production, and in particular to a method, device, electronic device and storage medium for controlling the expansion of fish and shrimp feed. Background Art

[0002] Extruder is a commonly used equipment in the modern feed industry, usually used to produce feed, such as fish and shrimp feed, that is, feed for fish and shrimp. The extruder includes sub-equipment such as feeder, modulator, main machine, extruding chamber, cutter motor, etc. During the production process of the extruder, the operating parameters change with the operation of the extruder. In order to ensure the stability and efficiency of the production process, it is very important to accurately evaluate and monitor the state of the extruder. Therefore, it is of great practical significance to accurately evaluate the operating state of the extruder and control the operating parameters of the extruder. At present, the state evaluation of the extruder and the control of its operating parameters mainly rely on manual experience. This method has certain subjectivity and uncertainty, which easily leads to low quality of the produced feed. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a method, device, electronic device and storage medium for controlling the puffing of fish and shrimp feed, which can evaluate the puffing machine based on the operating parameters of the puffing machine, and adjust the adjustable parameters of the puffing machine based on the evaluation, thereby improving the quality of the feed.

[0004] According to the first aspect of the present application, the method for controlling the expansion of fish and shrimp feed includes:

[0005] Acquire current parameter values ​​of various operating parameters of the extruder, and acquire historical data sets of various operating parameters of the extruder; the operating parameters include adjustable parameters and non-adjustable parameters;

[0006] Determining an evaluation score of the operating parameter based on a relative position of the current parameter value of the operating parameter in the corresponding historical data set;

[0007] Determining a weight value of each of the operating parameters based on the historical data set;

[0008] Performing weighted sum processing based on the weight value of each operating parameter and the evaluation score to obtain the operating status score of the expander;

[0009] When it is detected that the running status score is lower than a preset threshold, an average value is calculated based on the weight values ​​of each of the adjustable parameters to obtain a weighted average value;

[0010] Based on the historical data set corresponding to the target adjustable parameter, a parameter average value of the target adjustable parameter is calculated; wherein the weight value of the target adjustable parameter is greater than the weight average value;

[0011] The current parameter value of the target adjustable parameter is modified based on the parameter average value of the target adjustable parameter.

[0012] According to the fish and shrimp feed puffing control method of the first embodiment of the present application, at least the following beneficial effects are achieved: based on the relative position of the current parameter value of the operating parameter in the corresponding historical data set, the evaluation score of the operating parameter is determined; then the weight value of each operating parameter is determined based on the historical data set; based on the weight value of each operating parameter and the evaluation score, a weighted summation process is performed to obtain the operating status score of the puffing machine. In this way, the operating status score of the puffing machine is automatically obtained. Compared with relying on manual experience, the present application obtains the score based on the weight value of each operating parameter and the evaluation score by weighted summation process, and is obtained based on adjustable parameters and non-adjustable parameters. Therefore, the score obtained by the present application is more comprehensive and more accurate. When it is detected that the operating status score is lower than the preset threshold, the current parameter value of the target adjustable parameter is modified based on the parameter average value of the target adjustable parameter, so that the current parameter value of the target adjustable parameter tends to the parameter average value, thereby ensuring the stability of the operation of the puffing machine. Therefore, the present application can obtain the evaluation of the puffing machine based on the operating parameters of the puffing machine, and adjust the adjustable parameters of the puffing machine based on the evaluation, thereby improving the quality of the feed.

[0013] According to some embodiments of the present application, the modifying the current parameter value of the target adjustable parameter based on the parameter average value of the target adjustable parameter includes:

[0014] Determining a difference between the current parameter value of the target adjustable parameter and the corresponding parameter average value;

[0015] If the current parameter value is lower than the corresponding parameter average value, then the current parameter value=the current parameter value+0.5*the difference;

[0016] If the current parameter value is higher than the corresponding parameter average value, then the current parameter value=the current parameter value-0.5*the difference.

[0017] According to some embodiments of the present application, determining a weight value of each of the operating parameters based on the historical data set includes:

[0018] Standardizing the historical data set of the operating parameters to obtain a standardized data set;

[0019] Constructing a covariance matrix based on the standardized data set of each operating parameter;

[0020] Performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues ​​and a plurality of eigenvectors, wherein the eigenvalues ​​correspond to the eigenvectors one-to-one;

[0021] Based on the eigenvalue and the eigenvector, a weight value of the operating parameter is obtained.

