Grain loss detection method and device, electronic equipment and readable storage medium
By performing spectrum analysis and fast Fourier transformation on the grain vibration signal, the grain species is automatically identified and the detection mode is switched, which solves the problem of repeatedly switching detection modes in the existing technology, and improves the harvest efficiency of agricultural machinery.
Patent Information
- Application Number
- CN202411733780.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-03-04
AI Technical Summary
When facing complex crop planting scenarios, existing grain loss detection methods require operators to repeatedly switch detection modes, resulting in low harvest efficiency of agricultural machinery.
By performing spectrum analysis of the collected grain vibration signals, the fast Fourier transform algorithm is used to automatically identify the grain species, and the loss detection mode is automatically switched according to the identified species.
It realizes automatic identification of grain species and switching detection modes in complex crop planting scenarios, improving the operating efficiency of agricultural machinery harvest.
Smart Images
Figure CN119555800A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of agricultural machinery, and in particular to a grain loss detection method, device, electronic device and readable storage medium. Background Art
[0002] With the development of automated agriculture, grain loss detection is used to monitor and evaluate the amount of grain loss during the harvesting process. The purpose is to optimize the process of agricultural machinery harvesting operations to improve harvesting efficiency, reduce food crop waste, and thus improve agricultural production efficiency.
[0003] The existing grain loss detection method requires operators to observe the crop types of harvested grains and manually input the crop types into the detection equipment to switch the grain loss detection mode for different grain types before performing grain loss detection. When faced with complex crop planting scenarios such as intercropping, operators are required to repeatedly switch grain loss detection modes, resulting in low efficiency of agricultural machinery harvesting. Summary of the invention
[0004] In view of this, the embodiments of the present application provide at least a grain loss detection method, device, electronic device and readable storage medium. By performing spectral analysis on the collected grain vibration signals, the grain type can be automatically identified and the corresponding loss detection mode can be switched, thereby improving the operation efficiency of agricultural machinery harvesting.
[0005] This application mainly includes the following aspects:
[0006] In a first aspect, an embodiment of the present application provides a grain loss detection method, which is applied to a grain loss detection system; the grain loss detection system includes an acceleration sensor module and a data processing module; the method includes:
[0007] According to the preset sampling frequency, the target vibration signal collected by the acceleration sensor module is collected;
[0008] For any sampling moment, based on the target vibration signal collected at the sampling moment and a fast Fourier transform algorithm, determine a target frequency domain signal corresponding to the target vibration signal;
[0009] Determining the target grain type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each grain type;
[0010] Based on the characteristic frequency spectrum of the target grain type, the grain loss detection mode is adjusted, and grain loss detection is performed according to the grain loss detection mode corresponding to the target grain type.
[0011] In a second aspect, an embodiment of the present application further provides a grain loss detection device, which is applied to a grain loss detection system; the grain loss detection system includes an acceleration sensor module and a data processing module; the grain loss detection device includes:
[0012] A signal acquisition module, used to acquire the target vibration signal acquired by the acceleration sensor module according to a preset sampling frequency;
[0013] A first determination module is used to determine, for any sampling moment, a target frequency domain signal corresponding to the target vibration signal based on the target vibration signal collected at the sampling moment and a fast Fourier transform algorithm;
[0014] A second determination module is used to determine the target cereal type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each cereal type;
[0015] The mode adjustment module is used to adjust the grain loss detection mode based on the characteristic frequency spectrum of the target grain type, and perform grain loss detection according to the grain loss detection mode corresponding to the target grain type.
[0016] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the grain loss detection method as described above.
[0017] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the grain loss detection method as described above are executed.
