Adaptive regulation fish feed intelligent drying method and system

Abstract

The present application belongs to the technical field of data acquisition and intelligent preparation, and provides an intelligent fish feed drying method and system with adaptive regulation, specifically: a humidity sensor is arranged in the drying device to obtain real-time measured values, imbalance analysis is performed according to the measured values to obtain the evaporation value imbalance level, and finally, the drying device is compensated and controlled in combination with the evaporation value imbalance level of each position. The obtained data is subjected to hierarchical quantitative analysis, and the feed drying uniformity in the radiation range of the data acquisition position is effectively quantified, so that the data support for further optimizing the drying strategy is more accurate and reliable. The low-temperature threshold method is compensated and controlled, thereby reducing the risk of uneven drying effect in the feed drying process. Further, the problem of insufficient stability of fish feed caused by probability is eliminated, so that the feed can guarantee the performance of the suspension state and the sinking state, and the stability of the fish feed quality is enhanced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data acquisition and intelligent preparation, and particularly relates to an intelligent fish feed drying method and system with adaptive regulation. BACKGROUND

[0002] There is a drying link in the process of fish feed preparation. By drying the feed, the moisture content in the fish feed can be reduced to a lower level, thereby prolonging its shelf life and reducing the risk of feed spoilage and deterioration. At the same time, the high temperature in the drying process can kill bacteria, parasites and other harmful microorganisms in the fish feed, which helps to ensure the hygiene and safety of the fish feed and avoid potential threats to fish health due to microbial contamination. The dried fish feed is smaller in volume and lighter in weight, making it easy to store and transport. Compared with wet feed, dried feed occupies less space, making it easier to manage and distribute. Feed drying equipment usually uses a flow type or continuous type working method, in which the temperature control module in the drying process is the core module of the drying principle.

[0003] However, due to different raw material components, different formula proportions, different particle sizes and different discharging processes, etc., the speed at which the feed discharges moisture in hot air will be different. If the temperature is too high, the feed may be carbonized, thereby causing some of the feed to become waste, wasting materials and increasing costs. The method commonly used in the industry is to set a threshold to prevent the problem of feed carbonization caused by too high a temperature. In order to prevent the hysteresis of temperature control from causing too high a temperature, a lower temperature threshold is generally used as a preventive measure. This optimization in preventing carbonization problems also brings the problem of uneven drying level of the feed. Uneven drying level can cause insufficient stability of the fish feed, further affecting the performance of the suspended state and the sinking state. SUMMARY

[0004] The present application aims to provide an intelligent fish feed drying method and system with adaptive regulation to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, an intelligent fish feed drying method with adaptive regulation is provided, which comprises the following steps:

[0006] S100, arranging a humidity sensor in the drying device to obtain real-time measured values;

[0007] S200, performing imbalance analysis according to the measured values to obtain an evaporation value imbalance level;

[0008] S300, compensating and controlling the drying device in combination with the evaporation value imbalance level of each position.

[0009] Further, in step S100, the method of arranging the humidity sensor in the drying device to obtain the measured value in real time is: taking the direction of the feed transmission on the conveying belt in the drying device as the conveying direction, equidistantly setting a plurality of positions as temperature control points along the conveying direction, and arranging a heating element at each temperature control point, the heating element being an electric heating tube, a gas burner or a steam heater; the distance between the heating elements is denoted as HL, and the humidity sensor is arranged at a position 0.5×HL away from each heating element along the conveying direction, which measures the humidity of the feed at the current time and takes it as the measured value Mpv; the acquisition interval of the measured value is tp, tp∈[10, 60] seconds.

[0010] Further, in step S200, the method of performing imbalance analysis according to the measured value to obtain the evaporation value imbalance level is:

[0011] taking each humidity sensor as a node; taking a time period TZ as the analysis period, TZ∈[1, 2] hours; the default value of TZ is set to 2; the measured value of each node obtained in real time is denoted as Mpv, and the average value of the measured values obtained from each node at the same time is defined as the field average measurement Tav; taking the measured values at each time in the same node as a row and the measured values of each node at the same time as a column, a matrix is constructed as a measured distribution table RM; the time range in RM is the latest TZ time period;

