Chlorophyll fluorescence observation system based on lidar

Abstract

The present invention relates to the technical field of vegetation remote sensing observation, and in particular to a chlorophyll fluorescence observation system based on laser radar, comprising: a target assessment module, for responding to observation assessment conditions to determine an observation analysis strategy for each observation target; an observation analysis module, for determining a mirror dominant direction of the observation target, and responding to an inversion execution condition to determine an inversion execution strategy for the observation target; a dominant inversion module, for determining a set of estimated execution bands, and determining observation estimated data based on polarization reflection parameters of the estimated execution bands; a multi-parameter inversion module, for determining an inversion weight coefficient for each observation band, and responding to a weight analysis condition to determine whether to adjust the inversion weight index for each observation band; and an auxiliary analysis module, for responding to the auxiliary analysis condition to determine an auxiliary analysis mode for the observation target. The present invention improves the reliability of fluorescence observation results of the observation target.

Description

Technical Field

[0001] The present invention relates to the technical field of vegetation remote sensing observation, and in particular to a chlorophyll fluorescence observation system based on laser radar. Background Art

[0002] Nowadays, chlorophyll fluorescence observation and research on vegetation crops are widely used in tracking photosynthesis and monitoring vegetation stress. Chlorophyll fluorescence observation can effectively reflect the actual photosynthesis of crops, and thus provide effective data basis for the subsequent process of determining the growth status of vegetation crops. However, during the chlorophyll fluorescence observation of vegetation crops, the leaf reflection and branch and leaf status of the crops can interfere with the obtained fluorescence data. Therefore, how to effectively analyze the fluorescence data by analyzing the actual status and directional reflection characteristics of vegetation crops to ensure the quality of the assessment of the photosynthetic productivity of crops is a problem that needs to be solved urgently by technical personnel in this field.

[0003] Chinese Patent Publication No. CN108693154A discloses a method for accurately inverting sunlight-induced chlorophyll fluorescence (chlorophyll fluorescence) of canopy yin and yang leaves using spectral data acquired from a multi-angle observation system. This method falls within the research field of vegetation remote sensing parameter acquisition methods. The method includes: establishing a multi-angle ultra-high-spectral observation system; acquiring multi-angle ultra-high-spectral data; calculating solar incident and canopy reflectance radiance; calculating canopy reflectance and inverting chlorophyll fluorescence; observing leaf reflectance using a leaf clip; calculating the ratio of yin and yang leaves at different observation angles using the ratio of canopy reflectance to leaf reflectance, combined with a geometric optical model; and obtaining the fluorescence of yin and yang leaves using a least-squares fitting method. However, this method suffers from the following issues: it fails to perform targeted analysis of the fluorescence data acquired during observation based on the crop state and directional reflectance characteristics of the observed target, resulting in poor reliability of subsequent fluorescence observation results for vegetation crops. Summary of the Invention

[0004] To this end, the present invention provides a chlorophyll fluorescence observation system based on lidar to overcome the problem in the prior art that the crop fluorescence data obtained during the observation process is not analyzed in a targeted manner based on the crop status and directional reflection characteristics of different observation targets, resulting in poor reliability of subsequent fluorescence observation results for vegetation crops.

[0005] To achieve the above objectives, the present invention provides a chlorophyll fluorescence observation system based on laser radar, comprising:

[0006] A target assessment module is used to respond to observation assessment conditions to determine the observation analysis strategy for each observation target, which is to use an observation feature analysis method to perform an inversion and prediction analysis on the fluorescence observation data of the observation target, or to use an auxiliary compliance analysis method to perform a rationality analysis on the fluorescence observation data of the observation target;

[0007] an observation analysis module connected to the target evaluation module to determine the dominant mirror direction of the observed target and, in response to an inversion execution condition, determine an inversion execution strategy for the observed target, such as determining an estimated execution band set based on an approximate stability index and a proportion of a dominant reflection component, or determining an inversion weight coefficient for each observation band based on an observed variation difference and a feature overlap;

[0008] A leading inversion module, connected to the observation analysis module, for determining a set of estimation execution bands and determining observation estimation data based on polarization reflection parameters of the estimation execution bands;

[0009] a multi-parameter inversion module, connected to the observation analysis module, for determining an inversion weight coefficient for each observation band and, in response to a weight analysis condition, determining whether to adjust the inversion weight index for each observation band;

[0010] An auxiliary analysis module is connected to the target evaluation module and is used to respond to the auxiliary analysis conditions to determine whether the auxiliary analysis method of the observation target in the second-class observation analysis state is to determine whether to issue a growth restriction warning for the observation target based on the reference observation stability coefficient, or to determine the observation quality index of the observation target based on the conflict difference ratio and the change association parameter.

[0011] Furthermore, the target evaluation module determines the reflection complexity coefficient of each observed target based on the echo difference parameter and the echo standard difference parameter;

[0012] The target evaluation module determines the observation complexity coefficient of each observation target according to the target element richness index and the canopy complexity index.

