Saline-alkali soil information monitoring and treatment method and system

Abstract

The invention relates to the technical field of saline-alkali soil monitoring and treatment, and particularly discloses a saline-alkali soil information monitoring and treatment method and system. A camera is used for collecting full topographic data of a target saline-alkali soil before treatment and full topographic data of the target saline-alkali soil after treatment; introducing an image processing algorithm based on deep learning to perform feature extraction on the full topographic data before and after treatment so as to respectively capture the topographic and geomorphic features of the target saline-alkali soil before and after treatment, and then taking the topographic and geomorphic features of the saline-alkali soil before treatment as a baseline state; and performing causal alignment analysis on the landform characteristics and the baseline state of the treated saline-alkali soil to excavate the potential difference between the landform characteristics and the baseline state, thereby realizing intelligent assessment of the treatment effect. Therefore, by considering the landform information before and after saline-alkali land treatment, the treatment effect can be analyzed more accurately, and the influence of short-term fluctuation of soil physical property parameters on treatment effect evaluation is avoided.

Description

Technical Field

[0001] The present application relates to the technical field of saline-alkali land monitoring and management, and more specifically, to a saline-alkali land information monitoring and management method and system. Background Art

[0002] Saline-alkali land refers to low-yield soil that contains excessive soluble salts, resulting in stunted plant growth. Soil salinization not only affects the effective use of land resources, but also poses a threat to the ecological environment. In order to improve the efficiency and effectiveness of saline-alkali land management, the invention patent with publication number CN118966611A proposes a saline-alkali land information monitoring and management method and system, which divides the saline-alkali land into priority sub-regions and non-priority sub-regions according to the area of ​​the saline-alkali land and the severity of the salinity. Through regional management, resources are concentrated to deal with serious areas, while maintaining monitoring and management of non-priority areas, and by analyzing the changes in soil moisture content, conductivity, soil structure and vegetation cover before and after saline-alkali land management, the management coefficients of different regions are calculated to achieve a quantitative evaluation of the management effect. This method helps to reasonably allocate resources and improve management efficiency through regional management and quantitative evaluation of management effects.

[0003] However, existing technologies mainly focus on physical measurement and statistical analysis of limited data dimensions at the soil level when evaluating governance effects. However, soil physical property parameters are easily affected by a variety of external factors such as seasonal precipitation, temperature fluctuations, and human cultivation, resulting in short-term fluctuations, which may lead to insufficient accuracy and reliability of evaluation results.

[0004] Therefore, an optimized saline-alkali land information monitoring and management method and system are expected. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method and system for monitoring and managing saline-alkali land information, which uses a camera to collect the overall terrain data of the target saline-alkali land before and after management, and introduces an image processing algorithm based on deep learning to extract features from the overall terrain data before and after management, so as to capture the topographic and geomorphic characteristics of the target saline-alkali land before and after management, respectively, and then use the topographic and geomorphic characteristics of the saline-alkali land before management as the baseline state, and perform causal alignment analysis on the topographic and geomorphic characteristics of the saline-alkali land after management and the baseline state to dig out the potential differences between the two, thereby realizing intelligent evaluation of the management effect. In this way, by considering the topographic and geomorphic information before and after the management of the saline-alkali land, the management effect can be analyzed more accurately, avoiding the influence of short-term fluctuations in soil physical property parameters on the management effect evaluation.

[0006] Accordingly, according to one aspect of the present application, a saline-alkali land information monitoring and management system is provided, which includes: A module for acquiring overall terrain data before treatment, used for acquiring overall terrain data of the target saline-alkali land before treatment collected by a camera; A post-treatment overall terrain data acquisition module, used to acquire the post-treatment overall terrain data of the target saline-alkali land collected by the camera; A topographic and geomorphic feature extraction module is used to extract topographic and geomorphic features from the overall topographic data before treatment and the overall topographic data after treatment to obtain a target saline-alkali land topographic and geomorphic baseline state feature coding map and a target saline-alkali land topographic and geomorphic state feature coding map after treatment; A causal alignment analysis module is used to perform a causal alignment analysis based on topographic and geomorphic characteristics on the target saline-alkali land topography baseline state characteristic coding map and the target saline-alkali land topography after governance state characteristic coding map to obtain a strong causal joint perception coding characteristic map before and after governance of the target saline-alkali land; The control effect evaluation module is used to determine the evaluation result of the control effect based on the strong causal joint perception coding feature map before and after the control of the target saline-alkali land.

[0007] According to another aspect of the present application, a method for monitoring and managing saline-alkali land information is provided, comprising: Obtaining the overall topographic data of the target saline-alkali land before treatment collected by the camera; Acquiring the overall terrain data of the target saline-alkali land after treatment collected by the camera; Extracting topographic and geomorphic features from the overall topographic data before treatment and the overall topographic data after treatment to obtain a target saline-alkali land topographic and geomorphic baseline state feature coding map and a target saline-alkali land topographic and geomorphic state feature coding map after treatment; Performing a causal alignment analysis based on topographic and geomorphic features on the target saline-alkali land topography baseline state characteristic coding map and the target saline-alkali land topography after governance state characteristic coding map to obtain a strong causal joint perception coding characteristic map of the target saline-alkali land before and after governance; Based on the strong causal joint perception coding feature map before and after the treatment of the target saline-alkali land, the evaluation result of the treatment effect is determined.