[0018] The embodiment of the present application provides a grain loss detection method, device, electronic device and readable storage medium, wherein the method is applied to a grain loss detection system composed of an acceleration sensor module and a data processing module, including: collecting a target vibration signal collected by the acceleration sensor module; for any sampling moment, based on the target vibration signal collected at the sampling moment and the fast Fourier transform algorithm, determining the target frequency domain signal corresponding to the target vibration signal; based on the target frequency domain signal and the characteristic spectrum of each preset grain type, determining the target grain type corresponding to the target frequency domain signal; based on the characteristic spectrum of the target grain type, adjusting the grain loss detection mode, and performing grain loss detection according to the grain loss detection mode corresponding to the target grain type. In this way, by performing spectrum analysis on the collected grain vibration signal, the grain type can be automatically identified and the corresponding loss detection mode can be switched, thereby improving the operation efficiency of agricultural machinery harvesting.
[0019] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0021] Figure 1 A flow chart of a grain loss detection method provided in an embodiment of the present application is shown;
[0022] Figure 2 One of the functional module diagrams of a grain loss detection device provided in an embodiment of the present application is shown;
[0023] Figure 3 A second functional module diagram of a grain loss detection device provided in an embodiment of the present application is shown;
[0024] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION
[0025] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.
[0026] To facilitate the understanding of the present application, the technical solution provided by the present application is described in detail below in conjunction with specific embodiments.
[0027] Considering that the existing grain loss detection method requires operators to repeatedly switch grain loss detection modes when facing complex crop planting scenarios such as intercropping, resulting in low efficiency of agricultural machinery harvesting. Based on this, the embodiment of the present application provides a grain loss detection method, which can automatically identify the grain type and switch the corresponding loss detection mode by performing spectral analysis on the collected grain vibration signal, thereby improving the efficiency of agricultural machinery harvesting.
[0028] See also Figure 1 , Figure 1 The flowchart of a grain loss detection method provided in an embodiment of the present application. The grain loss detection method is applied to a grain loss detection control system; the grain loss detection system includes an acceleration sensor module and a data processing module. Figure 1 As shown, the grain loss detection method provided in the embodiment of the present application comprises the following steps:
[0029] S101, collecting a target vibration signal collected by an acceleration sensor module according to a preset sampling frequency.
[0030] Here, in the data processing module of the grain loss detection system, the target vibration signal collected by the acceleration sensor module is collected according to the preset sampling frequency. Specifically, the acceleration sensor module installed at the impurity discharge port of the harvester faces the straw blown out by the fan at a certain angle during the harvesting operation (the straw is mixed with a certain amount of grain particles). The lost grain particles and part of the straw (because a certain number of comb teeth are generally installed in front of the sensor sensing plate, and the comb teeth will block most of the straw) continuously hit the sensing plate of the acceleration sensor module. The acceleration sensor module sends the detected real-time signal to the data processing module. The data processing module calculates the number of grain particles lost per unit time through an algorithm, and then calculates the real-time harvest loss rate through an algorithm. The harvest loss rate includes the impurity loss rate and the entrainment loss rate.
[0031] S102, for any sampling moment, based on the target vibration signal collected at the sampling moment and a fast Fourier transform algorithm, determining a target frequency domain signal corresponding to the target vibration signal.
[0032] Here, for any sampling moment, the time domain signal of the target vibration signal is converted into a frequency domain signal by means of a fast Fourier transform algorithm, and a target frequency domain signal corresponding to the target vibration signal is determined.
[0033] The specific formula is as follows:
[0034]
[0035] F(x)=k1f1+k2f2+k3f3.
[0036] In the formula, a0 is the constant term coefficient, a n is the cosine term amplitude coefficient, b n is the amplitude coefficient of the sinusoidal term, L is the period, n is the number of harmonics in the series, k is the number of terms in the Fourier series, k1, k2, k3 are the empirical coefficients measured in experiments, f1, f2, f3 are the body noise resonance frequency, straw vibration frequency and grain vibration frequency of various machines measured in experiments.
[0037] S103, determining the target cereal type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each cereal type.