[0022] According to some embodiments of the present application, obtaining the weight value of the operating parameter based on the eigenvalue and the eigenvector includes:

[0023] Arrange the corresponding eigenvectors in descending order according to the size of the eigenvalues, and select the first P eigenvectors as target eigenvectors; where P is a preset value;

[0024] Calculate the sub-weight of the operating parameter in the i-th target feature vector, where 1≤i≤P;

[0025] The sub-weights of the operating parameter in the i-th to P-th target feature vectors are accumulated to obtain a weight value of the operating parameter.

[0026] According to some embodiments of the present application, determining the evaluation score of the operating parameter based on the relative position of the current parameter value of the operating parameter in the corresponding historical data set includes:

[0027] Calculating the average value of the historical data set and the standard deviation of the historical data set;

[0028] Performing detection processing on the current parameter value based on the average value of the historical data set and the standard deviation of the historical data set;

[0029] When it is detected that the current parameter value is greater than or equal to (μ xi -σ xi ), and less than or equal to (μ xi +σ xi ), based on the historical data set corresponding to the current parameter value, xi -σ xi , μ xi +σ xi ] elements to construct the reference data set; where μ xi represents the average value of the historical data set corresponding to the i-th operating parameter, σ xi Characterizes the standard deviation of the historical data set corresponding to the i-th operating parameter;

[0030] An evaluation score of the operating parameter corresponding to the current parameter value is determined based on the element with the largest value and the element with the smallest value in the reference data set.

[0031] According to some embodiments of the present application, after the detection processing of the current parameter value based on the average value of the historical data set and the standard deviation of the historical data set, the method further includes:

[0032] When it is detected that the current parameter value is less than (μ xi -σ xi ), the evaluation score of the operating parameter corresponding to the current parameter value is set to the lower limit of a preset range; the preset range is the value range of the evaluation score;

[0033] When it is detected that the current parameter value is greater than (μ xi +σ xi ), the evaluation score of the operating parameter corresponding to the current parameter value is set to the upper limit of the preset range; wherein, μ xi represents the average value of the historical data set corresponding to the i-th operating parameter, σ xi Characterizes the standard deviation of the historical data set corresponding to the i-th operating parameter.

[0034] According to some embodiments of the present application, determining the evaluation score of the operating parameter corresponding to the current parameter value based on the element with the largest value and the element with the smallest value in the reference data set includes:

[0035] The evaluation score is obtained through the assignment calculation formula, which is:

[0036]

[0037] Among them, value i Characterizes the evaluation score of the i-th operating parameter, x i Characterizing the current parameter value of the i-th operating parameter; X imin Characterizes the element with the smallest value in the reference data set; X imax Characterizes the element with the largest value in the reference data set.

[0038] The second aspect of the present application provides a device for controlling the expansion of fish and shrimp feed, comprising:

[0039] An acquisition module, used for acquiring current parameter values ​​of various operating parameters of the extruder, and acquiring historical data sets of various operating parameters of the extruder; the operating parameters include adjustable parameters and non-adjustable parameters;

[0040] A scoring determination module, configured to determine an evaluation score of the operating parameter based on a relative position of the current parameter value of the operating parameter in the corresponding historical data set;

[0041] A weight determination module, used to determine a weight value of each of the operating parameters based on the historical data set;

[0042] A summing module, used for performing weighted summing processing based on the weight value and the evaluation score of each operating parameter to obtain the operating status score of the extruder;

[0043] A first calculation module, configured to calculate an average value based on the weight values ​​of each of the adjustable parameters to obtain a weighted average value when it is detected that the running status score is lower than a preset threshold;

[0044] A second calculation module is used to calculate the parameter average value of the target adjustable parameter based on the historical data set corresponding to the target adjustable parameter; wherein the weight value of the target adjustable parameter is greater than the weight average value;

[0045] A modification module is used to modify the current parameter value of the target adjustable parameter based on the parameter average value of the target adjustable parameter.

[0046] The third aspect of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for controlling the puffing of fish and shrimp feeds as described in any one of the first aspect embodiments is implemented.

[0047] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, it implements the fish and shrimp feed puffing control method described in any one of the first aspect embodiments of the present application.