[0008] Compared with the prior art, the saline-alkali land information monitoring and management method and system provided in this application use cameras to collect the overall terrain data of the target saline-alkali land before and after management, and introduce an image processing algorithm based on deep learning to extract features from the overall terrain data before and after management, so as to capture the topographic and geomorphic characteristics of the target saline-alkali land before and after management, and then use the topographic and geomorphic characteristics of the saline-alkali land before management as the baseline state, and perform causal alignment analysis on the topographic and geomorphic characteristics of the saline-alkali land after management and the baseline state to dig out the potential differences between the two, thereby realizing intelligent evaluation of the management effect. In this way, by considering the topographic and geomorphic information before and after the management of the saline-alkali land, the management effect can be analyzed more accurately, avoiding the impact of short-term fluctuations in soil physical property parameters on the management effect evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 It is a block diagram of a saline-alkali land information monitoring and management system according to an embodiment of the present application.

[0011] Figure 2 This is a data flow diagram of a saline-alkali land information monitoring and management system according to an embodiment of the present application.

[0012] Figure 3 It is a block diagram of the causal alignment analysis module in the saline-alkali land information monitoring and management system according to an embodiment of the present application.

[0013] Figure 4 It is a block diagram of a causal correlation strength measurement unit in a saline-alkali land information monitoring and management system according to an embodiment of the present application.

[0014] Figure 5 This is a flow chart of a method for monitoring and controlling saline-alkali land information according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0016] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0017] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0018] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0019] As mentioned in the background technology above, Patent Publication No. CN118966611A proposes a method and system for monitoring and managing saline-alkali land information, which divides the saline-alkali land into priority sub-regions and non-priority sub-regions according to the area of ​​the saline-alkali land and the severity of the salinity. Through regional management, resources are concentrated to deal with serious areas, while maintaining monitoring and management of non-priority areas, and by analyzing data such as changes in soil moisture content, conductivity, soil structure, and vegetation coverage before and after saline-alkali land management, the management coefficients of different regions are calculated to achieve a quantitative evaluation of the management effect. This method helps to reasonably allocate resources and improve management efficiency through regional management and quantitative evaluation of management effects.

[0020] In the prior art, the main focus is on physical measurement and statistical analysis of limited data dimensions at the soil level. However, soil physical property parameters are easily affected by various external factors such as seasonal precipitation, temperature fluctuations, and artificial cultivation, resulting in short-term fluctuations, which may lead to insufficient accuracy and reliability of the evaluation results. In response to this technical problem, the present application proposes an optimized saline-alkali land information monitoring and management system, which uses a camera to collect the full-view terrain data of the target saline-alkali land before and after management, and introduces an image processing algorithm based on deep learning to extract features from the full-view terrain data before and after management, so as to capture the topographic and geomorphic characteristics of the target saline-alkali land before and after management, and then use the topographic and geomorphic characteristics of the saline-alkali land before management as the baseline state, and perform causal alignment analysis on the topographic and geomorphic characteristics of the saline-alkali land after management and the baseline state to dig out the potential differences between the two, thereby realizing an intelligent evaluation of the management effect. In this way, by considering the topographic and geomorphic information before and after the management of the saline-alkali land, the management effect can be analyzed more accurately, avoiding the impact of short-term fluctuations in soil physical property parameters on the management effect evaluation.

[0021] Figure 1 It is a block diagram of a saline-alkali land information monitoring and management system according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the saline-alkali land information monitoring and management system according to the embodiment of the present application. Figure 1 and Figure 2 As shown, the saline-alkali land information monitoring and management system 100 includes: a full-view terrain data acquisition module 110 before management, which is used to acquire the full-view terrain data of the target saline-alkali land before management collected by a camera; a full-view terrain data acquisition module 120 after management, which is used to acquire the full-view terrain data of the target saline-alkali land after management collected by the camera; a terrain and geomorphic feature extraction module 130, which is used to extract terrain and geomorphic features from the full-view terrain data before management and the full-view terrain data after management to obtain a target saline-alkali land terrain and geomorphic baseline state feature coding map and a target saline-alkali land terrain and geomorphic state feature coding map after management; a causal alignment analysis module 140, which is used to perform a causal alignment analysis based on terrain and geomorphic features on the target saline-alkali land terrain and geomorphic baseline state feature coding map and the target saline-alkali land terrain and geomorphic state feature coding map after management to obtain a target saline-alkali land strong causal joint perception coding feature map before and after management; a management effect evaluation module 150, which is used to determine the evaluation result of the management effect based on the target saline-alkali land strong causal joint perception coding feature map before and after management.

[0022] In the above-mentioned saline-alkali land information monitoring and management system, the module 110 for acquiring the full-view terrain data before management is used to acquire the full-view terrain data of the target saline-alkali land before management collected by the camera. It should be understood that the topography is an important intuitive manifestation of the management effect of saline-alkali land, and its changes often contain the management results of many aspects such as soil structure and vegetation restoration. For example, in the management process, changes in topography such as changes in land flatness, adjustments in waterlogged areas, and optimization of slopes may directly affect the drainage, ventilation, and lighting conditions of the saline-alkali land, and thus have a profound impact on the overall management effect. Therefore, in order to comprehensively and accurately evaluate the management effect, this application uses the full-view terrain data of the target saline-alkali land before management collected by the camera as baseline data.