[0038] Here, the embodiment of the present application obtains the characteristic spectrum of each type of grain through a large number of preliminary experiments. Specifically, for different grain harvesting operations, the vibration signal of the corresponding loss grain impact loss detection sensor is collected, and signal processing (fast Fourier transform) is performed on it to convert the time domain vibration signal into a frequency domain vibration signal. After obtaining the frequency domain characteristics unique to this type of grain, it is solidified in the memory. In this way, after a large number of experimental tests, the vibration spectrum of typical grains such as wheat, corn, rice, soybeans, millet, etc. will be obtained and solidified in the memory respectively. During the actual harvesting operation, the acceleration sensor module will receive the impact of the harvested grains and generate corresponding vibration signals. After signal processing, its characteristic spectrum is obtained. By comparing the spectrum with the typical grain spectrum in the memory, it can be identified what kind of grain is harvested, that is, the target grain type corresponding to the target frequency domain signal is determined.
[0039] S104, adjusting a grain loss detection mode based on a characteristic frequency spectrum of the target grain type, and performing grain loss detection according to the grain loss detection mode corresponding to the target grain type.
[0040] Here, the grain loss detection mode is adjusted according to the characteristic frequency spectrum of the target grain type, and grain loss detection is performed according to the grain loss detection mode corresponding to the target grain type.
[0041] Further, the determining the target grain type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each grain type includes:
[0042] Step a1, obtaining the amplitude and frequency range of the target frequency domain signal.
[0043] Here, the amplitude and frequency range of the target frequency domain signal are obtained as typical features of the target frequency domain signal.
[0044] Step a2: based on the amplitude and frequency range of the target frequency domain signal and the amplitude and frequency range of the preset characteristic spectrum of each cereal type, determine the characteristic spectrum that best matches the target frequency domain signal as the target characteristic spectrum.
[0045] Here, the amplitude and frequency range of the target frequency domain signal are compared with the amplitude and frequency range of the preset characteristic spectrum of each grain type, and the characteristic spectrum that best matches the target frequency domain signal is determined as the target characteristic spectrum.
[0046] Step a3: determining the target grain type corresponding to the target characteristic spectrum as the target grain type corresponding to the target frequency domain signal.
[0047] Here, the target characteristic spectrum corresponds to the grain type, which is determined as the target grain type corresponding to the target frequency domain signal.
[0048] In a possible implementation manner, after determining the target grain type corresponding to the target frequency domain signal, the method further includes:
[0049] Step b1, determining a target screening frequency range based on a characteristic frequency spectrum of the target cereal type.
[0050] Here, according to the characteristic spectrum of the target cereal type, the frequency range of the characteristic spectrum of the target cereal type is determined as the target screening frequency range, so that the frequency domain signal of the target cereal type can be obtained by subsequently screening only the frequency domain signal in this frequency range through a filtering algorithm.
[0051] Step b2: filtering the target frequency domain signal based on the target screening frequency range, filtering out the target frequency domain signal outside the target screening frequency range, and obtaining the frequency domain signal of the target grain type.
[0052] Here, the target frequency domain signal is filtered based on the target screening frequency range, and the target frequency domain signal outside the target screening frequency range is filtered out to obtain the frequency domain signal of the target grain type. That is, the precise grain vibration component can be obtained by only detecting the signal in the characteristic spectrum range, and then the degree of grain loss can be accurately fed back.
[0053] In a possible implementation, the method further includes:
[0054] Step c1, determining a second target screening frequency range based on a preset characteristic frequency spectrum of the vibration noise of the agricultural machinery and a vibration spectrum of the straw.
[0055] Here, based on the preset vibration noise characteristic spectrum of the agricultural machinery and the vibration spectrum of the straw, the second target screening frequency range is determined. Specifically, since the inherent vibration noise frequency of each machine is basically fixed, the vibration frequency of each grain straw also has a certain range, which is quite different from the vibration frequency of the grain hitting sensor, so the vibration noise component of the machine and the vibration component of the straw can be filtered out by the filter filtering method. After filtering out the vibration noise component of the machine and the vibration component of the straw, the accurate grain vibration component is obtained.
[0056] Step c2: filtering the target frequency domain signal based on the second target screening frequency range, filtering out the target frequency domain signal in the second target screening frequency range, and obtaining the frequency domain signal of the target grain type.