[0048] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present application is further described below with reference to the accompanying drawings and embodiments, wherein:

[0050] Figure 1 This is a schematic flow chart of the steps of the method for controlling the expansion of fish and shrimp feed according to an embodiment of the present application;

[0051] Figure 2 This is a schematic diagram of the structure of the fish and shrimp feed puffing control device according to an embodiment of the present application;

[0052] Figure 3 A schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0054] In the description of the present application, it should be understood that descriptions involving orientation, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0055] In the description of this application, "several" means more than one, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0056] In the description of this application, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application based on the specific content of the technical solution.

[0057] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0058] First, some nouns involved in this application are analyzed:

[0059] Principal Component Analysis (PCA) is a commonly used data analysis technique that aims to transform multiple indicators into a few comprehensive indicators using the idea of ​​dimensionality reduction. PCA is a linear transformation that transforms data into a new coordinate system so that the first largest variance of any data projection is on the first coordinate (called the first principal component), the second largest variance is on the second coordinate (the second principal component), and so on. PCA is often used to reduce the dimensionality of a data set while retaining the features that contribute the most to the variance of the data set. This is done by retaining the lower-order principal components and ignoring the higher-order principal components. In this way, the lower-order components often retain the most important aspects of the data.

[0060] The traditional extruder status evaluation and parameter adjustment mainly rely on manual experience. This method not only requires a lot of time and manpower, but also has certain subjectivity and uncertainty. Due to the different experience levels and judgment abilities of operators, the evaluation results may have large deviations, making it difficult to ensure the stable and efficient operation of the extruder. In addition, the manual identification and evaluation process is cumbersome and inefficient, and it is impossible to reflect the operating status of the extruder in a timely and rapid manner, and adjust the parameters of the extruder according to the operating status, thus affecting the production efficiency of the extruder and the quality of feed.

[0061] Based on this, the present application proposes a method, device, electronic device and storage medium for controlling the puffing of fish and shrimp feeds, which can evaluate the puffing machine based on the operating parameters of the puffing machine, and adjust the adjustable parameters of the puffing machine based on the evaluation, thereby improving the quality of the feed.

[0062] The fish and shrimp feed puffing control method of the embodiment of the present application can be applied to a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the fish and shrimp feed puffing control method, but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] According to the first aspect of the present application, the method for controlling the expansion of fish and shrimp feed is described in detail. Figure 1 , Figure 1 The following is a flow chart of the steps of the method for controlling the expansion of fish and shrimp feed according to an embodiment of the present application. The method for controlling the expansion of fish and shrimp feed includes:

[0065] Step S110, obtaining current parameter values ​​of various operating parameters of the extruder, and obtaining historical data sets of various operating parameters of the extruder; wherein the operating parameters include adjustable parameters and non-adjustable parameters;

[0066] It is worth noting that the operating parameters of the extruder include adjustable parameters and non-adjustable parameters. Adjustable parameters refer to parameters that can be directly set through the control system of the extruder, and non-adjustable parameters are parameters that cannot be directly set and can only be adjusted indirectly. The extruder includes devices such as a feeder, a modulator, a main machine, an extruding chamber, and a cutter motor. The adjustable parameters of the extruder may include the load rate, operating current, speed and frequency of the feeder, the load rate, modulation humidity, and steam flow of the modulator, the speed and load rate of the main machine, and the operating current and load rate of the cutter motor. Non-adjustable parameters include the temperature and pressure in the extruding chamber, as well as the temperature and pressure at the extruder outlet.

[0067] It is worth noting that the extruder is provided with relevant sensors to detect the parameter values ​​of the operating parameters, such as temperature sensors and pressure sensors, etc. In addition, a log module is configured in the control system of the extruder, which records the parameter values ​​of the operating parameters to form a historical data set. A historical data set is generated for each operating parameter. The corresponding time periods of each historical data set are the same. For example, if the historical time period of the historical data set corresponding to the rotation speed of the host is X months in the past, then the historical time period of the historical data set corresponding to the pressure in the extruding chamber is also X months in the past. And the number of elements in the historical data sets corresponding to each operating parameter is the same.

[0068] Step S120, determining the evaluation score of the operating parameter based on the relative position of the current parameter value of the operating parameter in the corresponding historical data set;

[0069] It is worth noting that the relative position of the current parameter value of the operating parameter in the corresponding historical data set can reflect the relationship between the current parameter value and the change trend of the parameter value in the historical data set, and the obtained evaluation score can accurately reflect the state of the current parameter value of the operating parameter. Specifically, the more the current parameter value conforms to the change trend of the parameter value in the historical data set, the higher the evaluation score of the indicator, and the range of the evaluation score is [0, 1].