[0023] In the above-mentioned saline-alkali land information monitoring and governance system, the post-governance full-view terrain data acquisition module 120 is used to obtain the post-governance full-view terrain data of the target saline-alkali land collected by the camera. That is, the present application evaluates the governance effect of the saline-alkali land by obtaining the full-view terrain data of the target saline-alkali land collected by the camera before governance as baseline data, and the full-view terrain data of the target saline-alkali land after governance as comparison data, and by comparing and analyzing the two. Specifically, the camera is a high-definition camera mounted on an unmanned aerial vehicle. It should be understood that the unmanned aerial vehicle has the characteristics of flexibility and low-altitude flight, can quickly reach the target saline-alkali land area, is not restricted by terrain and traffic conditions, and can cover a large area of ​​saline-alkali land in a short time. The high-definition camera focuses light on the image sensor through an optical lens, uses the image sensor to convert the optical signal into an electrical signal or a digital signal, and then encodes, compresses, and processes the image processing circuit, and finally forms high-resolution image data, so that various details of the saline-alkali land, such as the texture and color difference of the soil surface, can be clearly presented, providing rich information for subsequent analysis.

[0024] Specifically, first of all, it is necessary to use a suitable camera to collect the overall terrain data of the target saline-alkali land before treatment and the overall terrain data of the target saline-alkali land after treatment. Specifically, the camera should have high resolution and wide-angle lens so that it can capture as much detailed information as possible. At the same time, in order to meet the shooting needs under different lighting conditions, the camera also needs to have good low-light performance. Considering that saline-alkali land is often vast and a single camera is difficult to cover the entire area, drones are usually used to carry out operations with cameras. Drones can not only flexibly adjust the flight altitude and speed to achieve refined shooting of areas of different scales, but also adjust the shooting route according to actual needs to ensure that every corner can be photographed.

[0025] In the preparation stage, the flight path and shooting parameters of the drone must be carefully planned. This includes determining the location of the take-off and landing points, calculating the optimal flight altitude, setting the number of photos taken per second, etc. In order to reduce the impact of external environmental factors on data accuracy, shooting should be carried out on days with clear weather and low wind speed. In addition, understanding the geographical information of the target saline-alkali land, such as changes in altitude and undulating terrain, will help optimize the flight trajectory of the drone and avoid blind spots caused by complex terrain. For each shooting mission, full preparations need to be made in advance to ensure a smooth shooting process.

[0026] To ensure the integrity and consistency of the data, the camera should be calibrated before each shooting. During the calibration process, not only the focal length and exposure time of the camera should be adjusted, but also the white balance should be set according to the current lighting conditions to ensure the true color of the photos taken. In view of the reflection problem that may be caused by the white or light-colored soil surface unique to saline-alkali land areas, special attention should be paid to adjusting the camera settings to reduce the occurrence of overexposure. In addition, considering the sparse vegetation coverage of saline-alkali land, the overlap of the shooting should be appropriately increased during the shooting process so that the transition parts between images can be better integrated when they are subsequently stitched into a panorama.

[0027] In addition to the preparation of hardware equipment and shooting parameters, strict quality control measures need to be formulated to ensure the reliability of the collected data. For example, before the formal shooting, you can take a test shot in a small area to check whether the photo quality meets the requirements. If problems such as image blur and color distortion are found, adjust the camera settings in time until the problem is solved. During the entire shooting process, the operator needs to monitor the status of the drone and the working condition of the camera in real time, and take immediate measures to correct any abnormalities. For example, when the drone deviates from the scheduled route or encounters sudden weather conditions, it can respond quickly to ensure the safety of the equipment while also ensuring the integrity of the data.

[0028] When performing the specific shooting task, in order to ensure efficiency and data consistency, the target saline-alkali land can be divided into several relatively independent small areas, and the photos can be taken in sequence according to the pre-planned order. After the shooting work in each small area is completed, it is necessary to carefully check whether the photos obtained cover all the key features of the area and have sufficient overlap with photos of other adjacent areas. In this way, not only can the efficiency of shooting be improved, but it also helps to accurately stitch the photos later to form a complete pre-treatment full-view terrain data set.

[0029] Finally, after completing all the shooting tasks, it is an essential step to conduct preliminary sorting and backing up of the collected data. This includes deleting duplicate or unclear photos, and filing the remaining photos in order of shooting location and time, ensuring that each photo can be traced back to its specific shooting location and time. At the same time, the original data should be uploaded to the cloud storage or copied to an external hard drive in time to prevent data loss.

[0030] The acquisition process of the full-view topographic data of the target saline-alkali land after treatment is similar to the acquisition steps of the full-view topographic data of the target saline-alkali land before treatment. After the treatment is completed, it is also necessary to select suitable weather conditions for shooting to maintain the consistency and comparability of the data. Considering that the treatment measures may lead to significant changes in the topography, such as vegetation restoration and soil structure improvement, more attention needs to be paid to capturing details when shooting, especially those key areas that may show the effect of treatment.

[0031] In the post-treatment shooting work, in addition to following the aforementioned basic principles of equipment calibration, flight path planning and quality control, extra attention needs to be paid to the specific implementation of the treatment measures. For example, some areas may have changed color or texture due to the planting of specific plants, or the introduction of irrigation systems may have caused local humidity to increase, affecting the reflectivity of the soil surface. In this case, it is particularly important to adjust the camera settings to adapt to the new environmental conditions. In this way, it can be ensured that the terrain data after treatment has the same quality and accuracy as the data before treatment, providing a solid foundation for subsequent analysis.