[0012] taking RM(j1) as the j1th element in RM, when RM(j1)≤Tav, the element is denoted as the measured site CRD, and cnt(CRD) is the total amount of the measured sites in RM; a sequence is constructed from all the measured sites and denoted as the measured value sequence, and the current time imbalance degree qx is calculated according to the measured value sequence, qx=HF<lg(1+MMpv), lg(1+EMpv)>, where MMpv and EMpv represent the median and arithmetic mean of the measured value sequence respectively, and HF is the harmonic mean function; the evaporation value imbalance level TGEV is calculated:

[0013] ;

[0014] where j2 is an accumulation variable, ERM is the average value of all elements in RM, RM(j2) is the j2th element in the measured value sequence, CRD j2 represents the j2th measured site, and st<> is a trace function that returns the serial number of the corresponding column of the measured site in the measured distribution table.

[0015] Since the above steam value imbalance level is obtained in combination with the test points, the influence of the common phenomenon of high humidity at local points in the drying process on the overall quantitative result is effectively reduced, however, this method of ignoring the high humidity at local points is prone to cause quantitative deficiency or a large amount of waste of measured values in the calculation process, which can lead to the problem that the obtained result is too one-sided and not accurate in imbalance analysis, but the prior art cannot solve the problem that the identification of the steam value imbalance level is too monotonous and not flexible, in order to make the test points more accurate and the adaptability of the steam value imbalance level stronger, and eliminate the waste of using data, therefore, a more preferred scheme is provided in the present application.

[0016] Preferably, in step S200, the method of obtaining the steam value imbalance level according to the measured values in the imbalance analysis is:

[0017] Each humidity sensor is taken as a node, a time period TZ is taken as an analysis period, TZ ∈ [1, 2] hours; each measured value obtained in the time period TZ is constructed into a sequence, which is denoted as a measured value sequence; Etgl is the ratio of the minimum value to the maximum value in the measured value sequence;

[0018] The time when each element corresponding to the maximum value or the minimum value appears in the measured value sequence is denoted as a measured reset point; the time period between two adjacent measured reset points before and after a measured reset point is taken as the reset period Tlen of the measured reset point; all the reset periods that can be obtained in TZ are obtained, and the average value of the time lengths of the reset periods is taken as the calculation interval Nvat; in the measured value sequence, a time point is selected every Nvat in time sequence from the first element; the total number of the skip test points is denoted as Ntp;

[0019] The average value of the measured values of each skip test point is defined as the measurement point average value Tce at the current time; the ratio of the measured value to the measurement point average value of a skip test point is taken as the floating moment value Evic of the skip test point; Mtsv represents the imbalance moment value, and the imbalance moment value Mtsv(n1) of the nth1 skip test point at the current time is calculated:

[0020] ;

[0021] Wherein, i1 is an accumulation variable, Evic i1 and Evic i1-1 are the floating moment values of the i1th and i1-1th skip test points respectively; ln() is the logarithmic function with e as the base number, and ds(Tlen n1 ) represents the difference between the maximum value and the minimum value in the reset period of the nth1 skip test point.

[0022] The imbalance moment value of each jump site is constructed into a sequence, which is recorded as an imbalance moment value sequence, and the arithmetic mean of the imbalance moment value sequence is defined as a moment value index Wctl; when a jump site satisfies Mtsv > Wctl, the jump site is defined as a first jump site, otherwise the time is defined as a second jump site; the median value of the imbalance moment value corresponding to all first jump sites is recorded as a high measurement moment value Tqa at the current time, and the median value of the imbalance moment value corresponding to all second jump sites is recorded as a low measurement moment value Tqb at the current time; the next node of a node in the conveying belt direction is taken as the lower adjacent node of the node; and the steam value imbalance level TGEV of the node at the current time is calculated:

[0023] ;

[0024] Wherein exp() is an exponential function with natural constant e as the base, HF<> is a harmonic mean function, lg() is a logarithmic function with natural constant 10 as the base, Etgl_nd, Tqa_nd and Tqb_nd represent the moment value reference, the high measurement moment value and the low measurement moment value of the lower adjacent node respectively.

[0025] The steam value imbalance level is calculated based on the measured values at each time, and the obtained data is subjected to hierarchical quantitative analysis, so that the feed drying balance in the radiation range of the data acquisition position is effectively quantified, and the steam utilization efficiency in the feed drying process can be further understood. According to the analysis of the measured values, the imbalance degree of steam utilization at different times is not consistent, and the feature extraction can subdivide the data into different subsets, so as to obtain more specific drying balance indicators, so that the data support for further optimizing the drying strategy is more accurate and reliable.