[0013] Furthermore, if the observation evaluation condition responded by the target evaluation module is that the observed target is in a type of observation analysis state, the observation analysis module is determined to perform inversion and prediction analysis on the fluorescence observation data of the observed target using an observation feature analysis method;

[0014] The observation analysis module responds to the characteristic analysis conditions, determines the mirror dominant direction of the observation target according to the observed polarization parameters, and determines the inversion execution strategy of the observation target based on the proportion of the dominant direction;

[0015] The characteristic analysis condition is that the target evaluation module determines that the observation analysis module uses the observation characteristic analysis method to perform inversion and prediction analysis on the fluorescence observation data of the observation target, and the type of observation analysis state is that the reflection complexity coefficient of the observation target is greater than the preset reflection complexity coefficient or the observation complexity coefficient is greater than the preset observation complexity coefficient.

[0016] Furthermore, if the inversion execution condition responded by the observation analysis module is that the dominant direction ratio is greater than a preset dominant direction ratio, the dominant inversion module determines the estimated execution band set based on the approximate stability index and the dominant reflection component ratio;

[0017] The leading inversion module responds to the estimation execution condition and determines the observation estimation data based on the polarization reflection parameters of each estimation execution band in the estimation execution band set;

[0018] The estimation execution condition is that the observation target completes the determination of the estimation execution band set.

[0019] Furthermore, if the inversion execution condition responded by the observation analysis module is that the dominant direction ratio is less than or equal to the preset dominant direction ratio, the multi-parameter inversion module is determined to determine the inversion weight coefficient of each observation band based on the observation change difference and the feature overlap;

[0020] The inversion weight coefficient is negatively correlated with the observed change difference, and the inversion weight coefficient is positively correlated with the feature coincidence.

[0021] Furthermore, the multi-parameter inversion module responds to the multi-parameter execution conditions, performs band weight analysis for the dominant direction of each mirror, and determines whether to adjust the inversion weight index for each observation band according to the proportion of the dominant reflection component;

[0022] The weight analysis condition of the multi-parameter inversion module response is that if the proportion of the dominant reflection component in the observation band is less than the preset dominant reflection component proportion, the inversion weight index of the observation band is increased according to the dominant reflection component proportion;

[0023] The multi-parameter execution condition is that the observation analysis module determines the multi-parameter inversion module to determine the inversion weight coefficient of each observation band.

[0024] Furthermore, if the observation evaluation condition responded by the target evaluation module is that the observed target is in the second-category observation analysis state, the auxiliary analysis module is determined to perform a rationality analysis on the fluorescence observation data of the observed target using an auxiliary compliance analysis method;

[0025] The auxiliary analysis module responds to the auxiliary correction condition and determines the auxiliary analysis method based on the correlation of the distribution characteristics of the conflicting data points;

[0026] The auxiliary correction condition is that the target evaluation module determines that the auxiliary analysis module uses an auxiliary compliance analysis method to correct the observation data of the observation target, and the second type of observation analysis state is that the reflection complexity coefficient of the observation target is less than or equal to the preset reflection complexity coefficient and the observation complexity coefficient is less than or equal to the preset observation complexity coefficient.

[0027] Furthermore, if the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation of the observed target is greater than the preset distribution feature correlation, the observation stability coefficient of each conflicting data point is detected, and it is determined whether to issue a growth restriction warning for the observed target based on the reference observation stability coefficient;

[0028] The observation stability coefficient is determined according to the intensity change degree of the fluorescence intensity of the reflection data acquired each time during the observation evaluation phase.

[0029] Furthermore, if the restriction analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is less than or equal to the preset reference observation stability coefficient, it is determined that a growth restriction warning is issued for the observation target;

[0030] If the restricted analysis condition of the auxiliary analysis module response is that the reference observation stability coefficient is greater than the preset reference observation stability coefficient, the observation quality index of the observation target is determined based on the conflict difference ratio;

[0031] The observation quality index is negatively correlated with the proportion of conflict differences.

[0032] Furthermore, if the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation of the observed target is less than or equal to the preset distribution feature correlation, the observation quality index of the observed target is determined according to the conflict difference ratio and the change association parameter;

[0033] The observation quality index is positively correlated with the change association parameter, and is negatively correlated with the conflict difference ratio.

[0034] Compared with the prior art, the beneficial effect of the present invention lies in that the technical solution of the present invention determines the observation and analysis status of each observation target based on the reflection complexity coefficient and the observation complexity coefficient, and determines the observation and analysis strategy of each observation target based on the observation and analysis status, so that the analysis process performed on the acquired observation data is more in line with the actual state of the observation target. The present invention ensures the reliability of the fluorescence observation results of the observation target, thereby improving the estimated quality of the photosynthetic productivity of vegetation crops.

[0035] Furthermore, the present invention determines the observation and analysis status of each observed crop based on the reflection complexity coefficient and the observation complexity coefficient, so as to characterize the degree to which the observation results are affected by the leaf directional reflection characteristics during the chlorophyll fluorescence observation of the observation target and the degree to which the branch and leaf segmentation interferes with the analysis process of the observation results, thereby determining the reliability of the obtained observation data and the dominant cause of the deviation, providing a preliminary basis for targeted determination of the observation and analysis strategy for each observation target, and ensuring that the executed analysis process is more in line with the actual state of the observation target.