[0032] In the above-mentioned saline-alkali land information monitoring and management system, the topographic and geomorphic feature extraction module 130 is used to extract topographic and geomorphic features from the full-view terrain data before management and the full-view terrain data after management to obtain a target saline-alkali land topographic and geomorphic baseline state feature coding map and a target saline-alkali land topographic and geomorphic state feature coding map after management. In a specific example of the present application, the topographic and geomorphic feature extraction module 130 is used to: use a topographic and geomorphic feature extractor based on the FPT model to perform image feature extraction on the full-view terrain data before management and the full-view terrain data after management to obtain the target saline-alkali land topographic and geomorphic baseline state feature coding map and the target saline-alkali land topographic and geomorphic state feature coding map after management. Specifically, since the topographic and geomorphic features of saline-alkali land are complex and diverse, including soil texture, color, texture, slope, gully distribution and other aspects, and have different spatial distribution and scale characteristics in the image. Therefore, in order to comprehensively cover the various topographic and geomorphic features of saline-alkali land, this application adopts a topographic and geomorphic feature extractor based on the FPT (Feature Pyramid Transformer) model to extract image features from the full-view terrain data before treatment and the full-view terrain data after treatment. The FPT model can capture the detailed information of the topography in the image at different scales by constructing a multi-scale feature pyramid and introducing the Transformer architecture. At the same time, by considering the global contextual dependencies of cross-scale features, the multi-scale features are effectively associated and fused, thereby achieving a comprehensive description of the topographic and geomorphic features of saline-alkali land, obtaining the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after treatment state feature coding map, thereby providing basic data for subsequent treatment effect analysis.

[0033] In the above-mentioned saline-alkali land information monitoring and management system, the causal alignment analysis module 140 is used to perform causal alignment analysis based on topographic and geomorphic characteristics on the target saline-alkali land topography baseline state characteristic coding map and the target saline-alkali land topography after management state characteristic coding map to obtain a strong causal joint perception coding feature map before and after management of the target saline-alkali land. That is, the present application further reveals the potential connection and change law between the topographic and geomorphic features before and after management by performing feature comparison and interactive analysis on the target saline-alkali land topography baseline state characteristic coding map and the target saline-alkali land topography after management state characteristic coding map, so as to realize intelligent evaluation of the management effect. In particular, considering that in the process of saline-alkali land management, some changes in topography and geomorphology may be directly caused by management measures, while other changes may be caused by natural factors or other unknown factors. Therefore, in order to accurately evaluate the actual effect of governance measures, this application proposes a causal alignment analysis method based on topographic and geomorphic features, which constructs the potential causal alignment relationship of saline-alkali land topographic and geomorphic changes by mining the local feature pairs with strong causal correlation between the target saline-alkali land topographic and geomorphic baseline state feature coding map and the target saline-alkali land topographic and geomorphic post-governance state feature coding map, thereby helping to reveal the direct impact of governance measures on topographic and geomorphic features, suppress the interference of other factors, and improve the accuracy of governance effect evaluation. Among them, Figure 3 FIG. 1 is a block diagram of a causal alignment analysis module in a saline-alkali land information monitoring and management system according to an embodiment of the present application. Figure 3 As shown, the causal alignment analysis module 140 includes: a causal association strength measurement unit 141, which is used to measure the causal association strength of the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map based on local fine-grained decoupling features to obtain a set of closed-loop strength factors of the causal chain unit of the local geomorphic characteristics of the target saline-alkali land before and after governance; a strong causal association pairing unit 142, which is used to measure the causal association strength of the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map based on the set of causal chain unit closed-loop strength factors of the local geomorphic characteristics of the target saline-alkali land before and after governance. The target saline-alkali land topography and geomorphology post-treatment state feature coding map is subjected to strong causal association pairing based on local fine-grained decoupling features to obtain a set of feature pairs of {target saline-alkali land baseline geomorphology state local feature vector, target saline-alkali land after treatment geomorphology state local feature vector} constituting a causal chain unit; a strong causal joint perception unit 143 is used to perform multi-dimensional joint perception aggregation processing on the set of feature pairs of {target saline-alkali land baseline geomorphology state local feature vector, target saline-alkali land after treatment geomorphology state local feature vector} constituting a causal chain unit to obtain the target saline-alkali land before-after treatment strong causal joint perception coding feature map.

[0034] Figure 4FIG. 1 is a block diagram of a causal correlation strength measurement unit in a saline-alkali land information monitoring and management system according to an embodiment of the present application. Figure 4 As shown, the causal association strength measurement unit 141 includes: a feature decoupling subunit 1411, which is used to perform feature decoupling on the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map along the channel dimension of the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map to obtain a set of local feature vectors of the target saline-alkali land baseline geomorphic state and a set of local feature vectors of the target saline-alkali land after governance geomorphic state; an alternative feature extraction subunit 1412, which is used to The alternative target saline-alkali land baseline geomorphic state local feature vector and the alternative target saline-alkali land post-treatment geomorphic state local feature vector are extracted respectively from the set of the target saline-alkali land baseline geomorphic state local feature vector and the set of the target saline-alkali land post-treatment geomorphic state local feature vector; the association strength calculation subunit 1413 is used to measure the causal association strength of the alternative target saline-alkali land baseline geomorphic state local feature vector and the alternative target saline-alkali land post-treatment geomorphic state local feature vector to obtain the closed-loop strength factor of the causal chain unit of the local geomorphic characteristics of the target saline-alkali land before and after treatment.