[0070] Step S130, determining a weight value of each operating parameter based on the historical data set;

[0071] Step S140, performing weighted sum processing based on the weight value of each operating parameter and the evaluation score to obtain the operating status score of the expander;

[0072] It is worth noting that after determining the weight and evaluation score of each indicator, the weighted sum is calculated using the following formula:

[0073]

[0074] Among them, score represents the score of the operating status of the extruder, n is the number of operating parameters, w is i Characterizes the weight of the i-th operating parameter, value i The evaluation score representing the i-th operating parameter. Since the evaluation score ranges from [0, 1] and the sum of the weights of the operating parameters is 1, the operating status score of the extruder ranges from [0, 1]. Since the more the current parameter value conforms to the change trend of the parameter value in the historical data set, the higher the evaluation score of the operating parameter, the higher the operating status score of the extruder is, indicating that the operating status score of the extruder is better. The operating status of the extruder can be reflected by the score of the operating status score of the extruder.

[0075] Step S150, when it is detected that the operating status score is lower than a preset threshold, determining adjustable parameters from various operating parameters, calculating an average value based on the weight values ​​of various adjustable parameters, and obtaining a weighted average value;

[0076] It is worth noting that when the operating status score is detected to be equal to or higher than the preset threshold, it means that the extruder is in good operating condition and no adjustment is required. Those skilled in the art can set the preset threshold according to actual conditions.

[0077] Step S160, based on the historical data set corresponding to the target adjustable parameter, calculate the parameter average value of the target adjustable parameter; wherein the weight value of the target adjustable parameter is greater than the weight average value;

[0078] Exemplarily, a historical data set of a target adjustable parameter is X0, X1, ..., Xn; then the parameter average value of the target adjustable parameter is X=(X0+X1+...Xn) / n.

[0079] Step S170 : modifying the current parameter value of the target adjustable parameter based on the parameter average value of the target adjustable parameter.

[0080] It is worth noting that the weight value of the target adjustable parameter is greater than the weight average value, so the target adjustable parameter has a greater impact on the operating status score of the extruder. Therefore, by adjusting the target adjustable parameter value, the operating status score of the extruder can be greatly affected without adjusting all adjustable parameter values, so that the adjustment efficiency can be improved. In addition, during the adjustment process, the current parameter value of the target adjustable parameter is made close to the parameter average value of the target adjustable parameter. After the adjustment, steps S110 to S140 are repeatedly performed until the operating status score is higher than the preset threshold.

[0081] According to the fish and shrimp feed puffing control method of the first embodiment of the present application, through the above steps S110 to S170, the evaluation score of the operating parameter is determined based on the relative position of the current parameter value of the operating parameter in the corresponding historical data set; then the weight value of each operating parameter is determined based on the historical data set; and the weighted summation processing is performed based on the weight value of each operating parameter and the evaluation score to obtain the operating status score of the extruder. In this way, the operating status score of the extruder is automatically obtained. Compared with relying on manual experience, the present application obtains the score based on the weighted summation processing based on the weight value of each operating parameter and the evaluation score, and is obtained based on adjustable parameters and non-adjustable parameters. Therefore, the score obtained by the present application is more comprehensive and more accurate. When it is detected that the operating status score is lower than the preset threshold, the current parameter value of the target adjustable parameter is modified based on the parameter average value of the target adjustable parameter, so that the current parameter value of the target adjustable parameter tends to the parameter average value, thereby ensuring the stability of the operation of the extruder. Therefore, the present application can obtain the evaluation of the extruder based on the operating parameters of the extruder, and adjust the adjustable parameters of the extruder based on the evaluation, thereby improving the quality of the feed.

[0082] In one embodiment, step S170 may include but is not limited to the following steps:

[0083] Step S171, determining the difference between the current parameter value of the target adjustable parameter and the corresponding parameter average value;

[0084] Step S172, if the current parameter value is lower than the corresponding parameter average value, set the current parameter value = current parameter value + 0.5 * difference;

[0085] Step S173, if the current parameter value is higher than the corresponding parameter average value, set the current parameter value = current parameter value - 0.5 * difference.