[0057] Here, the target frequency domain signal is filtered based on the second target screening frequency range, and the target frequency domain signal in the second target screening frequency range is filtered out to obtain the frequency domain signal of the target grain type. That is, the machine vibration noise signal and the straw impact vibration signal are filtered out, and only the vibration signal of grain particle impact is retained. In this way, the processed vibration signal is a pure grain impact vibration signal, which can accurately feedback the degree of grain loss.
[0058] Further, the adjusting the grain loss detection mode based on the characteristic spectrum of the target grain type includes:
[0059] Step d1, determining the detection sensitivity corresponding to the target grain type according to the frequency range of the characteristic spectrum of the target grain type and the mapping relationship between the preset frequency range and the detection sensitivity.
[0060] Here, when dealing with different types of grains, different detection sensitivities need to be set. For example, when detecting large-grain grains such as corn, a lower detection sensitivity needs to be used to ensure the service life of the equipment, while when detecting small-grain grains such as sesame, a higher detection sensitivity needs to be used to ensure detection accuracy and avoid missed detection. The existing grain loss detection method manually sets different detection sensitivities to adapt to the detection of different grains. When facing complex crop planting scenarios such as intercropping, this method requires operators to repeatedly switch grain loss detection modes, resulting in low operating efficiency of agricultural machinery harvesting. Therefore, the embodiment of the present application determines the detection sensitivity corresponding to the target grain type according to the frequency range of the characteristic spectrum of the target grain type, and the mapping relationship between the preset frequency range and the detection sensitivity. In this way, the corresponding detection sensitivity can be automatically identified and switched, thereby improving the operating efficiency of agricultural machinery harvesting.
[0061] Step d2, adjusting the detection sensitivity of the acceleration sensor module based on the detection sensitivity, and adjusting the grain loss detection mode.
[0062] Here, the detection sensitivity is sent to the acceleration sensor module to adjust the detection sensitivity of the acceleration sensor module and adjust the grain loss detection mode.
[0063] Furthermore, the determining of the detection sensitivity corresponding to the target grain type according to the frequency range of the characteristic spectrum of the target grain type and the mapping relationship between the preset frequency range and the detection sensitivity includes:
[0064] Step e1, obtaining an initial frequency range corresponding to the current detection sensitivity of the acceleration sensor module according to a mapping relationship between a preset frequency range and detection sensitivity.
[0065] Here, the detection sensitivity adjustment corresponding to the target grain type can be divided into two parts: rough adjustment and fine adjustment. First, the detection sensitivity corresponding to the preset target grain type is roughly adjusted, and then the detection sensitivity corresponding to the target grain type is finely adjusted according to the mapping relationship between the preset frequency range and the detection sensitivity to meet the real-time environmental conditions. After the rough adjustment, the initial frequency range corresponding to the current detection sensitivity of the acceleration sensor module is first determined according to the mapping relationship between the preset frequency spectrum range and the detection sensitivity.
[0066] Step e2: if the frequency range of the characteristic spectrum of the target cereal type is higher than the initial frequency range, determine to increase the detection sensitivity corresponding to the acceleration sensor module.
[0067] Here, if the frequency range of the characteristic spectrum of the target grain type is higher than the initial frequency range, it means that the grain size of the target grain type is smaller than the existing detection sensitivity, and the detection sensitivity corresponding to the acceleration sensor module needs to be increased to improve the detection accuracy.
[0068] Step e3: if the frequency range of the characteristic spectrum of the target cereal type is lower than the initial frequency range, determine to reduce the detection sensitivity corresponding to the acceleration sensor module.
[0069] Here, if the frequency range of the characteristic spectrum of the target grain type is lower than the initial frequency range, it means that the grain grain size of the target grain type is larger than the existing detection sensitivity, and the detection sensitivity corresponding to the acceleration sensor module needs to be reduced to prevent the grain grains from damaging the acceleration sensor module.