[0035] More specifically, the characteristic decoupling subunit 1411 is expressed by the formula:

[0036]

[0037] in, represents the feature decoupling operation, and They respectively represent the target saline-alkali land topography baseline state characteristic coding map and the target saline-alkali land topography after treatment state characteristic coding map, , , and They represent the first, second, and third local feature vectors of the baseline geomorphic state of the target saline-alkali land. and The local feature vector of the baseline geomorphic state of the target saline-alkali land, , , and They represent the first, second, and third local feature vectors of the landform state after the target saline-alkali land is treated. and The local feature vector of the landform state after the treatment of the target saline-alkali land.

[0038] That is, the present application decouples the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map, and decomposes them into a set of target saline-alkali land baseline geomorphic state local feature vectors and a set of target saline-alkali land governance geomorphic state local feature vectors. In this way, the highly coupled feature relationships are separated, the feature expressions are refined, and the semantic representation of each local feature vector is made clearer and independent, providing more refined and accurate feature units for subsequent causal association strength calculations, which helps to improve the ability to understand changes in saline-alkali land topography and geomorphic features.

[0039] More specifically, the association strength calculation subunit 1413 is used to construct a semantic alignment modulation flow field between the local feature vector of the baseline geomorphic state of the candidate target saline-alkali land and the local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment, which is expressed by the formula:

[0040] in, represents the transpose of a vector, represents matrix multiplication, represents a 3×3 convolution operation, represents a 1×1 convolution operation, Represents the semantically aligned modulated flow field.

[0041] That is, a semantic alignment modulation flow field is constructed for the local feature vector of the baseline geomorphic state of the alternative target saline-alkali land and the local feature vector of the geomorphic state of the alternative target saline-alkali land after treatment. In actual scenarios, due to the influence of factors such as acquisition time and environment, there may be semantic differences and offsets between the local feature vector of the baseline geomorphic state of the alternative target saline-alkali land and the local feature vector of the geomorphic state of the alternative target saline-alkali land after treatment. By constructing a semantic alignment modulation flow field, the local feature vector of the baseline geomorphic state of the alternative target saline-alkali land and the local feature vector of the geomorphic state of the alternative target saline-alkali land after treatment can be calibrated and adjusted at the semantic level, making the two more comparable in the semantic space. This operation refines the expression of features at the semantic level, so that subsequent feature alignment mapping can be performed based on a more accurate semantic basis, thereby providing a more reliable semantic environment for accurately measuring the strength of causal associations and improving the accuracy of causal relationship analysis at the semantic level.

[0042] More specifically, the association strength calculation subunit 1413 is further used to: perform feature alignment mapping on the local feature vector of the baseline geomorphic state of the candidate target saline-alkali land and the local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment based on the semantic alignment modulation flow field to obtain the aligned local feature vector of the baseline geomorphic state of the candidate target saline-alkali land and the aligned local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment, which is expressed by the formula:

[0043]

[0044] in, and They respectively represent the local feature vector of the baseline geomorphic state of the candidate target saline-alkali land after alignment and the local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment after alignment.

[0045] That is, on the basis of the semantic calibration provided by the semantic alignment modulation flow field, feature alignment mapping is performed on the local feature vector of the baseline geomorphic state of the candidate saline-alkali land and the local feature vector of the geomorphic state of the candidate saline-alkali land after treatment. In this way, each dimension of each feature vector can establish a more reasonable correspondence with the corresponding dimension of another feature vector, thereby refining the expression of features in dimension and scale. The local feature vector of the baseline geomorphic state of the candidate saline-alkali land after alignment and the local feature vector of the geomorphic state of the candidate saline-alkali land after treatment obtained will have higher consistency and comparability at the feature level, thereby further improving the accuracy of causal relationship analysis at the semantic level.

[0046] In particular, in a preferred example of the present application, the association strength calculation subunit 1413 is further used to: input the local feature vector of the baseline geomorphic state of the aligned candidate target saline-alkali land and the local feature vector of the geomorphic state of the aligned candidate target saline-alkali land after treatment into the causal chain unit screening network to obtain the causal chain unit closed-loop strength factor of the local geomorphic feature of the target saline-alkali land before and after treatment, which is expressed by the formula:

[0047] in, represents the arccosine function, It means calculating the square of the vector norm. express and The closed-loop intensity factor of the causal chain unit between the local geomorphological characteristics before and after the target saline-alkali land governance.

[0048] That is, the closed-loop strength factor of the causal chain unit of the local geomorphic characteristics before and after the treatment of the target saline-alkali land includes a knowledge storage part based on the characteristic difference norm between the local characteristic vector of the baseline geomorphic state of the candidate saline-alkali land after the alignment and the local characteristic vector of the geomorphic state of the candidate saline-alkali land after the alignment. And their respective norms are expressed as , The environmental interaction part under the distribution evolution knowledge is to cope with the forgetting of causal evolution knowledge and the loss of autocorrelation plasticity. Furthermore, the positive contribution of distribution evolution knowledge to the strength of causal chain closure (as a molecule) and the negative environmental interaction , Based on the reverse contribution of (as the denominator), the causal strength of geometric correlation is quantified ( Function) is used to intuitively reflect the directional similarity of the causal closed loop strength, so as to obtain the causal chain unit closed loop strength factor of the local geomorphological characteristics before and after the treatment of the target saline-alkali land, which is used to screen the highly correlated and causal feature pairs.

[0049] Specifically, in a specific example of the present application, the strong causal association pairing unit 142 is used to determine whether the local feature vector of the baseline geomorphic state of the candidate target saline-alkali land and the local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment constitute a causal chain unit based on the comparison between the closed-loop strength factor of the causal chain unit of the local geomorphic feature before and after treatment of the target saline-alkali land and a preset threshold, which is expressed by the formula:

[0050] in, Indicates the preset threshold value, Represents the feature pair of {local feature vector of the baseline geomorphic state of the target saline-alkali land, local feature vector of the geomorphic state of the target saline-alkali land after treatment} that constitutes the causal chain unit.