[0086] It is worth noting that through steps S171 to S173, the current parameter value of the target adjustable parameter can be gradually approached to the parameter average value. And each time steps S171 to S173 are executed, steps S110 to S140 are executed once until the operating status score is higher than the preset threshold.

[0087] In one embodiment, step S130 may include but is not limited to the following steps:

[0088] Step S131, standardizing the historical data set of operating parameters to obtain a standardized data set;

[0089] It is worth noting that the historical data set of each operating parameter is standardized to eliminate the dimensional effects between different variables.

[0090] Exemplarily, the normalization is performed by the following formula:

[0091]

[0092] Among them, x ij The jth element in the historical data set representing the i-th operating parameter, μ xi The average value of the historical data set representing the i-th operating parameter, σ xi The standard deviation of the historical data set representing the i-th operating parameter, y ij Characterization x ij The normalized value.

[0093] Step S132, constructing a covariance matrix based on the standardized data set of each operating parameter;

[0094] Specifically, the covariance of any two operating parameters is calculated using the following formula:

[0095]

[0096] Among them, S represents the covariance, Y k Characterizes the kth operating parameter among multiple operating parameters, Y f Characterizes the fth operating parameter among multiple operating parameters, Y ki Characterization Y k The i-th value in the historical data set, Y fi Characterization Y f The i-th value in the historical data set, Y ki With Y fi All are values ​​after the standardization process in step S131, and n is the number of elements in the historical data set.

[0097] After calculating the covariance of every two operating parameters, a covariance matrix can be constructed based on each covariance.

[0098] Step S133, performing eigenvalue decomposition on the covariance matrix to obtain multiple eigenvalues ​​and multiple eigenvectors, where the eigenvalues ​​correspond to the eigenvectors one to one;

[0099] Specifically, the LAPACP (Linear Algebra PACPAGE) library can be used to obtain the eigenvalues ​​and eigenvectors of the covariance matrix. LAPACP is a linear algebra computing library written in Fortran, which provides a wealth of functions to calculate linear algebra problems such as eigenvalues ​​and eigenvectors. This library is not only applicable to dense matrices, but also to banded matrices, and provides real and complex version functions. The LAPACP library is often used together with the BLAS (Basic Linear Algebra Subprograms) library, which is the core of many numerical computing software libraries and provides efficient vector and matrix operations.

[0100] Step S134, obtaining the weight value of the operating parameter based on the eigenvalue and the eigenvector.

[0101] In one embodiment, step S134 may include the following steps:

[0102] Step S1341, arranging the corresponding eigenvectors in descending order according to the size of the eigenvalues, and selecting the first P eigenvectors as target eigenvectors; where P is a preset value;

[0103] Step S1342, calculating the sub-weight of the operating parameter in the i-th target feature vector, where 1≤i≤P;

[0104] Step S1343, accumulating the sub-weights of the operating parameters in the i-th to P-th target feature vectors to obtain the weight value of the operating parameter.

[0105] Exemplarily, there are P target feature vectors, and the i-th target feature vector is [Zi1, Zi2, ... Zim], where m is the number of operating parameters, and the operating parameters are X1, X2... Xm. Then Xj represents the j-th operating parameter, and the sub-weight of Xj in the i-th target feature vector is:

[0106]

[0107] Among them, Li represents the sub-weight of Xj in the i-th target feature vector, where 1≤j≤m, 1≤i≤P. Then the weight value of Xj is

[0108] It is worth noting that, although the purpose of the principal component analysis method is to reduce the dimension of the data, the contribution of each variable will be determined during the processing of the principal component analysis method, so as to discard some variables based on the contribution and achieve the purpose of data dimensionality reduction. The processing process of the present application is similar to the principal component analysis method, but it is not entirely based on the principal component analysis method. Through the above steps, the contribution of each operating parameter to the operating status score of the extruder can be determined, thereby determining the weight value of each operating parameter, but the data is not reduced in dimension. The weight obtained in the embodiment of the present application can reflect the importance of the corresponding operating parameter to the operating status score of the extruder.