[0070] Furthermore, in the process of performing grain loss detection according to the grain loss detection mode corresponding to the target grain type, the method further includes:
[0071] Step f1: If the grain loss value obtained by detecting the frequency domain signal of the target grain type is greater than a preset grain loss value threshold, it is determined that an agricultural machinery failure occurs.
[0072] Here, when it is detected that the grain loss value is greater than the preset grain loss threshold, it means that the amount of grain lost at the harvester's impurity outlet exceeds the normal production loss, and it is determined that the agricultural machinery has a fault.
[0073] Step f2, generating a fault prompt signal to remind the user of the occurrence of agricultural machinery fault.
[0074] Here, a fault prompt signal is generated to remind the user of the occurrence of a fault in the agricultural machinery by giving an audible and visual warning with a warning light or outputting a prompt message on the agricultural machinery operation interface.
[0075] The embodiment of the present application provides a grain loss detection method, which is applied to a grain loss detection system composed of an acceleration sensor module and a data processing module, including: collecting a target vibration signal collected by the acceleration sensor module; for any sampling moment, based on the target vibration signal collected at the sampling moment and a fast Fourier transform algorithm, determining a target frequency domain signal corresponding to the target vibration signal; based on the target frequency domain signal and the characteristic spectrum of each preset grain type, determining the target grain type corresponding to the target frequency domain signal; based on the characteristic spectrum of the target grain type, adjusting the grain loss detection mode, and performing grain loss detection according to the grain loss detection mode corresponding to the target grain type. In this way, by performing spectrum analysis on the collected grain vibration signal, the grain type can be automatically identified and the corresponding loss detection mode can be switched, thereby improving the operation efficiency of agricultural machinery harvesting.
[0076] Based on the same application concept, the embodiments of the present application also provide a grain loss detection device corresponding to the grain loss detection method provided in the above embodiments. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the grain loss detection method in the above embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.
[0077] See also Figure 2 , Figure 2 This is one of the functional module diagrams of a grain loss detection device provided in an embodiment of the present application. The grain loss detection device is applied to a grain loss detection system composed of an acceleration sensor module and a data processing module. Figure 3 As shown, the grain loss detection device 200 includes:
[0078] The signal acquisition module 210 is used to acquire the target vibration signal acquired by the acceleration sensor module according to a preset sampling frequency.
[0079] The first determination module 220 is used to determine, at any sampling moment, a target frequency domain signal corresponding to the target vibration signal based on the target vibration signal collected at the sampling moment and a fast Fourier transform algorithm.
[0080] The second determination module 230 is used to determine the target cereal type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each cereal type.
[0081] The mode adjustment module 240 is used to adjust the grain loss detection mode based on the characteristic frequency spectrum of the target grain type, and perform grain loss detection according to the grain loss detection mode corresponding to the target grain type.
[0082] Further, when the second determination module 230 is used to determine the target cereal type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each cereal type, the second determination module 230 is specifically used to:
[0083] Obtaining the amplitude and frequency range of the target frequency domain signal;
[0084] Based on the amplitude and frequency range of the target frequency domain signal and the amplitude and frequency range of the preset characteristic spectrum of each grain type, determining the characteristic spectrum that best matches the target frequency domain signal as the target characteristic spectrum;
[0085] The target characteristic spectrum corresponds to the type of grain, and is determined as the target grain type corresponding to the target frequency domain signal.
[0086] In one possible implementation, see Figure 3 , Figure 3 This is a second functional module diagram of a grain loss detection device provided in an embodiment of the present application. Figure 3 As shown, the grain loss detection device 200 also includes:
[0087] The third determination module 250 is used to determine a target screening frequency range based on the characteristic frequency spectrum of the target cereal type.
[0088] The filtering processing module 260 is used to filter the target frequency domain signal based on the target screening frequency range, filter out the target frequency domain signal outside the target screening frequency range, and obtain the frequency domain signal of the target grain type.
[0089] In a possible implementation manner, the third determination module 250 is further used to determine a second target screening frequency range based on a preset characteristic spectrum of vibration noise of the agricultural machinery and a preset spectrum of straw vibration.