[0051] That is, the closed-loop strength factors of the causal chain units of the local geomorphic features before and after the treatment of the target saline-alkali land are compared one by one with the preset threshold. Through this comparison operation, it is possible to accurately screen out those feature pairs whose causal correlation strength reaches or exceeds the preset threshold. These feature pairs represent the strong causal relationship between the topographic and geomorphic features before and after the treatment. After excluding feature pairs with weak causal correlation strength or no obvious causal relationship, subsequent analysis and processing can focus on truly meaningful strong causal correlation features, which helps to reduce the interference of irrelevant information and improve the accuracy and pertinence of the analysis of factors affecting the effect of saline-alkali land treatment.

[0052] Specifically, in a specific example of the present application, the strong causal joint perception unit 143 is used to: input each of the {target saline-alkali land baseline geomorphic state local feature vector, target saline-alkali land post-treatment geomorphic state local feature vector} feature pairs constituting the causal chain unit in the set of {target saline-alkali land baseline geomorphic state local feature vector, target saline-alkali land post-treatment geomorphic state local feature vector} feature pairs constituting the causal chain unit into the causal chain unit semantic dynamic response network to obtain a set of strong causal joint perception coding feature vectors of the target saline-alkali land before and after treatment; perform feature aggregation on the set of strong causal joint perception coding feature vectors of the target saline-alkali land before and after treatment to obtain the strong causal joint perception coding feature map of the target saline-alkali land before and after treatment, which is expressed by the formula:

[0053] in, and represents different weight parameters, and represents different weight matrices, Indicates point reduction, Indicates point addition, Represents the unit that constitutes the causal chain and The strong causal joint perception coding feature vector of the target saline-alkali land landform before and after treatment is obtained after joint perception coding.

[0054] That is, on the basis of the previous refinement of feature expression and screening of strong causal correlation feature pairs, each feature in the set of feature pairs of {target saline-alkali land baseline geomorphic state local feature vector, target saline-alkali land geomorphic state local feature vector after treatment} that constitute the causal chain unit is subjected to multi-dimensional joint perception aggregation processing, and the feature pairs are effectively integrated, so that the final generated strong causal joint perception coding feature map of the target saline-alkali land before and after treatment can more comprehensively and accurately reflect the topographic changes and causal relationships before and after the saline-alkali land treatment, provide rich and reliable feature information for the treatment effect evaluation, and improve the accuracy and credibility of the evaluation results.

[0055] In the above-mentioned saline-alkali land information monitoring and governance system, the governance effect evaluation module 150 is used to determine the evaluation result of the governance effect based on the strong causal joint perception coding feature map of the target saline-alkali land before and after governance. In a specific example of the present application, the governance effect evaluation module 150 is used to: input the strong causal joint perception coding feature map of the target saline-alkali land before and after governance into the governance effect intelligent evaluation module based on the classifier to obtain the evaluation result, and the evaluation result is used to indicate whether the governance effect meets the preset requirements. Here, the strong causal joint perception coding feature map of the target saline-alkali land before and after governance contains the strong causal correlation information of the topographic and geomorphic features before and after governance. After receiving it as input, the governance effect intelligent evaluation module based on the classifier automatically judges whether the governance effect reaches the expected goal by learning the correlation change pattern of the topographic and geomorphic features before and after governance contained in the strong causal joint perception coding feature map of the target saline-alkali land before and after governance, and gives the corresponding evaluation result. If the evaluation results show that the governance effect meets the preset requirements, it means that the governance measures are effective and can be further promoted and applied; if the evaluation results show that the governance effect does not meet the preset requirements, it is necessary to review the governance plan and adjust the governance strategy in order to achieve better results in subsequent governance work.

[0056] Specifically, the classifier first expands the strong causal joint perception coding feature map of the target saline-alkali land before and after governance into a strong causal joint perception coding feature vector of the target saline-alkali land before and after governance, and then uses the fully connected layer of the governance effect intelligent evaluation module to fully connect the target saline-alkali land before and after governance strong causal joint perception coding feature vector to obtain the target saline-alkali land before and after governance strong causal joint perception fully connected coding feature vector, and then inputs the target saline-alkali land before and after governance strong causal joint perception fully connected coding feature vector into the Softmax classification function of the governance effect intelligent evaluation module to obtain the probability value of the target saline-alkali land before and after governance strong causal joint perception coding feature vector belonging to each classification label, wherein the classification label includes the governance effect meets the preset requirements and the governance effect does not meet the preset requirements, and finally the classification label corresponding to the largest one of the probability values ​​is determined as the evaluation result.

[0057] When the governance effect meets the preset requirements, it means that the governance measures taken have achieved the expected goals to a large extent, such as significantly reducing soil salinity, increasing vegetation coverage, or improving soil structure. At this point, it is important to identify which specific strategies and methods have played a key role in achieving success. For example, if it is found that the planting of a certain type of plant significantly promotes vegetation recovery, it will become particularly important to further study the ecological adaptability and growth requirements of this plant. In addition, it is necessary to examine the synergistic effects between different governance measures and understand how they work together to achieve the best results. Through such analysis, not only can a set of effective governance solutions be summarized, but it can also provide reference for governance projects in other similar environments. At the same time, consideration should be given to expanding the scope of application of this governance model, promoting it to areas with similar conditions, and making appropriate adjustments based on specific circumstances. In order to ensure long-term effects, a continuous monitoring mechanism should also be established to regularly evaluate governance results and promptly identify and solve new problems that may arise.

