Regional scale-based soil heavy metal ecological damage identification and evaluation method and system

Abstract

The invention relates to the field of intelligent detection, and provides a soil heavy metal ecological damage identification and evaluation method and system based on a regional scale, and the method comprises the steps: carrying out the region division of a to-be-evaluated region, so as to obtain a plurality of region units; the heavy metal content of each region unit in the plurality of region units is analyzed by using an ICP-MS detection technology to obtain detection results of the plurality of region units, and the detection results of the heavy metal content of each region unit are embedded and coded by using a data analysis technology based on deep learning; and automatically judging whether the soil heavy metal ecological damage condition of the first region unit is abnormal or not according to the query matching expression between the first region unit and the semantic embedding characteristics of the heavy metal content of each region. Therefore, on-site rapid detection of the soil sample can be realized, and the evaluation accuracy can be improved by more finely capturing the subtle change of the heavy metal content in the soil.

Description

Technical Field

[0001] The present application relates to the field of intelligent detection, and more specifically, to a method and system for identifying and evaluating soil heavy metal ecological damage based on a regional scale. Background Art

[0002] With the accelerated development of industrialization, agricultural activities and urbanization, soil pollution has become increasingly prominent. Heavy metal pollution is an important aspect of this. Once heavy metals such as lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), etc. enter the soil, they will accumulate in the environment for a long time and are not easy to decompose, causing serious damage to the ecosystem. For example, heavy metals can affect plant growth, change the structure of soil microbial communities, and even enter the human body through the food chain, endangering human health. Therefore, the monitoring and assessment of soil heavy metal pollution has become particularly important.

[0003] However, the traditional method of identifying and assessing ecological damage of heavy metals in soil relies on manual sampling and laboratory analysis, which makes the whole process time-consuming and inefficient. From sampling to laboratory testing to data analysis, each link requires a lot of time and manpower, and it is impossible to achieve rapid assessment. In addition, traditional methods usually use limited sampling points to represent the entire area, resulting in the inability to perform fine-grained analysis of local areas. Specifically, due to the limited number of sampling points, it is impossible to capture subtle changes in the heavy metal content in the soil, especially near the pollution source or at the pollution boundary, and it is easy to miss high-concentration areas or transition zones at the edge of pollution, so that the accuracy and reliability of the assessment results are limited.

[0004] Therefore, an optimized identification and assessment scheme for ecological damage of heavy metals in soil is desired. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present application provides a method and system for identifying and evaluating ecological damage of heavy metals in soil based on a regional scale.

[0006] A method for identifying and assessing ecological damage of heavy metals in soil based on a regional scale, comprising:

[0007] Divide the area to be evaluated into regions to obtain multiple regional units;

[0008] Detecting the heavy metal content of each of the multiple regional units to obtain heavy metal content detection results of the multiple regional units;

[0009] Performing high-dimensional embedding coding on each of the plurality of regional unit heavy metal content detection results to obtain semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results;

[0010] Performing query matching implicit optimization on the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit among the multiple regional units and the semantic embedding coding vectors of the heavy metal content detection results of the multiple regional units to obtain an implicit optimization representation of the query matching of the heavy metal content of the first regional unit, including: processing the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit to obtain a semantic value feature vector of the first regional unit detection result; using the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit as a query vector, the semantic value feature vector of the first regional unit detection result as a value vector, and the semantic embedding coding vectors of the multiple regional unit heavy metal content detection result as a sequence of key vectors, performing attention gating analysis based on local fields on the sequence of the query vector, the value vector, and the key vector to obtain an implicit optimization representation of the query matching of the heavy metal content of the first regional unit;

[0011] Based on the query matching implicit optimization representation of the heavy metal content of the first area unit, an evaluation result is obtained.

[0012] A soil heavy metal ecological damage identification and assessment system based on regional scale, comprising:

[0013] An area division module to be evaluated is used to divide the area to be evaluated into multiple area units;

[0014] A regional unit heavy metal content detection module, used to detect the heavy metal content of each regional unit in the multiple regional units to obtain multiple regional unit heavy metal content detection results;

[0015] A regional unit heavy metal content detection data embedding coding module is used to perform high-dimensional embedding coding on each of the multiple regional unit heavy metal content detection results to obtain multiple regional unit heavy metal content detection result semantic embedding coding vectors;