[0036] Furthermore, in the present invention, for the observation target in a type of observation and analysis state, the observation feature analysis method is used to perform inversion and prediction analysis on the fluorescence observation data of the observation target, and a targeted inversion execution strategy is determined according to the proportion of the dominant direction, so that the inversion process of the chlorophyll content based on the reflection data obtained by the observation is more in line with the actual scenario. The present invention improves the reliability of the inversion results of the chlorophyll content of the observation target.

[0037] Furthermore, in the present invention, for observation targets in the second category of observation and analysis status, an auxiliary compliance analysis method is used to perform rationality analysis on the fluorescence observation data of the observation targets. The observation data of such observation targets are not continuously disturbed. If there are conflicting data points, a rationality analysis is required. The auxiliary analysis method is determined according to the correlation of the distribution characteristics of the conflicting data points. If the correlation of the distribution characteristics is large, it indicates that their growth restriction conditions are similar. According to their observation stability, it is determined whether there are growth restriction factors and whether to issue an early warning. If the correlation of the distribution characteristics is large or the observation stability is relatively good, a targeted judgment is made on the observation quality of the observation target. The present invention ensures the reliability of the fluorescence observation results of the observation target. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a module connection diagram of the chlorophyll fluorescence observation system based on laser radar of the present invention;

[0039] Figure 2 A flow chart of an object evaluation module of the present invention responding to observation evaluation conditions to determine an observation analysis strategy for each observation object;

[0040] Figure 3 A flowchart of an inversion execution strategy for determining an observation target by an observation analysis module in response to an inversion execution condition of the present invention;

[0041] Figure 4 This is a flow chart of the auxiliary analysis module of the present invention responding to auxiliary analysis conditions to determine the auxiliary analysis method. DETAILED DESCRIPTION

[0042] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0043] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0044] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0045] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0046] See also Figures 1 to 4 As shown, the present invention provides a chlorophyll fluorescence observation system based on laser radar, comprising:

[0047] A target assessment module is used to respond to observation assessment conditions to determine the observation analysis strategy for each observation target, which is to use an observation feature analysis method to perform an inversion and prediction analysis on the fluorescence observation data of the observation target, or to use an auxiliary compliance analysis method to perform a rationality analysis on the fluorescence observation data of the observation target;

[0048] an observation analysis module connected to the target evaluation module to determine the dominant mirror direction of the observed target and, in response to an inversion execution condition, determine an inversion execution strategy for the observed target, such as determining an estimated execution band set based on an approximate stability index and a proportion of a dominant reflection component, or determining an inversion weight coefficient for each observation band based on an observed variation difference and a feature overlap;

[0049] A leading inversion module, connected to the observation analysis module, for determining a set of estimation execution bands and determining observation estimation data based on polarization reflection parameters of the estimation execution bands;

[0050] a multi-parameter inversion module, connected to the observation analysis module, for determining an inversion weight coefficient for each observation band and, in response to a weight analysis condition, determining whether to adjust the inversion weight index for each observation band;

[0051] An auxiliary analysis module is connected to the target evaluation module and is used to respond to the auxiliary analysis conditions to determine whether the auxiliary analysis method of the observation target in the second-class observation analysis state is to determine whether to issue a growth restriction warning for the observation target based on the reference observation stability coefficient, or to determine the observation quality index of the observation target based on the conflict difference ratio and the change association parameter.

[0052] The present invention is used to correct the chlorophyll fluorescence observation data of the observation target to ensure accurate judgment of the growth status of the vegetation and crops. The target observation area in the present invention is the area covered by various types of vegetation and crops that need to be observed for fluorescence. The observation target in the present invention is the vegetation and crops that need to be observed for chlorophyll fluorescence. The fluorescence observation data obtained in the present invention includes but is not limited to the fluorescence reflection signal and the fluorescence intensity of the fluorescence reflection data.

[0053] The present invention applies a cyclic observation and evaluation cycle, the duration of which can be determined by the user. An observation and evaluation cycle is provided, which is 10 days. At the end of each observation and evaluation cycle, the target evaluation module determines the observation and analysis state of each observation target based on the reflection complexity coefficient and the observation complexity coefficient, and determines the observation and analysis strategy of each observation target based on the observation and analysis state.

[0054] The present invention uses several observation management records, and any one of the observation management records records the reflection complexity coefficient, observation complexity coefficient, observation polarization parameter, dominant direction ratio, dominant reflection component ratio, inversion band coefficient, observation conflict coefficient, distribution characteristic correlation, reference observation stability coefficient and observation quality index in the correction process of at least one chlorophyll fluorescence observation result of the observation target, and each observation management record corresponds to a qualified mark, which records whether the reliability of the fluorescence observation result of the observation target meets the user's requirements. It can be understood that the user can determine whether the reliability of the fluorescence observation result of the observation target meets the requirements based on the self-set indicators. For example, the self-set indicators can be but are not limited to the productivity assessment difference index. The productivity assessment difference index is the difference between the crop photosynthesis productivity determined based on the fluorescence observation data and the crop photosynthesis productivity determined based on the normalized vegetation index.