[0109] In one embodiment, step S120, determining the evaluation score of the operating parameter based on the relative position of the current parameter value of the operating parameter in the corresponding historical data set, includes:

[0110] Step S121, calculating the average value of the historical data set and the standard deviation of the historical data set;

[0111] Step S122, detecting and processing the current parameter value based on the average value of the historical data set and the standard deviation of the historical data set;

[0112] Step S123: upon detecting that the current parameter value is greater than or equal to (μ xi -σ xi ), and less than or equal to (μ xi +σ xi ), based on the historical data set corresponding to the current parameter value, xi -σ xi , μ xi +σ xi ] elements to construct the reference data set; where μ xi Characterizes the average value of the historical data set corresponding to the i-th operating parameter, σ xi Characterizes the standard deviation of the historical data set corresponding to the i-th operating parameter;

[0113] Step S124, determining the evaluation score of the operating parameter corresponding to the current parameter value based on the element with the largest value and the element with the smallest value in the reference data set.

[0114] The embodiment of the present application obtains a reference data set through steps S121 to S123 based on the average value and standard deviation of the historical data set, and the relationship between the current parameter value and the average value and standard deviation. Each element in the reference data set is in [μ xi -σ xi , μ xi +σ xi ] range, so the elements in the reference data set are more representative and can reflect the intermediate state of the parameter value change trend of the operating parameter during the historical operation process.

[0115] In one embodiment, in step S122, after the current parameter value is detected based on the average value of the historical data set and the standard deviation of the historical data set, the following further comprises:

[0116] Step S125: When the current parameter value is detected to be less than (μ xi -σ xi ), the evaluation score of the operating parameter corresponding to the current parameter value is set to the lower limit of the preset range; the preset range is the value range of the evaluation score;

[0117] It is worth noting that when the current parameter value is detected to be less than (μ xi -σ xi), it means that the current parameter value of the operating parameter deviates from the average value of the historical data set of the operating parameter, and that the current parameter value is abnormal. Therefore, the evaluation score of the operating parameter is set to the lower limit value of the preset range. For example, if the preset range is [0, 1], the evaluation score of the operating parameter is set to 0.

[0118] Step S126, when it is detected that the current parameter value is greater than (μ xi +σ xi ), the evaluation score of the operating parameter corresponding to the current parameter value is set to the upper limit of the preset range; where μ xi Characterizes the average value of the historical data set corresponding to the i-th operating parameter, σ xi Characterizes the standard deviation of the historical data set corresponding to the i-th operating parameter.

[0119] It is worth noting that when the current parameter value is detected to be greater than (μ xi +σ xi ), it means that the current parameter value is consistent with the operating trend of the operating parameter, indicating that the operating parameter is in a better state. Therefore, the evaluation score of the operating parameter corresponding to the current parameter value is set to the upper limit of the preset range. For example, if the preset range is [0, 1], the evaluation score of the operating parameter is set to 1.

[0120] In one embodiment, step S124, determining the evaluation score of the operating parameter corresponding to the current parameter value based on the element with the largest value and the element with the smallest value in the reference data set, includes:

[0121] The evaluation score is obtained through the assignment calculation formula, which is:

[0122]

[0123] Among them, value i Characterizes the evaluation score of the i-th operating parameter, x i Characterizes the current parameter value of the i-th operating parameter; X imin Represents the element with the smallest value in the reference data set; X imax Represents the element with the largest value in the reference data set.

[0124] In this embodiment, the range of the evaluation score is limited to [0, 1]. By assigning a calculation formula, it can be ensured that the evaluation score is within the range of [0, 1] and can reflect the relative position of the current parameter value in the reference parameter set.

[0125] The second embodiment of the present application provides a device for controlling the expansion of fish and shrimp feed. Figure 2 , Figure 2This is a schematic diagram of the structure of the fish and shrimp feed expansion control device of the embodiment of the present application. The control device includes:

[0126] The acquisition module 210 is used to acquire the current parameter value of each operating parameter of the extruder, and acquire the historical data set of each operating parameter of the extruder; the operating parameters include adjustable parameters and non-adjustable parameters;

[0127] A score determination module 220, for determining an evaluation score of an operating parameter based on a relative position of a current parameter value of the operating parameter in a corresponding historical data set;

[0128] A weight determination module 230, for determining a weight value of each operating parameter based on a historical data set;

[0129] A summing module 240 is used to perform weighted summing processing based on the weight value of each operating parameter and the evaluation score to obtain the operating status score of the extruder;

[0130] A first calculation module 250 is used to calculate an average value based on the weight values ​​of each adjustable parameter to obtain a weighted average value when it is detected that the running status score is lower than a preset threshold;

[0131] A second calculation module 260 is used to calculate a parameter average value of the target adjustable parameter based on a historical data set corresponding to the target adjustable parameter; wherein the weight value of the target adjustable parameter is greater than the weight average value;

[0132] The modifying module 270 is configured to modify the current parameter value of the target adjustable parameter based on the parameter average value of the target adjustable parameter.