[0090] The filtering processing module 260 is further used to filter the target frequency domain signal based on the second target screening frequency range, filter out the target frequency domain signal in the second target screening frequency range, and obtain the frequency domain signal of the target grain type.
[0091] Furthermore, when the mode adjustment module 240 is used to adjust the grain loss detection mode based on the characteristic spectrum of the target grain type, the mode adjustment module 240 is specifically used to:
[0092] Determine the detection sensitivity corresponding to the target grain type according to the frequency range of the characteristic spectrum of the target grain type and the mapping relationship between the preset frequency range and the detection sensitivity;
[0093] The detection sensitivity of the acceleration sensor module is adjusted based on the detection sensitivity to adjust the grain loss detection mode.
[0094] Further, when the mode adjustment module 240 is used to determine the detection sensitivity corresponding to the target grain type according to the frequency range of the characteristic spectrum of the target grain type and the mapping relationship between the preset frequency range and the detection sensitivity, the mode adjustment module 240 is specifically used to:
[0095] According to the mapping relationship between the preset frequency range and the detection sensitivity, an initial frequency range corresponding to the current detection sensitivity of the acceleration sensor module is obtained;
[0096] If the frequency range of the characteristic spectrum of the target grain type is higher than the initial frequency range, determining to increase the detection sensitivity corresponding to the acceleration sensor module;
[0097] If the frequency range of the characteristic frequency spectrum of the target cereal type is lower than the initial frequency range, it is determined to reduce the detection sensitivity corresponding to the acceleration sensor module.
[0098] Furthermore, in the process of performing grain loss detection according to the grain loss detection mode corresponding to the target grain type, the mode adjustment module 240 is specifically used to:
[0099] If the grain loss value obtained by detecting the frequency domain signal of the target grain type is greater than a preset grain loss value threshold, it is determined that an agricultural machinery failure occurs;
[0100] Generate a fault prompt signal to warn of agricultural machinery failure.
[0101] The embodiment of the present application provides a grain loss detection device, which is applied to a grain loss detection system composed of an acceleration sensor module and a data processing module, including: a signal acquisition module, which is used to acquire a target vibration signal acquired by the acceleration sensor module; a first determination module, which is used to determine the target frequency domain signal corresponding to the target vibration signal based on the target vibration signal acquired at the sampling moment and a fast Fourier transform algorithm at any sampling moment; a second determination module, which is used to determine the target grain type corresponding to the target frequency domain signal based on the target frequency domain signal and the characteristic spectrum of each preset grain type; a mode adjustment module, which is used to adjust the grain loss detection mode based on the characteristic spectrum of the target grain type, and perform grain loss detection according to the grain loss detection mode corresponding to the target grain type. In this way, by performing spectrum analysis on the acquired grain vibration signal, the grain type can be automatically identified and the corresponding loss detection mode can be switched, thereby improving the operation efficiency of agricultural machinery harvesting.
[0102] Based on the same application idea, please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .
[0103] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 through the bus 430. When the processor 410 is running, the machine-readable instructions execute the steps of the grain loss detection method provided in the above embodiment. The specific implementation method can be found in the method embodiment, which will not be repeated here.
[0104] Based on the same application concept, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the grain loss detection method provided in the above embodiment are executed. The specific implementation method can be found in the method embodiment, which will not be repeated here.
[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0106] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, 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 through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.
[0107] The units described 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.
[0108] In addition, each functional unit in the embodiments provided in 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.
[0109] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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 the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0110] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.
[0111] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A method for detecting grain loss, characterized in that: Applied to a grain loss detection system; the grain loss detection system comprises an acceleration sensor module and a data processing module; the method comprises: According to the preset sampling frequency, the target vibration signal collected by the acceleration sensor module is collected; For any sampling moment, based on the target vibration signal collected at the sampling moment and a fast Fourier transform algorithm, determine a target frequency domain signal corresponding to the target vibration signal; Determining the target grain type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each grain type; Based on the characteristic frequency spectrum of the target grain type, the grain loss detection mode is adjusted, and grain loss detection is performed according to the grain loss detection mode corresponding to the target grain type.