[0026] Further, in step S300, the method for compensating and controlling the drying device in combination with the steam value imbalance levels of each position is: the number of humidity sensors is recorded as NSen, and the measurement point threshold Th_Sen is set as: Th_Sen = int(NSen ÷ 2), wherein int() is a rounding up function; the humidity sensor obtains each steam value imbalance level in sequence; if the steam value imbalance level at a time is greater than that at the previous time, then the steam value imbalance event at the time is defined; when a humidity sensor occurs steam value imbalance event at the current time and the previous time, then the sensor is defined as imbalance abnormality; the number of sensors that occur imbalance abnormality at the current time is recorded as N_Ov; when N_Ov ≥ Th_Sen, the fan power is increased by 5-10%, and the time length for increasing the drying fan power is Lv_tp:

[0027] When 1.0 ≤ N_Ov: Th_Sen < 1.2, Lv_tp is set as 0.5tp;

[0028] when 1.2≤N_Ov:Th_Sen<1.4, Lv_tp is set as 0.6tp;

[0029] when 1.4≤N_Ov:Th_Sen<1.6, Lv_tp is set as 0.7tp;

[0030] when 1.6≤N_Ov:Th_Sen, Lv_tp is set as 0.8tp;

[0031] wherein the drying fan is any one of a radial fan, a radial fan or a ventilation fan.

[0032] Preferably, all the undefined variables in the present application are manually set thresholds if not explicitly defined.

[0033] The present application also provides a self-adaptive regulation fish feed intelligent drying system, the self-adaptive regulation fish feed intelligent drying system comprises a processor, a memory and a computer program stored in the memory and executable on the processor, the processor executes the computer program to realize the steps in the self-adaptive regulation fish feed intelligent drying method, the self-adaptive regulation fish feed intelligent drying system can run in desktop computers, notebook computers, palmtop computers and cloud data centers and other computing devices, the executable system can include, but is not limited to, a processor, a memory, a server cluster, and the processor executes the computer program to run in the following system units:

[0034] A data measurement unit is arranged for arranging a humidity sensor in the drying device to obtain real-time measured values.

[0035] An imbalance analysis unit is arranged for imbalance analysis according to the measured values to obtain the evaporation value imbalance level.

[0036] An element regulation unit is arranged for compensating control of the drying device in combination with the evaporation value imbalance level of each position.

[0037] The beneficial effects of the present application are: the present application provides a self-adaptive regulation fish feed intelligent drying method and system, the obtained various data are subjected to hierarchical quantitative analysis, the feed drying balance in the radiation range of the data collection position is effectively quantified, and the steam utilization efficiency in the feed drying process can be more deeply understood. According to the analysis of the measured values, the feature extraction of the imbalance degree of steam utilization at different times is carried out, the data can be subdivided into different subsets, so that more specific drying balance indexes are obtained, so that the data support for further optimizing the drying strategy is more accurate and reliable. A more reasonable operation strategy is formulated for different time periods and positions, so as to maximize the uniform drying of the feed and improve the utilization efficiency of the steam, reduce energy waste, and prevent the problem of carbonization of the feed caused by too high temperature. When a lower temperature threshold is used as a preventive measure, the drying device is subjected to compensation control, thereby reducing the risk of uneven drying effect in the feed drying process. Further prevent the problem of insufficient stability of fish feed caused by probability, so that the feed can guarantee the performance of the suspended state and the sinking state, and enhance the stability of the fish feed quality. BRIEF DESCRIPTION OF DRAWINGS

[0038] The above and other features of the present application will become more apparent from the following detailed description of embodiments taken in conjunction with the accompanying drawings, in which like reference characters indicate the same or similar elements throughout the drawings, and in which:

[0039] Figure 1 A flowchart of a self-adaptive regulation fish feed intelligent drying method is shown.