[0055] Specifically, the target evaluation module determines the reflection complexity coefficient of each observed target based on the echo difference parameter and the echo standard difference parameter;

[0056] The target evaluation module determines the observation complexity coefficient of each observation target according to the target element richness index and the canopy complexity index.

[0057] Among them, for a single observation target, the reflection complexity coefficient = ln (echo difference parameter × echo standard difference parameter), the echo difference parameter , m is the number of observation incident angles involved in monitoring the observation target, hj is the echo intensity measured at the jth observation incident angle, h0 is the average value of the echo intensities measured at each observation incident angle, the echo standard difference parameter is the average value of the standard difference values of each observation incident angle, for a single observation incident angle, the standard difference value is the absolute value of the difference between the echo intensity measured at the observation incident angle and the standard echo intensity of the observation incident angle, the standard echo intensity is the echo intensity of the standard reflector at the observation incident angle, the observation incident angle is the angle formed between the incident wave and the horizontal plane where the observation target is located, the echo intensity is the intensity of the reflected wave, how to detect the echo intensity and each observation incident angle The standard echo intensity is easily understood by those skilled in the art and will not be elaborated here. The observation complexity coefficient is determined based on the target element richness index and canopy complexity index of the observation target of the crop category. The observation complexity coefficient = ln (canopy complexity index / target element richness index). The canopy complexity index is the absolute value of the difference between the maximum and minimum leaf inclination angles of the observation target obtained during the current observation evaluation period. The target element richness index is the average chlorophyll content obtained for the leaves of the crop category in which the observation target is located in the corresponding growth stage during the current observation evaluation period in the observation management record. How to determine the leaf inclination angle of the key target is something that those skilled in the art have already mastered and will not be elaborated here.

[0058] Specifically, the observation evaluation condition responded by the target evaluation module is that the observed target is in a type of observation analysis state, and then the observation analysis module is determined to perform inversion and prediction analysis on the fluorescence observation data of the observed target using an observation feature analysis method;

[0059] The observation analysis module responds to the characteristic analysis conditions, determines the mirror dominant direction of the observation target according to the observed polarization parameters, and determines the inversion execution strategy of the observation target based on the proportion of the dominant direction;

[0060] The characteristic analysis condition is that the target evaluation module determines that the observation analysis module uses the observation characteristic analysis method to perform inversion and prediction analysis on the fluorescence observation data of the observation target, and the type of observation analysis state is that the reflection complexity coefficient of the observation target is greater than the preset reflection complexity coefficient or the observation complexity coefficient is greater than the preset observation complexity coefficient.

[0061] Among them, the values of the preset reflection complexity coefficient and the preset observation complexity coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the observation management record. The higher the user's reliability requirement for the fluorescence observation result of the observation target, the smaller the value of the preset reflection complexity coefficient, and the smaller the value of the preset observation complexity coefficient. A method for determining the value of the preset reflection complexity coefficient is provided, and the observation management record for inversion and estimation analysis of the fluorescence observation data of the observation target using the observation feature analysis method is recorded as an evaluation reference record. The average value of the reflection complexity coefficient of the observation target in the evaluation reference record that satisfies the user's effectiveness of the correction result of the fluorescence observation data of the observation target is recorded as the preset reflection complexity coefficient. A method for determining the value of the preset observation complexity coefficient is provided, and the average value of the observation complexity coefficient of the observation target in the evaluation reference record that satisfies the user's effectiveness of the correction result of the fluorescence observation data of the observation target is recorded as the preset observation complexity coefficient.

[0062] If the observation target is in a Class I observation and analysis state, it indicates that the process of chloroplast fluorescence observation for such observation targets is subject to continuous influence. The fluorescence observation data is inverted and estimated through observation feature analysis. The targeted inversion execution strategy is determined according to the proportion of the dominant direction of the observation target. The acquired observation data is screened to determine the data used for chlorophyll content inversion and estimation to ensure the reliability of the inversion result. For a single observation target in a Class I observation and analysis state, the proportion of the dominant reflection component of each observation direction for the crop category corresponding to the observation target in the observation management record is determined. The observation polarization parameter of each direction corresponds to only a single observation incident angle. For a single observation direction, the observation polarization parameter is the average value of the reference dominant reflection component ratio determined for the crop category corresponding to the observation target in the observation management record under the observation direction. The reference dominant reflection component ratio is the average value of the dominant reflection component ratio of each observation band under the observation direction. The observation direction with an observation polarization parameter greater than the preset observation polarization parameter is recorded as the mirror dominant direction. The dominant direction ratio = the number of mirror dominant directions determined in the current observation evaluation cycle / the number of observation directions used in the current observation evaluation cycle;

[0063] The value of the preset observation polarization parameter can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation results of the observation target, the smaller the value of the preset observation polarization parameter. A method for determining the value of the preset observation polarization parameter is provided, and the average value of the observation polarization parameters of the dominant directions of each mirror in the observation management record that meets the user's requirement for the reliability of the fluorescence observation results of the observation target is recorded as the preset observation polarization parameter.