[0058] On the other hand, if the governance effect fails to meet the preset requirements, it is necessary to analyze the reasons in more detail. First, it is possible to check whether there are deviations or deficiencies in the data collection and processing process. For example, whether the quality of the original data is high enough, whether the feature selection is appropriate, whether the sample set used for model training is representative, etc. Omissions in any link may cause the final evaluation results to deviate from the actual situation. Secondly, starting from the governance measures themselves, examine whether there are certain aspects that have not been considered or implemented in place. For example, whether the layout of the irrigation system is reasonable, whether the selected plant species are suitable for local climatic conditions, whether the land management measures are appropriate, etc. In response to these problems, new technologies and methods may need to be introduced to optimize existing governance solutions. For example, more advanced sensor technology can be used to monitor soil moisture and salinity changes in real time, or to explore emerging governance methods such as bioremediation.

[0059] In addition, when faced with poor governance results, interdisciplinary cooperation is particularly critical. Inviting experts from different fields to participate in discussions and provide insights from their professional perspectives will help to form a comprehensive and balanced solution. For example, agricultural scientists can give advice on crop rotation systems; soil scientists can recommend suitable amendments based on the characteristics of different types of soil; and ecologists are responsible for evaluating the effectiveness of biodiversity conservation measures. By integrating the wisdom of all parties, a more scientific and reasonable governance strategy can be formulated.

[0060] In summary, the saline-alkali land information monitoring and management system based on the embodiment of the present application is explained, which uses a camera to collect the overall terrain data of the target saline-alkali land before and after management, and introduces an image processing algorithm based on deep learning to extract features from the overall terrain data before and after management, so as to capture the topographic and geomorphic characteristics of the target saline-alkali land before and after management, respectively, and then use the topographic and geomorphic characteristics of the saline-alkali land before management as the baseline state, and perform causal alignment analysis on the topographic and geomorphic characteristics of the saline-alkali land after management and the baseline state to dig out the potential differences between the two, thereby realizing an intelligent evaluation of the management effect. In this way, by considering the topographic and geomorphic information before and after the management of the saline-alkali land, the management effect can be analyzed more accurately, avoiding the impact of short-term fluctuations in soil physical property parameters on the management effect evaluation.

[0061] Furthermore, a method for monitoring and managing saline-alkali land information is also provided.

[0062] Figure 5 Flow chart of the method for monitoring and managing saline-alkali land information according to an embodiment of the present application. Figure 5 As shown, the saline-alkali land information monitoring and management method includes the following steps: S1, obtaining the overall terrain data of the target saline-alkali land before management collected by a camera; S2, obtaining the overall terrain data of the target saline-alkali land after management collected by the camera; S3, extracting topographic and geomorphic features from the overall terrain data before management and the overall terrain data after management to obtain a target saline-alkali land topographic and geomorphic baseline state feature coding map and a target saline-alkali land topographic and geomorphic state feature coding map after management; S4, performing a causal alignment analysis based on topographic and geomorphic features on the target saline-alkali land topographic and geomorphic baseline state feature coding map and the target saline-alkali land topographic and geomorphic state feature coding map after management to obtain a target saline-alkali land strong causal joint perception coding feature map before and after management; S5, determining the evaluation result of the management effect based on the target saline-alkali land strong causal joint perception coding feature map before and after management.

[0063] Here, those skilled in the art will appreciate that the specific operations of each step in the above-mentioned saline-alkali land information monitoring and management method have been described in detail above. Figures 1 to 4 The description of the saline-alkali land information monitoring and management system has been introduced in detail, and therefore, its repeated description will be omitted.

[0064] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0065] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0066] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0067] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0068] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A saline-alkali land information monitoring and management system, characterized in that: include: A module for acquiring overall terrain data before treatment, used for acquiring overall terrain data of the target saline-alkali land before treatment collected by a camera; A post-treatment overall terrain data acquisition module, used to acquire the post-treatment overall terrain data of the target saline-alkali land collected by the camera; A topographic and geomorphic feature extraction module is used to extract topographic and geomorphic features from the overall topographic data before treatment and the overall topographic data after treatment to obtain a target saline-alkali land topographic and geomorphic baseline state feature coding map and a target saline-alkali land topographic and geomorphic state feature coding map after treatment; A causal alignment analysis module is used to perform a causal alignment analysis based on topographic and geomorphic characteristics on the target saline-alkali land topography baseline state characteristic coding map and the target saline-alkali land topography after governance state characteristic coding map to obtain a strong causal joint perception coding characteristic map before and after governance of the target saline-alkali land; The control effect evaluation module is used to determine the evaluation result of the control effect based on the strong causal joint perception coding feature map before and after the control of the target saline-alkali land.

2. The saline-alkali land information monitoring and management system according to claim 1 is characterized in that: The camera is a high-definition camera mounted on a drone.

3. The saline-alkali land information monitoring and management system according to claim 2 is characterized in that: The topographic and geomorphic feature extraction module is used to: A terrain feature extractor based on the FPT model is used to perform image feature extraction on the overall terrain data before treatment and the overall terrain data after treatment to obtain a baseline state feature coding map of the target saline-alkali land terrain and a state feature coding map of the target saline-alkali land terrain after treatment.