[0133] The fish and shrimp feed puffing control device of the embodiment of the present application is used to execute the fish and shrimp feed puffing control method of the embodiment of the first aspect. When executing the method, the evaluation score of the operating parameter is determined based on the relative position of the current parameter value of the operating parameter in the corresponding historical data set; then the weight value of each operating parameter is determined based on the historical data set; and the weighted summation processing is performed based on the weight value of each operating parameter and the evaluation score to obtain the operating state score of the puffing machine. In this way, the operating state score of the puffing machine is automatically obtained. Compared with relying on manual experience, the present application is obtained by weighted summation processing based on the weight value of each operating parameter and the evaluation score, and is obtained based on adjustable parameters and non-adjustable parameters. Therefore, the score obtained by the present application is more comprehensive and more accurate. When it is detected that the operating state score is lower than the preset threshold, the current parameter value of the target adjustable parameter is modified based on the parameter average value of the target adjustable parameter, so that the current parameter value of the target adjustable parameter tends to the parameter average value, thereby ensuring the stability of the operation of the puffing machine. Therefore, the present application can obtain the evaluation of the puffing machine based on the operating parameters of the puffing machine, and adjust the adjustable parameters of the puffing machine based on the evaluation, thereby improving the quality of the feed.

[0134] The third aspect of the present application provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the fish and shrimp feed puffing control method of the above embodiment when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0135] In one embodiment, referring to Figure 3 , Figure 3 The hardware structure of the electronic device of the embodiment of the present application is illustrated, and the electronic device includes:

[0136] The processor 301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0137] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 302, and the processor 301 calls and executes the fish and shrimp feed puffing control method of the embodiment of this application;

[0138] Input / output interface 303, used to implement information input and output;

[0139] The communication interface 304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0140] A bus 305 that transmits information between the various components of the device (e.g., the processor 301, the memory 302, the input / output interface 303, and the communication interface 304);

[0141] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0142] The fourth aspect of the present application is a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for controlling the puffing of fish and shrimp feeds according to the first aspect is implemented.

[0143] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0144] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0145] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0146] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0148] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0149] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0150] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0151] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0153] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0154] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for controlling the expansion of fish and shrimp feed, characterized in that: include: Acquire current parameter values ​​of various operating parameters of the extruder, and acquire historical data sets of various operating parameters of the extruder; The operating parameters include adjustable parameters and non-adjustable parameters; Determining an evaluation score of the operating parameter based on a relative position of the current parameter value of the operating parameter in the corresponding historical data set; Determining a weight value of each of the operating parameters based on the historical data set; Performing weighted sum processing based on the weight value of each operating parameter and the evaluation score to obtain the operating status score of the expander; When it is detected that the running status score is lower than a preset threshold, an average value is calculated based on the weight values ​​of each of the adjustable parameters to obtain a weighted average value; Based on the historical data set corresponding to the target adjustable parameter, a parameter average value of the target adjustable parameter is calculated; wherein the weight value of the target adjustable parameter is greater than the weight average value; The current parameter value of the target adjustable parameter is modified based on the parameter average value of the target adjustable parameter.

2. The method for controlling the expansion of fish and shrimp feed according to claim 1, characterized in that: The step of modifying the current parameter value of the target adjustable parameter based on the parameter average value of the target adjustable parameter comprises: Determining a difference between the current parameter value of the target adjustable parameter and the corresponding parameter average value; If the current parameter value is lower than the corresponding parameter average value, then the current parameter value=the current parameter value+0.5*the difference; If the current parameter value is higher than the corresponding parameter average value, then the current parameter value=the current parameter value-0.5*the difference.