[0064] Specifically, if the inversion execution condition responded by the observation analysis module is that the dominant direction ratio is greater than the preset dominant direction ratio, the dominant inversion module determines the estimated execution band set based on the approximate stability index and the dominant reflection component ratio;

[0065] The leading inversion module responds to the estimation execution condition and determines the observation estimation data based on the polarization reflection parameters of each estimation execution band in the estimation execution band set;

[0066] The estimation execution condition is that the observation target completes the determination of the estimation execution band set.

[0067] The value of the preset dominant direction ratio can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. A method for determining the value of the preset dominant direction ratio is provided, in which the observation management record for determining the observation estimation data based on the polarization reflection parameter of each estimation execution band is recorded as the dominant inversion record, and the minimum value of the dominant direction ratio in the dominant inversion record that meets the user's reliability requirement for the fluorescence observation result of the observation target is recorded as the preset dominant direction ratio;

[0068] When the dominant direction ratio is greater than the preset dominant direction ratio, the mirror dominant direction with the smallest reference dominant reflection component ratio is recorded as the estimated analysis direction, and the estimated execution band set of the estimated analysis direction is determined according to the approximate stability index. Any determined estimated execution band set contains three observation bands, and the approximate stability index of the determined estimated execution band set is guaranteed to be the maximum value. For a single estimated execution fluctuation set, the approximate stability index = 1 / (band difference parameter + polarization reflection difference parameter), the band difference parameter is the absolute value of the difference between the maximum wavelength and the minimum wavelength in the estimated execution fluctuation set, the polarization reflection difference parameter is the absolute value of the difference between the maximum value and the minimum value of the polarization reflection parameter of each observation band in the estimated execution fluctuation set, and the observation estimated data , y1, y2 and y3 are the reciprocals of the proportions of the dominant reflection components corresponding to each observation band in the determined estimated execution band set. The determined observation estimated data can be used as a spectral index for inverting and determining the chlorophyll content for the observation target. How to determine the chlorophyll content based on the spectral index is easy for those skilled in the art to understand and will not be elaborated here.

[0069] Specifically, the inversion execution condition responded by the observation analysis module is that the dominant direction ratio is less than or equal to the preset dominant direction ratio, then the multi-parameter inversion module is determined to determine the inversion weight coefficient of each observation band based on the observation change difference and the feature overlap;

[0070] The inversion weight coefficient is negatively correlated with the observed change difference, and the inversion weight coefficient is positively correlated with the feature coincidence.

[0071] Specifically, the multi-parameter inversion module responds to the multi-parameter execution conditions, performs band weight analysis for the dominant direction of each mirror, and determines whether to adjust the inversion weight index for each observation band according to the proportion of the dominant reflection component;

[0072] The weight analysis condition of the multi-parameter inversion module response is that if the proportion of the dominant reflection component in the observation band is less than the preset dominant reflection component proportion, the inversion weight index of the observation band is increased according to the dominant reflection component proportion;

[0073] The multi-parameter execution condition is that the observation analysis module determines the multi-parameter inversion module to determine the inversion weight coefficient of each observation band.

[0074] When the dominant direction proportion is less than or equal to the preset dominant direction proportion, for a single mirror dominant direction, the inversion weight coefficient of each observation band of the mirror dominant direction is determined based on the observed change difference and the characteristic overlap, and the dominant reflection component proportion of each observation band during the observation of the mirror dominant direction is detected. The inversion weight coefficient of any observation band of the mirror dominant direction is positively correlated with the reflection effective parameter, where the reflection effective parameter = ln (characteristic overlap / observed change difference), the characteristic overlap = the absolute value of the difference between the maximum wavelength and the minimum wavelength of the overlapping range between the observation band and the observation characteristic band / the absolute value of the difference between the maximum wavelength and the minimum wavelength of the observation band. The observation characteristic band is a set of observation bands that are all inverted preferred bands for the crop category corresponding to the observation target in the observation management record. The observed change difference is the absolute value of the difference between the maximum value and the minimum value of the dominant reflection component proportion of each observation band of the mirror dominant direction.

[0075] For a single observation band, if the dominant reflection component ratio of the observation band is less than the preset dominant reflection component ratio, the inversion weight index is increased according to the dominant reflection component ratio. The increase in the inversion weight index is negatively correlated with the dominant reflection component ratio. For a single observation band, the dominant reflection component ratio = polarized reflection component / (polarized reflection component + non-polarized reflection component). How to extract the polarized reflection component and the non-polarized reflection component for the reflection data obtained in each observation band is easy for those skilled in the art to understand and will not be elaborated here.