[0040] Figure 2 A self-adaptive regulation fish feed intelligent drying system structure diagram is shown. DETAILED DESCRIPTION

[0041] The concept, specific structure and technical effects of the present application will be described clearly and completely in conjunction with the embodiments and the drawings, so as to fully understand the purpose, scheme and effect of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0042] As Figure 1 A flowchart of a self-adaptive regulation fish feed intelligent drying method is shown. The self-adaptive regulation fish feed intelligent drying method according to the embodiments of the present application will be described below in conjunction with Figure 1 The method comprises the following steps:

[0043] S100, arranging humidity sensors in the drying device to obtain measured values in real time;

[0044] S200, obtaining the evaporation value imbalance level by imbalance analysis according to the measured values;

[0045] S300, compensating and controlling the drying device in combination with the evaporation value imbalance level of each position.

[0046] Further, in step S100, the method of arranging humidity sensors in the drying device to obtain measured values in real time is: taking the direction of the feed transmission on the conveying belt in the drying device as the conveying direction, setting a plurality of positions as temperature control points along the conveying direction at equal distances, arranging a heating element at each temperature control point, the heating element being an electric heating tube, a gas burner or a steam heater; taking the distance between the heating elements as HL, arranging a humidity sensor at a position 0.5×HL away from each heating element along the conveying direction, the humidity sensor measuring the humidity of the feed at the current time and taking it as the measured value Mpv in real time; the acquisition interval of the measured value is tp, tp∈[10, 60] seconds.

[0047] Further, in step S200, the method of obtaining the evaporation value imbalance level by imbalance analysis according to the measured values is:

[0048] taking each humidity sensor as a node; taking a time period TZ as the analysis period, TZ∈[1, 2] hours; setting the default value of TZ as 2; taking the measured value of each node obtained in real time as Mpv, defining the average value of the measured values obtained from each node at the same time as the field average measurement Tav; taking the measured values at each time in the same node as a row, and the measured values of each node at the same time as a column, to construct a matrix as a measured distribution table RM; the time range in RM is the latest TZ time period;

[0049] taking RM(j1) as the j1th element in RM, when RM(j1)≤Tav, taking the element as the over-measured point CRD, and taking cnt(CRD) as the total amount of over-measured points in RM; constructing a sequence from all over-measured points as an over-measured value sequence, calculating the over-measured degree qx at the current time according to the over-measured value sequence, qx=HF<lg(1+MMpv), lg(1+EMpv)>, where MMpv and EMpv represent the median and arithmetic mean of the over-measured value sequence respectively, and HF is the harmonic mean function; calculating the evaporation value imbalance level TGEV:

[0050] ;

[0051] where j2 is an accumulative variable, ERM is the average of all elements in RM, RM(j2) is the j2th element in the sequence of measured values, CRD j2 represents the j2th measured site, st< is a trace-back function, and the trace-back function returns the serial number of the measured site in the corresponding column of the measured distribution table.

[0052] Preferably, in step S200, the method for obtaining the evaporation imbalance level according to the measured values is imbalance analysis.

[0053] Each humidity sensor is taken as a node, and a time period TZ is taken as an analysis period, TZ ∈ [1, 2] hours; a sequence is constructed by using each measured value obtained in the time period TZ, and the sequence is denoted as a sequence of measured values; Etgl is the ratio of the minimum value to the maximum value in the sequence of measured values.

[0054] Each element corresponding to a time point at which a maximum value or a minimum value appears in the sequence of measured values is denoted as a measured reset site; a time period between two measured reset sites adjacent to each other before and after a measured reset site is taken as a reset period Tlen of the measured reset site; all reset periods that can be obtained in TZ are obtained, and the average of the lengths of the reset periods is taken as a measurement interval Nvat; in the sequence of measured values, a time point is selected every Nvat in a time sequence starting from the first element; and the total amount of the selected time points is denoted as Ntp.

[0055] The average of the measured values of each selected time point is defined as a measured site average Tce at the current time point; the ratio of the measured value of a selected time point to the measured site average is taken as a floating moment value Evic of the selected time point; Mtsv represents an imbalance moment value, and the imbalance moment value Mtsv(n1) of the nth1 selected time point at the current time point is calculated and obtained.

[0056] ;

[0057] where i1 is an accumulative variable, Evic i1 and Evic i1-1 are the floating moment values of the ith1 and ith1-1 selected time points, respectively; ln() is a logarithmic function with e as a base number, and ds(Tlen n1 represents the difference between the maximum value and the minimum value in the reset period of the nth1 selected time point.