[0016] A query matching optimization module for an area unit heavy metal content coding vector, used for performing query matching implicit optimization on a semantic embedding coding vector of an area unit heavy metal content detection result corresponding to a first area unit among the multiple area units and the semantic embedding coding vectors of the multiple area unit heavy metal content detection results to obtain an implicitly optimized representation of the query matching of the first area unit heavy metal content, including: an area unit detection result semantic value feature generation unit, used for processing the semantic embedding coding vector of an area unit heavy metal content detection result corresponding to the first area unit to obtain a semantic value feature vector of the first area unit detection result; an implicitly optimized representation generation unit for query matching of an area unit heavy metal content, used for using the semantic embedding coding vector of an area unit heavy metal content detection result corresponding to the first area unit as a query vector, the semantic value feature vector of the first area unit detection result as a value vector, and the semantic embedding coding vectors of the multiple area unit heavy metal content detection results as a key vector sequence, and performing attention gating analysis based on a local domain on the sequence of the query vector, the value vector, and the key vector to obtain an implicitly optimized representation of the query matching of the first area unit heavy metal content;

[0017] An evaluation result generation module is used to obtain an evaluation result based on the query matching implicit optimization representation of the heavy metal content of the first regional unit.

[0018] This application has significant technical effects due to the adoption of the above technical solutions:

[0019] The method and system for identifying and evaluating the ecological damage of heavy metals in soil based on regional scale provided by the present application divide the area to be evaluated into multiple regional units, and use ICP-MS detection technology to analyze the heavy metal content of each of the multiple regional units to obtain the detection results of multiple regional units, and use data analysis technology based on deep learning to embed the detection results of the heavy metal content of each regional unit, so as to automatically judge whether there is an abnormality in the ecological damage of heavy metals in the soil of the first regional unit according to the query matching representation between the semantic embedding features of the heavy metal content of the first regional unit and each region. In this way, rapid on-site detection of soil samples can be achieved, and by more finely capturing the subtle changes in the heavy metal content in the soil, it is conducive to improving the accuracy of the assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1 The present invention is a flowchart of a method for identifying and assessing soil heavy metal ecological damage based on a regional scale according to an embodiment of the present application.

[0022] Figure 2 This is a data flow diagram of a method for identifying and assessing ecological damage of heavy metals in soil at a regional scale according to an embodiment of the present application.

[0023] Figure 3 In a method for identifying and assessing ecological damage of heavy metals in soil based on a regional scale according to an embodiment of the present application, a flowchart is provided for performing implicit optimization of query matching on a semantic embedded coding vector of a regional unit heavy metal content detection result corresponding to a first regional unit among the multiple regional units and the semantic embedded coding vectors of the heavy metal content detection results of the multiple regional units to obtain an implicit optimized representation of the query matching of the heavy metal content of the first regional unit.

[0024] Figure 4 In the soil heavy metal ecological damage identification and assessment method based on regional scale according to an embodiment of the present application, the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit is used as a query vector, the semantic value feature vector of the first regional unit detection result is used as a value vector and the semantic embedding coding vectors of the multiple regional unit heavy metal content detection results are used as a sequence of key vectors. The sequence of the query vector, the value vector and the key vector is subjected to attention gating analysis based on the local field to obtain a flowchart of the implicit optimization representation of the query matching of the first regional unit heavy metal content.

[0025] Figure 5 It is a system block diagram of a soil heavy metal ecological damage identification and assessment system based on a regional scale according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] 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.

[0027] With the rapid development of industrialization, agriculture and urbanization, the problem of heavy metal pollution in soil has become increasingly serious, among which heavy metals such as lead, cadmium, mercury and arsenic are particularly critical. These heavy metals are difficult to degrade in the soil and accumulate over a long period of time, causing continuous damage to the ecosystem. They not only affect plant growth and soil microbial structure, but may also enter the human body through the food chain, posing a threat to human health. Therefore, it is particularly urgent to effectively monitor and evaluate soil heavy metal pollution.

[0028] However, traditional methods for identifying and assessing ecological damage of heavy metals in soil rely on manual sampling and laboratory analysis, which is a time-consuming and inefficient process. From sampling to analysis to data interpretation, each step requires a lot of time and manpower, making it difficult to achieve rapid assessment. In addition, traditional methods usually conduct regional representative analysis based on limited sampling points, which limits detailed research on local areas. Due to the limited number of sampling points, this method may not be able to accurately capture small changes in the content of heavy metals in the soil, especially near pollution sources or in polluted areas, and it is easy to ignore high-concentration areas or pollution transition zones, thereby affecting the accuracy and reliability of the assessment results.

[0029] In view of the above technical problems, the technical concept of the present application is to divide the area to be evaluated into multiple regional units, and use ICP-MS detection technology to analyze the heavy metal content of each of the multiple regional units to obtain the detection results of multiple regional units, and use data processing and analysis technology based on deep learning to embed the detection results of the heavy metal content of each regional unit, so as to automatically judge whether there is an abnormality in the soil heavy metal ecological damage of the first regional unit according to the query matching representation between the semantic embedding features of the heavy metal content of the first regional unit and each region. In this way, through automated sampling equipment and advanced detection technology, rapid on-site detection of soil samples can be achieved. In addition, by obtaining multiple regional units through regional division, it is possible to more finely analyze the subtle changes in the heavy metal content in the soil, capture the complex relationship between different heavy metal elements, thereby improving the accuracy of the assessment and realizing intelligent soil heavy metal ecological damage assessment.