[0076] For observation bands in which the dominant reflection component accounts for a relatively small proportion, after the separation of the specular reflection component is completed, the reflection data obtained by such observation bands is relatively complete. Therefore, the inversion weight coefficient of such observation bands is further adjusted to further improve the reliability of the determined observation results. The value of the preset dominant reflection component proportion can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirements for the reliability of the fluorescence observation results of the observation target, the smaller the value of the preset dominant reflection component proportion. A method for determining the value of the preset dominant reflection component proportion is provided. The observation management record in which the inversion weight index of the observation band is increased and adjusted according to the dominant reflection component proportion is recorded as a weight control record. The average value of the dominant reflection component proportion of each observation band in the weight control record that meets the user's requirements for the reliability of the fluorescence observation results of the observation target is recorded as the preset dominant reflection component proportion.

[0077] The observation band whose inversion weight coefficient is greater than the preset inversion weight coefficient is recorded as the preferred inversion band of the observation target, and the observation result of the chlorophyll content of the observation target within the current observation evaluation period is determined based on the reflection data obtained from the preferred inversion band. This is content that is easy for technical personnel in this field to understand and will not be elaborated here. The value of the preset inversion weight coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation result of the observation target, the larger the value of the preset inversion weight coefficient. A method for determining the value of the preset inversion weight coefficient is provided, and the average value of the inversion band coefficients of each preferred inversion band in the observation management record that meets the user's requirement for the reliability of the fluorescence observation result of the observation target is recorded as the preset inversion band coefficient.

[0078] Specifically, if the observation evaluation condition responded by the target evaluation module is that the observed target is in the second-category observation analysis state, the auxiliary analysis module is determined to perform a rationality analysis on the fluorescence observation data of the observed target using an auxiliary compliance analysis method;

[0079] The auxiliary analysis module responds to the auxiliary correction condition and determines the auxiliary analysis method based on the correlation of the distribution characteristics of the conflicting data points;

[0080] The auxiliary correction condition is that the target evaluation module determines that the auxiliary analysis module uses an auxiliary compliance analysis method to correct the observation data of the observation target, and the second type of observation analysis state is that the reflection complexity coefficient of the observation target is less than or equal to the preset reflection complexity coefficient and the observation complexity coefficient is less than or equal to the preset observation complexity coefficient.

[0081] Among them, if the observation target is in the second-class observation and analysis state, it indicates that the interference in the process of chloroplast fluorescence observation for such observation targets is small, and it is easier to divide the branches and leaves of the observation target according to the acquired point cloud data to determine the growth position information corresponding to each part of the fluorescence observation data. Therefore, the auxiliary compliance analysis method is used to correct the fluorescence observation data of the observation target to assist in verification of the fluorescence observation data acquired for each part, further ensuring the reliability of the acquired fluorescence observation data;

[0082] When the observation data of the observation target is corrected by the auxiliary compliance analysis method, the distribution feature correlation of the conflict data points of the observation target is detected, and the auxiliary analysis method of the observation target is determined according to the distribution feature correlation. The conflict data point is an observation point whose observation conflict coefficient is greater than the preset observation conflict coefficient. For a single observation point, the observation conflict coefficient is the average of the absolute value of the difference between the fluorescence intensity of the reflection data obtained at the observation point and the fluorescence intensity of the reflection data obtained within the conflict evaluation range. The distance between any observation point within the conflict evaluation range and the observation point for determining the observation conflict coefficient is less than the preset conflict evaluation distance. The value of the preset conflict evaluation distance can be set by the user according to the actual working scenario. A value of the preset conflict evaluation distance is provided, which is five times the maximum length of the blade corresponding to the observation target.

[0083] The value of the preset observation conflict coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation result of the observation target, the smaller the value of the preset observation conflict coefficient. A method for determining the value of the preset observation conflict coefficient is provided, in which the minimum value of the observation conflict coefficient of each conflicting data point in the observation management record that meets the user's requirement for the reliability of the fluorescence observation result of the observation target is recorded as the preset observation conflict coefficient.

[0084] The distribution feature correlation is determined based on the distribution position index of each conflicting data point. For a single conflicting data point, the distribution position index is the distance between the position connecting the conflicting data point and the main branch of the observed crop. The distribution feature correlation = 1 / (distribution fluctuation parameter + distribution position difference parameter), the distribution position difference parameter = (the maximum value of the distribution position index of each conflicting data point - the minimum value of the distribution position index of each conflicting data point) / the average value of the distribution position index of each conflicting data point, the distribution fluctuation parameter , n is the number of conflicting data points of the observed target, fi is the distribution position index of the i-th conflicting data point, and f0 is the average distribution position index of each conflicting data point.

[0085] Specifically, the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation of the observed target is greater than the preset distribution feature correlation, then the observation stability coefficient of each conflicting data point is detected, and it is determined whether to issue a growth restriction warning for the observed target based on the reference observation stability coefficient;

[0086] The observation stability coefficient is determined according to the intensity change degree of the fluorescence intensity of the reflection data acquired each time during the observation evaluation phase.

[0087] The value of the preset distribution feature correlation can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation result of the observation target, the larger the value of the preset feature correlation. A method for determining the value of the preset observation conflict coefficient is provided. The observation management record for determining whether to issue a growth restriction warning for the observation target based on the observation stability coefficient of each conflicting data point is recorded as a warning evaluation record. The minimum value of the distribution feature correlation of the observation target in the warning evaluation record that meets the user's requirement for the reliability of the fluorescence observation result of the observation target is recorded as the preset distribution feature correlation.