[0058] The imbalance moment value of each jump site is constructed into a sequence, which is recorded as an imbalance moment value sequence. The arithmetic mean of the imbalance moment value sequence is defined as the moment value index Wctl. When a jump site satisfies Mtsv > Wctl, the jump site is defined as a first jump site, otherwise the time is defined as a second jump site. The median value of the imbalance moment value corresponding to all first jump sites is recorded as the high measurement moment value Tqa at the current time, and the median value of the imbalance moment value corresponding to all second jump sites is recorded as the low measurement moment value Tqb at the current time. The next node of a node in the direction of the conveying belt is defined as the lower adjacent node of the node. The evaporation value imbalance level TGEV of a node at the current time is calculated:

[0059] ;

[0060] Where exp() is the exponential function with the base number e, HF< > is the harmonic mean function, lg() is the logarithmic function with the base number 10, and Etgl_nd, Tqa_nd, and Tqb_nd represent the moment value reference, the high measurement moment value, and the low measurement moment value of the lower adjacent node, respectively.

[0061] Further, in step S300, the method for compensating and controlling the drying device in combination with the evaporation value imbalance level of each position is: the number of humidity sensors is recorded as NSen, and the measurement point threshold Th_Sen is set as: Th_Sen = int(NSen ÷ 2), where int() is the upward rounding function. The humidity sensor obtains the evaporation value imbalance level of each position in sequence, and if the evaporation value imbalance level at a time is greater than that at the previous time, the evaporation value imbalance event is defined to occur at the time. When a humidity sensor occurs an evaporation value imbalance event at the current time and the previous time, the sensor is defined to have an imbalance anomaly. The number of sensors having an imbalance anomaly at the current time is recorded as N_Ov. When N_Ov ≥ Th_Sen, the fan power is increased by 5-10%, and the time length for increasing the drying fan power is Lv_tp:

[0062] When 1.0 ≤ N_Ov: Th_Sen < 1.2, Lv_tp is set as 0.5tp;

[0063] When 1.2 ≤ N_Ov: Th_Sen < 1.4, Lv_tp is set as 0.6tp;

[0064] When 1.4 ≤ N_Ov: Th_Sen < 1.6, Lv_tp is set as 0.7tp;

[0065] When 1.6 ≤ N_Ov: Th_Sen, Lv_tp is set as 0.8tp;

[0066] The drying fan is any one of a radial fan, a radial fan or a ventilation fan.

[0067] The embodiment of the present application provides a self-adaptive regulation fish feed intelligent drying system. Figure 2 As shown in the figure, the embodiment of the present application provides a self-adaptive regulation fish feed intelligent drying system structure diagram, and the self-adaptive regulation fish feed intelligent drying system of the embodiment comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, and the processor realizes the steps in the self-adaptive regulation fish feed intelligent drying system embodiment when executing the computer program.

[0068] The system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor executes the computer program to run in the following system units:

[0069] The data measurement unit is used for arranging a humidity sensor in the drying device to obtain a measured value in real time.

[0070] The imbalance analysis unit is used for obtaining an evaporation value imbalance level according to the measured value.

[0071] The element regulation unit is used for compensating and controlling the drying device in combination with the evaporation value imbalance level of each position.

[0072] The self-adaptive regulation fish feed intelligent drying system can run in a desktop computer, a notebook computer, a palm computer and a cloud server and the like computing devices. The self-adaptive regulation fish feed intelligent drying system can run in a system which can comprise but is not limited to a processor and a memory. Those skilled in the art can understand that the example is only an example of the self-adaptive regulation fish feed intelligent drying system and does not constitute a limitation on the self-adaptive regulation fish feed intelligent drying system, can comprise more or less components, or combine certain components, or different components, for example, the self-adaptive regulation fish feed intelligent drying system can also comprise an input and output device, a network access device, a bus and the like.

[0073] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or can also be any conventional processor. The processor is a control center of the running system of the fish feed intelligent drying system with adaptive regulation, and connects each part of the running system of the fish feed intelligent drying system with adaptive regulation through various interfaces and lines.

[0074] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the fish feed intelligent drying system with adaptive regulation by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc. The data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0075] Although the description of the present application has been quite detailed and particularly described with respect to several embodiments, it is not intended to be limited to any of these details or embodiments or any special embodiment, so as to effectively cover the intended scope of the present application. In addition, the present application is described above in the embodiments that the inventors can foresee, and the purpose is to provide a useful description, and those non-essential modifications to the present application that have not yet been foreseen can still represent equivalent modifications of the present application.