[0030] Figure 1 The present invention is a flowchart of a method for identifying and assessing soil heavy metal ecological damage based on a regional scale according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the soil heavy metal ecological damage identification and assessment method based on regional scale according to the embodiment of the present application. Figure 1 and Figure 2As shown, according to the method for identifying and assessing ecological damage of heavy metals in soil based on regional scale in accordance with an embodiment of the present application, the method includes: S110, dividing the area to be assessed into multiple regional units; S120, detecting the heavy metal content of each of the multiple regional units to obtain multiple regional unit heavy metal content detection results; S130, respectively performing high-dimensional embedding coding on each of the multiple regional unit heavy metal content detection results to obtain multiple regional unit heavy metal content detection results semantic embedding coding vectors; S140, performing query matching implicit optimization on the regional unit heavy metal content detection result semantic embedding coding vector corresponding to the first regional unit among the multiple regional units and the multiple regional unit heavy metal content detection result semantic embedding coding vectors to obtain a first regional unit heavy metal content query matching implicit optimization representation; S150, obtaining an assessment result based on the first regional unit heavy metal content query matching implicit optimization representation.

[0031] In step S110, the area to be evaluated is divided into regions to obtain a plurality of regional units. It should be understood that soil properties vary depending on geographical location, land use pattern and environmental conditions. In order to help identify these differences and thus more accurately evaluate the soil, the area to be evaluated can be divided into regions. Regional division divides the area to be evaluated into a plurality of smaller and more manageable units, performs more detailed heavy metal detection in each unit, and analyzes the heavy metal detection results of each unit, which can improve the accuracy and reliability of the evaluation results.

[0032] In step S120, the heavy metal content of each of the multiple regional units is detected to obtain the heavy metal content detection results of the multiple regional units. Specifically, in the embodiment of the present application, the heavy metal content of each of the multiple regional units is detected to obtain the heavy metal content detection results of the multiple regional units, including: using I CP-MS detection technology to analyze the heavy metal content of each of the multiple regional units to obtain the heavy metal content detection results of the multiple regional units. It should be understood that in order to evaluate the heavy metal pollution status of the soil in a specific area to be evaluated, to identify the pattern and potential risk points of heavy metal pollution in the soil, and thus to determine whether the soil is affected by heavy metal pollution, in the technical solution of the present application, the heavy metal content of each of the multiple regional units is analyzed by I CP-MS detection technology to obtain the heavy metal content detection results of the multiple regional units. I CP-MS is an analytical instrument that combines inductively coupled plasma (I CP) technology and mass spectrometry (MS). In the ICP part of the analytical instrument, a high-frequency radio frequency signal with strong power is applied to the inductor coil to form a high-temperature plasma inside the coil. This high-temperature plasma can ionize an electron from most elements in the sample to form monovalent positive ions. In the MS part of the instrument, ions with different mass-to-nuclear ratios are selected to pass through to detect the intensity of a certain ion, and then the intensity of a certain element is analyzed and calculated. ICP-MS technology has extremely high sensitivity, can detect very low concentrations of heavy metal elements, and supports the simultaneous determination of multiple elements. Compared with traditional heavy metal detection methods, ICP-MS has a faster analysis speed. It usually only takes a few hours from sample processing to result acquisition, which is suitable for large-scale monitoring. In addition, the analysis process of ICP-MS can be standardized, which reduces operational errors and improves the reliability of heavy metal content detection results.

[0033] In step S130, high-dimensional embedding coding is performed on each of the multiple regional unit heavy metal content detection results to obtain multiple regional unit heavy metal content detection result semantic embedding coding vectors. Specifically, in an embodiment of the present application, high-dimensional embedding coding is performed on each of the multiple regional unit heavy metal content detection results to obtain multiple regional unit heavy metal content detection result semantic embedding coding vectors, including: using a heavy metal content embedding matrix to perform high-dimensional embedding coding on each of the multiple regional unit heavy metal content detection results to obtain the multiple regional unit heavy metal content detection result semantic embedding coding vectors. It should be understood that each of the multiple regional unit heavy metal content detection results contains semantic information about the detection of heavy metal content in the area, such as element type, concentration value, etc. Based on this, in the technical solution of the present application, a heavy metal content embedding matrix is ​​used to perform high-dimensional embedding coding on the heavy metal content detection results of each of the multiple regional unit heavy metal content detection results so as to better capture the complex semantic relationship between the metals in the region, such as the distribution pattern in different regional units, and obtain semantic embedding coding vectors of the heavy metal content detection results of multiple regional units.