3. The method for controlling the expansion of fish and shrimp feed according to claim 1, characterized in that: Determining the weight value of each of the operating parameters based on the historical data set includes: Standardizing the historical data set of the operating parameters to obtain a standardized data set; Constructing a covariance matrix based on the standardized data set of each operating parameter; Performing eigenvalue decomposition on the covariance matrix to obtain a plurality of eigenvalues ​​and a plurality of eigenvectors, wherein the eigenvalues ​​correspond to the eigenvectors one-to-one; Based on the eigenvalue and the eigenvector, a weight value of the operating parameter is obtained.

4. The method for controlling the expansion of fish and shrimp feed according to claim 3, characterized in that: The obtaining the weight value of the operating parameter based on the eigenvalue and the eigenvector includes: Arrange the corresponding eigenvectors in descending order according to the size of the eigenvalues, and select the first P eigenvectors as target eigenvectors; where P is a preset value; Calculate the sub-weight of the operating parameter in the i-th target feature vector, where 1≤i≤P; The sub-weights of the operating parameter in the i-th to P-th target feature vectors are accumulated to obtain a weight value of the operating parameter.

5. The method for controlling the expansion of fish and shrimp feed according to claim 1, characterized in that: The determining the evaluation score of the operating parameter based on the relative position of the current parameter value of the operating parameter in the corresponding historical data set includes: Calculating the average value of the historical data set and the standard deviation of the historical data set; Performing detection processing on the current parameter value based on the average value of the historical data set and the standard deviation of the historical data set; When it is detected that the current parameter value is greater than or equal to (μ xi -σ xi ), and less than or equal to (μ xi +σ xi ), based on the historical data set corresponding to the current parameter value, xi -σ xi , μ xi +σ xi ] elements to construct the reference data set; where μ xi represents the average value of the historical data set corresponding to the i-th operating parameter, σ xi Characterizes the standard deviation of the historical data set corresponding to the i-th operating parameter; An evaluation score of the operating parameter corresponding to the current parameter value is determined based on the element with the largest value and the element with the smallest value in the reference data set.

6. The method for controlling the expansion of fish and shrimp feed according to claim 5, characterized in that: After the current parameter value is detected based on the average value of the historical data set and the standard deviation of the historical data set, the method further includes: When it is detected that the current parameter value is less than (μ xi -σ xi ), the evaluation score of the operating parameter corresponding to the current parameter value is set to the lower limit of a preset range; the preset range is the value range of the evaluation score; When it is detected that the current parameter value is greater than (μ xi +σ xi ), the evaluation score of the operating parameter corresponding to the current parameter value is set to the upper limit of the preset range; wherein, μ xi represents the average value of the historical data set corresponding to the i-th operating parameter, σ xi Characterizes the standard deviation of the historical data set corresponding to the i-th operating parameter.

7. The method for controlling the expansion of fish and shrimp feed according to claim 5, characterized in that: The determining the evaluation score of the operating parameter corresponding to the current parameter value based on the element with the largest value and the element with the smallest value in the reference data set includes: The evaluation score is obtained through the assignment calculation formula, which is: Among them, value i Characterizes the evaluation score of the i-th operating parameter, x i Characterizing the current parameter value of the i-th operating parameter; X imin Characterizes the element with the smallest value in the reference data set; X imax Characterizes the element with the largest value in the reference data set.

8. A device for controlling the expansion of fish and shrimp feed, characterized in that: include: an acquisition module, used for acquiring current parameter values ​​of various operating parameters of the extruder, and acquiring historical data sets of various operating parameters of the extruder; The operating parameters include adjustable parameters and non-adjustable parameters; A scoring determination module, configured to determine an evaluation score of the operating parameter based on a relative position of the current parameter value of the operating parameter in the corresponding historical data set; A weight determination module, used to determine a weight value of each of the operating parameters based on the historical data set; A summing module, used for performing weighted summing processing based on the weight value and the evaluation score of each operating parameter to obtain the operating status score of the extruder; A first calculation module, configured to calculate an average value based on the weight values ​​of each of the adjustable parameters to obtain a weighted average value when it is detected that the running status score is lower than a preset threshold; A second calculation module is used to calculate the parameter average value of the target adjustable parameter based on the historical data set corresponding to the target adjustable parameter; wherein the weight value of the target adjustable parameter is greater than the weight average value; A modification module is used to modify the current parameter value of the target adjustable parameter based on the parameter average value of the target adjustable parameter.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the fish and shrimp feed puffing control method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for controlling the expansion of fish and shrimp feeds according to any one of claims 1 to 7 is implemented.

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