Claims

1. An adaptive and intelligent drying method for fish feed, characterized in that, The method includes the following steps: S100, a humidity sensor is installed inside the drying device to obtain the measured value in real time; S200, the level of evaporation imbalance is obtained by imbalance analysis based on measured values; S300, combined with the level of evaporation imbalance at various locations, compensates for the control of the drying device. The method for obtaining the evaporation imbalance level in S200 based on the measured values ​​is as follows: each humidity sensor is used as a node; a time period TZ is used as the analysis period, TZ∈[1,2] hours; the default value of TZ is set to 2; the measured values ​​of each node obtained in real time are denoted as Mpv, and the average value of the measured values ​​obtained from each node at the same time is defined as the field average measured value Tav; a matrix is ​​constructed as the measured distribution table RM, with the measured values ​​of each time in the same node as a row and the measured values ​​of each node at the same time as a column; the time range in RM is the most recent TZ time period. Let RM(j1) be the j1-th element in RM. If RM(j1) ≤ Tav, then this element is denoted as the overtesting point CRD, and cnt(CRD) is the total number of overtesting points in RM. A sequence is constructed from all overtesting points and denoted as the overtesting value sequence. The overtesting degree qx at the current time is calculated based on the overtesting value sequence, where qx ​​= HF.<lg(1+MMpv),lg(1+EMpv)> Where MMpv and EMpv represent the median and arithmetic mean of the measured value series, respectively, and HF<> is the harmonic mean function; the evaporation imbalance level TGEV is calculated as follows: ； Where j2 is the cumulative variable, ERM is the average of all elements in RM, RM(j2) is the j2-th element in the measured value sequence, and CRD j2 This represents the j2th overtest site, and st<> is a retrospective function that returns the index of the overtest site in the corresponding column of the measured distribution table.

2. The adaptive and intelligent drying method for fish feed according to claim 1, characterized in that, In step S100, the method for arranging a humidity sensor in the drying device to obtain the measured value in real time is as follows: taking the direction of feed transmission on the conveyor belt in the drying device as the transmission direction, several positions are set at equal intervals along the transmission direction as temperature control points, and heating elements are arranged at each temperature control point. The heating elements are electric heating tubes, gas burners, or steam heaters. The distance between the heating elements is denoted as HL. A humidity sensor is arranged at a distance of 0.5×HL from each heating element along the transmission direction. The humidity sensor measures the humidity of the feed at the current moment in real time and uses it as the measured value Mpv. The interval for obtaining the measured value is tp, tp∈[10,60] seconds.

3. The adaptive and intelligent drying method for fish feed according to claim 1, characterized in that, In step S300, the method for compensating and controlling the drying device based on the evaporation imbalance level at each location is as follows: The number of humidity sensors is denoted as NSen, and the threshold value Th_Sen for each measurement point is set as: Th_Sen = int(NSen ÷ 2), where int() is the round-up function; the humidity sensors acquire consecutive evaporation imbalance levels in time sequence; if the evaporation imbalance level at a given moment is greater than the evaporation imbalance level at the previous moment, an evaporation imbalance event is defined as occurring at that moment; if a humidity sensor experiences evaporation imbalance events at both the current moment and the previous moment, the sensor is defined as experiencing an imbalance anomaly; the number of sensors experiencing an imbalance anomaly at the current moment is denoted as N_Ov; when N_Ov ≥ Th_Sen, the fan power is increased by 5-10%, and the time duration for increasing the drying fan power is Lv_tp. When 1.0 ≤ N_Ov: Th_Sen < 1.2, then set Lv_tp to 0.5tp; When 1.2 ≤ N_Ov: Th_Sen < 1.4, then set Lv_tp to 0.6tp; When 1.4 ≤ N_Ov: Th_Sen < 1.6, then set Lv_tp to 0.7tp; When 1.6 ≤ N_Ov: Th_Sen, then set Lv_tp to 0.8tp; The drying fan can be any one of the following: a radial flow fan, a radial flow fan, or a ventilation fan.

4. An adaptive and intelligent fish feed drying system, characterized in that, The adaptive control intelligent drying system for fish feed includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the adaptive control intelligent drying method for fish feed according to any one of claims 1-3. The adaptive control intelligent drying system for fish feed operates on a desktop computer, a laptop computer, a handheld computer, or a cloud data center computing device.

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