[0088] The reference observation stability coefficient is the average value of the observation stability coefficients of each conflicting data point of the observation target. For a single conflicting data point, the observation stability coefficient = 1 / the average value of each intensity change degree in the stability evaluation stage. The intensity change degree of the fluorescence intensity of the reflection data acquired each time in the observation evaluation stage is detected. For the fluorescence intensity of the reflection data acquired once, the intensity change degree = |the fluorescence intensity of the reflection data acquired this time - the fluorescence intensity of the reflection data acquired last time| / the fluorescence intensity of the reflection data acquired last time. How to determine the fluorescence intensity of the reflection data acquired each time is content that is easy to understand for technical personnel in this field. The end time of the observation and evaluation stage is the end time of the current observation management cycle. The duration of the observation and evaluation stage can be set by the user according to the actual work scenario. For example, the user can set it according to the observation management record. The higher the user's requirements for the reliability of the fluorescence observation results of the observation target, the longer the duration of the observation and evaluation stage. A duration of the observation and evaluation stage is provided, and the duration of the observation and evaluation stage is 5 times the observation and evaluation cycle.

[0089] Specifically, if the restriction analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is less than or equal to the preset reference observation stability coefficient, it is determined that a growth restriction warning is issued for the observation target;

[0090] If the restricted analysis condition of the auxiliary analysis module response is that the reference observation stability coefficient is greater than the preset reference observation stability coefficient, the observation quality index of the observation target is determined based on the conflict difference ratio;

[0091] The observation quality index is negatively correlated with the proportion of conflict differences.

[0092] Among them, the value of the preset reference observation stability coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record, and a method for determining the value of the preset reference observation stability coefficient is provided. The observation management record for the growth restriction warning of the observation target is recorded as the warning reference record, and the maximum value of the reference observation stability coefficient in the warning reference record that meets the user's reliability requirements for the fluorescence observation results of the observation target is recorded as the preset reference observation stability coefficient. When the reference observation stability coefficient is greater than the preset reference observation stability coefficient, the conflict difference ratio of the observation target is detected, and the conflict difference ratio = the number of conflicting data points that do not exist in the current observation evaluation cycle and the previous observation management cycle / the number of conflicting data points that exist in the current observation evaluation cycle.

[0093] Specifically, the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation of the observed target is less than or equal to the preset distribution feature correlation, and the observation quality index of the observed target is determined according to the conflict difference ratio and the change association parameter;

[0094] The observation quality index is positively correlated with the change association parameter, and is negatively correlated with the conflict difference ratio.

[0095] Among them, when the distribution feature correlation is less than or equal to the preset distribution feature correlation, the observation quality index and the quality reference coefficient are positively correlated, and the quality reference coefficient = change association parameter / conflict difference ratio, the change association parameter is the average value of the change correlation of each newly added conflicting data point in the current observation evaluation cycle, and for a single newly added conflicting data point, the change correlation is the number of conflicting data points within the conflict evaluation range of the conflicting data point in the previous observation evaluation cycle. If the observation quality index is less than the preset observation quality index, an observation quality warning is issued to the user, indicating that the determined observation result is abnormal;

[0096] The value of the preset observation quality index can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation results of the observation target, the larger the value of the preset observation quality index. A method for setting the value of the preset observation quality index is provided, and the observation management record that issues an observation quality warning to the user is recorded as a quality reference record. The minimum value of the observation quality index in the quality reference record that meets the user's requirement for the reliability of the fluorescence observation results of the observation target is recorded as the preset observation quality index.

[0097] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0098] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A chlorophyll fluorescence observation system based on laser radar, characterized in that: include: A target assessment module is used to respond to observation assessment conditions to determine the observation analysis strategy for each observation target, which is to use an observation feature analysis method to perform an inversion and prediction analysis on the fluorescence observation data of the observation target, or to use an auxiliary compliance analysis method to perform a rationality analysis on the fluorescence observation data of the observation target; an observation analysis module connected to the target evaluation module to determine the dominant mirror direction of the observed target and, in response to an inversion execution condition, determine an inversion execution strategy for the observed target, such as determining an estimated execution band set based on an approximate stability index and a proportion of a dominant reflection component, or determining an inversion weight coefficient for each observation band based on an observed variation difference and a feature overlap; A leading inversion module, connected to the observation analysis module, for determining a set of estimation execution bands and determining observation estimation data based on polarization reflection parameters of the estimation execution bands; a multi-parameter inversion module, connected to the observation analysis module, for determining an inversion weight coefficient for each observation band and, in response to a weight analysis condition, determining whether to adjust the inversion weight index for each observation band; An auxiliary analysis module is connected to the target evaluation module and is used to respond to the auxiliary analysis conditions to determine whether the auxiliary analysis method of the observation target in the second-class observation analysis state is to determine whether to issue a growth restriction warning for the observation target based on the reference observation stability coefficient, or to determine the observation quality index of the observation target based on the conflict difference ratio and the change association parameter.