4. The saline-alkali land information monitoring and management system according to claim 3 is characterized in that: The causal alignment analysis module comprises: A causal association strength measurement unit is used to measure the causal association strength of the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map based on local fine-grained decoupling features to obtain a set of closed-loop strength factors of the causal chain unit of the local geomorphic features before and after the governance of the target saline-alkali land; A strong causal association pairing unit is used to perform strong causal association pairing based on the local fine-grained decoupling feature on the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map based on the set of closed-loop strength factors of the causal chain unit of the local geomorphic characteristics before and after the governance of the target saline-alkali land, so as to obtain a set of {target saline-alkali land baseline geomorphic state local feature vector, target saline-alkali land after governance geomorphic state local feature vector} feature pairs constituting the causal chain unit; The strong causal joint perception unit is used to perform multi-dimensional joint perception aggregation processing on the set of feature pairs of {target saline-alkali land baseline geomorphic state local feature vector, target saline-alkali land geomorphic state local feature vector after treatment} constituting the causal chain unit to obtain the strong causal joint perception coding feature map of the target saline-alkali land before and after treatment.

5. The saline-alkali land information monitoring and management system according to claim 4 is characterized in that: The causal association strength measurement unit comprises: A feature decoupling subunit is used to perform feature decoupling on the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map along the channel dimension of the target saline-alkali land topography baseline state feature coding map and the target saline-alkali land topography after governance state feature coding map to obtain a set of local feature vectors of the target saline-alkali land baseline geomorphic state and a set of local feature vectors of the target saline-alkali land after governance state feature coding map; The alternative feature extraction subunit is used to extract the alternative target saline-alkali land baseline geomorphic state local feature vector and the alternative target saline-alkali land post-treatment geomorphic state local feature vector from the set of the target saline-alkali land baseline geomorphic state local feature vector and the set of the target saline-alkali land post-treatment geomorphic state local feature vector respectively; The association strength calculation subunit is used to measure the causal association strength of the local feature vector of the baseline geomorphic state of the alternative target saline-alkali land and the local feature vector of the geomorphic state of the alternative target saline-alkali land after treatment to obtain the closed-loop strength factor of the causal chain unit of the local geomorphic characteristics of the target saline-alkali land before and after treatment.

6. The saline-alkali land information monitoring and management system according to claim 5 is characterized in that: The association strength calculation subunit is used to: Constructing a semantic alignment modulation flow field between the local feature vector of the baseline geomorphic state of the candidate target saline-alkali land and the local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment; Based on the semantic alignment modulated flow field, feature alignment mapping is performed on the local feature vector of the baseline geomorphic state of the candidate target saline-alkali land and the local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment to obtain the aligned local feature vector of the baseline geomorphic state of the candidate target saline-alkali land and the aligned local feature vector of the geomorphic state of the candidate target saline-alkali land after treatment; The local feature vector of the baseline geomorphic state of the aligned alternative target saline-alkali land and the local feature vector of the geomorphic state of the aligned alternative target saline-alkali land after treatment are input into the causal chain unit screening network to obtain the causal chain unit closed-loop strength factor of the local geomorphic characteristics of the target saline-alkali land before and after treatment.

7. The saline-alkali land information monitoring and management system according to claim 6 is characterized in that: The strong causal association pairing unit is used to: Based on the comparison between the closed-loop strength factor of the causal chain unit of the local geomorphological characteristics of the target saline-alkali land before and after treatment and a preset threshold, it is determined whether the local feature vector of the baseline geomorphological state of the alternative target saline-alkali land and the local feature vector of the geomorphological state of the alternative target saline-alkali land after treatment constitute a causal chain unit.

8. The saline-alkali land information monitoring and management system according to claim 7 is characterized in that: The strong causal joint perception unit is used to: Input each of the feature pairs of {target saline-alkali land baseline geomorphic state local feature vector, target saline-alkali land post-treatment geomorphic state local feature vector} constituting the causal chain unit into the causal chain unit semantic dynamic response network respectively to obtain a set of strong causal joint perception coding feature vectors of the target saline-alkali land pre-treatment and post-treatment geomorphic features; The set of strong causal joint perception coding feature vectors of the target saline-alkali land before and after the treatment is subjected to feature aggregation to obtain a strong causal joint perception coding feature map of the target saline-alkali land before and after the treatment.

9. The saline-alkali land information monitoring and management system according to claim 8 is characterized in that: The governance effect evaluation module is used to: The strong causal joint perception coding feature map of the target saline-alkali land before and after treatment is input into the classifier-based treatment effect intelligent evaluation module to obtain the evaluation result, and the evaluation result is used to indicate whether the treatment effect meets the preset requirements.

10. A method for monitoring and managing saline-alkali land information, characterized in that: include: Obtaining the overall topographic data of the target saline-alkali land before treatment collected by the camera; Acquiring the overall terrain data of the target saline-alkali land after treatment collected by the camera; Extracting topographic and geomorphic features from the overall topographic data before treatment and the overall topographic data after treatment to obtain a target saline-alkali land topographic and geomorphic baseline state feature coding map and a target saline-alkali land topographic and geomorphic state feature coding map after treatment; Performing a causal alignment analysis based on topographic and geomorphic features on the target saline-alkali land topography baseline state characteristic coding map and the target saline-alkali land topography after governance state characteristic coding map to obtain a strong causal joint perception coding characteristic map of the target saline-alkali land before and after governance; Based on the strong causal joint perception coding feature map before and after the treatment of the target saline-alkali land, the evaluation result of the treatment effect is determined.

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