2. The grain loss detection method according to claim 1, characterized in that: The step of determining the target grain type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each grain type includes: Obtaining the amplitude and frequency range of the target frequency domain signal; Based on the amplitude and frequency range of the target frequency domain signal and the amplitude and frequency range of the preset characteristic spectrum of each cereal type, determining the characteristic spectrum that best matches the target frequency domain signal as the target characteristic spectrum; The target characteristic spectrum corresponds to the type of grain, and is determined as the target grain type corresponding to the target frequency domain signal.
3. The grain loss detection method according to claim 1, characterized in that: After determining the target grain type corresponding to the target frequency domain signal, the method further includes: determining a target screening frequency range based on a characteristic frequency spectrum of the target cereal species; The target frequency domain signal is filtered based on the target screening frequency range, and the target frequency domain signal outside the target screening frequency range is filtered out to obtain the frequency domain signal of the target grain type.
4. The grain loss detection method according to claim 3, characterized in that: The method further comprises: Based on the preset characteristic spectrum of the vibration noise of the agricultural machinery and the spectrum of the straw vibration, the second target screening frequency range is determined; The target frequency domain signal is filtered based on the second target screening frequency range, the target frequency domain signal in the second target screening frequency range is filtered out, and the frequency domain signal of the target grain type is obtained.
5. The grain loss detection method according to claim 1, characterized in that: The step of adjusting the grain loss detection mode based on the characteristic spectrum of the target grain type includes: Determine the detection sensitivity corresponding to the target grain type according to the frequency range of the characteristic spectrum of the target grain type and the mapping relationship between the preset frequency range and the detection sensitivity; The detection sensitivity of the acceleration sensor module is adjusted based on the detection sensitivity to adjust the grain loss detection mode.
6. The grain loss detection method according to claim 5, characterized in that: The step of determining the detection sensitivity corresponding to the target grain type according to the frequency range of the characteristic spectrum of the target grain type and the mapping relationship between the preset frequency range and the detection sensitivity includes: According to the mapping relationship between the preset frequency range and the detection sensitivity, an initial frequency range corresponding to the current detection sensitivity of the acceleration sensor module is obtained; If the frequency range of the characteristic spectrum of the target grain type is higher than the initial frequency range, determining to increase the detection sensitivity corresponding to the acceleration sensor module; If the frequency range of the characteristic frequency spectrum of the target cereal type is lower than the initial frequency range, it is determined to reduce the detection sensitivity corresponding to the acceleration sensor module.
7. The grain loss detection method according to claim 4, characterized in that: During the grain loss detection process according to the grain loss detection mode corresponding to the target grain type, the method further includes: If the grain loss value obtained by detecting the frequency domain signal of the target grain type is greater than a preset grain loss value threshold, it is determined that an agricultural machinery failure occurs; Generate a fault prompt signal to warn of agricultural machinery failure.
8. A grain loss detection device, characterized in that: Applied to a grain loss detection system; the grain loss detection system comprises an acceleration sensor module and a data processing module; the grain loss detection device comprises: A signal acquisition module, used to acquire the target vibration signal acquired by the acceleration sensor module according to a preset sampling frequency; A first determination module is used to determine, for any sampling moment, a target frequency domain signal corresponding to the target vibration signal based on the target vibration signal collected at the sampling moment and a fast Fourier transform algorithm; A second determination module is used to determine the target cereal type corresponding to the target frequency domain signal based on the target frequency domain signal and the preset characteristic frequency spectra of each cereal type; The mode adjustment module is used to adjust the grain loss detection mode based on the characteristic frequency spectrum of the target grain type, and perform grain loss detection according to the grain loss detection mode corresponding to the target grain type.
9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and the machine-readable instructions are executed by the processor to execute the steps of the grain loss detection method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the grain loss detection method according to any one of claims 1 to 7 are executed.