2. The chlorophyll fluorescence observation system based on laser radar according to claim 1, characterized in that: The target evaluation module determines the reflection complexity coefficient of each observed target according to the echo difference parameter and the echo standard difference parameter; The target evaluation module determines the observation complexity coefficient of each observation target according to the target element richness index and the canopy complexity index.

3. The chlorophyll fluorescence observation system based on laser radar according to claim 2, characterized in that: If the target evaluation module responds to an observation evaluation condition that the observed target is in a first-class observation analysis state, the observation analysis module is determined to perform an inversion and prediction analysis on the fluorescence observation data of the observed target using an observation feature analysis method; The observation analysis module responds to the characteristic analysis conditions, determines the mirror dominant direction of the observation target according to the observed polarization parameters, and determines the inversion execution strategy of the observation target based on the proportion of the dominant direction; The characteristic analysis condition is that the target evaluation module determines that the observation analysis module uses the observation characteristic analysis method to perform inversion and prediction analysis on the fluorescence observation data of the observation target, and the type of observation analysis state is that the reflection complexity coefficient of the observation target is greater than the preset reflection complexity coefficient or the observation complexity coefficient is greater than the preset observation complexity coefficient.

4. The chlorophyll fluorescence observation system based on laser radar according to claim 3, characterized in that: If the inversion execution condition responded by the observation and analysis module is that the dominant direction ratio is greater than the preset dominant direction ratio, the dominant inversion module determines the estimated execution band set based on the approximate stability index and the dominant reflection component ratio; The leading inversion module responds to the estimation execution condition and determines the observation estimation data based on the polarization reflection parameters of each estimation execution band in the estimation execution band set; The estimation execution condition is that the observation target completes the determination of the estimation execution band set.

5. The chlorophyll fluorescence observation system based on laser radar according to claim 4, characterized in that: If the inversion execution condition responded by the observation analysis module is that the dominant direction ratio is less than or equal to the preset dominant direction ratio, the multi-parameter inversion module is determined to determine the inversion weight coefficient of each observation band based on the observation change difference and the feature overlap; The inversion weight coefficient is negatively correlated with the observed change difference, and the inversion weight coefficient is positively correlated with the feature coincidence.

6. The chlorophyll fluorescence observation system based on laser radar according to claim 5, characterized in that: The multi-parameter inversion module responds to the multi-parameter execution conditions, performs band weight analysis on the dominant direction of each mirror, and determines whether to adjust the inversion weight index for each observation band according to the proportion of the dominant reflection component; The weight analysis condition of the multi-parameter inversion module response is that if the proportion of the dominant reflection component in the observation band is less than the preset dominant reflection component proportion, the inversion weight index of the observation band is increased according to the dominant reflection component proportion; The multi-parameter execution condition is that the observation analysis module determines the multi-parameter inversion module to determine the inversion weight coefficient of each observation band.

7. The chlorophyll fluorescence observation system based on laser radar according to claim 6, characterized in that: If the target evaluation module responds to an observation evaluation condition that the observed target is in a Class II observation analysis state, the auxiliary analysis module is determined to perform a rationality analysis on the fluorescence observation data of the observed target using an auxiliary compliance analysis method; The auxiliary analysis module responds to the auxiliary correction condition and determines the auxiliary analysis method based on the correlation of the distribution characteristics of the conflicting data points; The auxiliary correction condition is that the target evaluation module determines that the auxiliary analysis module uses an auxiliary compliance analysis method to correct the observation data of the observation target, and the second type of observation analysis state is that the reflection complexity coefficient of the observation target is less than or equal to the preset reflection complexity coefficient and the observation complexity coefficient is less than or equal to the preset observation complexity coefficient.

8. The chlorophyll fluorescence observation system based on laser radar according to claim 1, characterized in that: If the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution characteristic correlation of the observed target is greater than the preset distribution characteristic correlation, the observation stability coefficient of each conflicting data point is detected, and it is determined whether to issue a growth restriction warning for the observed target based on the reference observation stability coefficient; The observation stability coefficient is determined according to the intensity change degree of the fluorescence intensity of the reflection data acquired each time during the observation evaluation phase.

9. The chlorophyll fluorescence observation system based on laser radar according to claim 8, characterized in that: If the restriction analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is less than or equal to the preset reference observation stability coefficient, it is determined that a growth restriction warning is issued for the observation target; If the restricted analysis condition of the auxiliary analysis module response is that the reference observation stability coefficient is greater than the preset reference observation stability coefficient, the observation quality index of the observation target is determined based on the conflict difference ratio; The observation quality index is negatively correlated with the proportion of conflict differences.

10. The chlorophyll fluorescence observation system based on laser radar according to claim 9, characterized in that: If the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation of the observed target is less than or equal to the preset distribution feature correlation, the observation quality index of the observed target is determined according to the conflict difference ratio and the change association parameter; The observation quality index is positively correlated with the change association parameter, and is negatively correlated with the conflict difference ratio.

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