[0034] In step S140, query matching implicit optimization is performed on the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit among the multiple regional units and the semantic embedding coding vector of the heavy metal content detection result of the multiple regional units to obtain the query matching implicit optimization representation of the heavy metal content of the first regional unit. Accordingly, considering that the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit among the multiple regional units contains the comprehensive information of the heavy metal content of the first regional unit, it not only includes the concentration information of various heavy metal elements detected, but also includes the context information carried by these information after encoding. The semantic embedding coding vector of the heavy metal content detection result of the multiple regional units is a sequence composed of the semantic embedding coding vectors of all regional units, each vector in this sequence corresponds to a specific regional unit, and contains a high-dimensional embedding representation of the heavy metal content detection result of the regional unit. Therefore, in order to more carefully analyze and evaluate the matching relationship between each regional unit and the entire region, the similarity or correlation between the first regional unit and other regional units can be accurately identified and characterized, so as to evaluate whether there is an abnormality in the ecological damage of soil heavy metals in the first regional unit. In the technical solution of the present application, query matching implicit optimization is performed on a sequence of feature vectors composed of a semantic embedded coding vector of a regional unit heavy metal content detection result corresponding to a first regional unit among the multiple regional units and the semantic embedded coding vectors of the multiple regional unit heavy metal content detection results to obtain a first regional unit heavy metal content query matching implicit optimization representation vector as a first regional unit heavy metal content query matching implicit optimization representation.

[0035] Specifically, first, the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit is embedded and coded to extract the key semantics of the heavy metal content in the first regional unit, and obtain the semantic value feature vector of the first regional unit detection result. Then, considering that if the value vector, the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit (query vector) and the semantic embedding coding vector of the multiple regional unit heavy metal content detection results (sequence of key vectors) are directly processed, the important local features in each key vector will be ignored, and the complex relationship between the data cannot be well captured. Therefore, in order to analyze the association within the local neighborhood of each region in a more fine-grained manner, further, the semantic embedding coding vector of the multiple regional unit heavy metal content detection results is one-dimensionally convolutionally encoded to capture the characteristic pattern in the local area and identify the change law of the heavy metal content between adjacent regional units, and obtain the sequence of semantic association feature vectors of the local neighborhood of the regional unit heavy metal content detection results, which provides support for subsequent more refined attention query matching. Then, the semantic embedding coding vectors of the heavy metal content detection results of multiple regional units and the sequence of semantic association feature vectors of the local neighborhood of the query vector regional unit heavy metal content detection results are used as the sequence of key vectors, the semantic embedding coding vector of the regional unit heavy metal content detection results corresponding to the first regional unit is used as the query vector, and the semantic value feature vector of the first regional unit detection results is used as the value vector, which are respectively input into the first and second multi-head attention query modules based on the converter structure to obtain the sequence of implicit vectors of the semantic granularity query matching of the regional unit heavy metal content detection results and the sequence of implicit vectors of the semantic neighborhood granularity query matching of the regional unit heavy metal content detection results. That is, the first multi-head attention query module focuses on analyzing and extracting the characteristics of the key vector itself, emphasizing the importance of the intrinsic attributes of the key vector, thereby capturing the unique properties of each regional unit. The second multi-head attention query module focuses on the characteristics of the key vector and its surrounding neighborhood. By considering the relationship between the key vector and the neighboring vector, it emphasizes the importance of the mutual connection and influence between regional units, so as to better understand the interaction and dependence of regional units in a larger range. Subsequently, the mean vector of the sequence of implicit vectors of semantic granularity query matching of regional unit heavy metal content detection results and the sequence of implicit vectors of semantic neighborhood granularity query matching of regional unit heavy metal content detection results are calculated, and the two mean vectors are fused to obtain a query matching representation that can fully reflect the query matching between the first area and the entire area, thereby obtaining an implicit optimized representation vector of the query matching of the first regional unit heavy metal content.

[0036] Specifically, Figure 3The present invention is a flowchart of performing implicit optimization of query matching on the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit among the multiple regional units and the semantic embedding coding vector of the heavy metal content detection result of the multiple regional units in the method for identifying and assessing the ecological damage of heavy metals in soil based on the regional scale according to the embodiment of the present application to obtain the implicit optimization representation of the query matching of the heavy metal content of the first regional unit. Figure 3 As shown, query matching implicit optimization is performed on the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit among the multiple regional units and the semantic embedding coding vectors of the heavy metal content detection results of the multiple regional units to obtain the first regional unit heavy metal content query matching implicit optimization representation. It includes: S141, processing the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit to obtain the first regional unit detection result semantic value feature vector; S142, using the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit as the query vector, the first regional unit detection result semantic value feature vector as the value vector and the multiple regional unit heavy metal content detection result semantic embedding coding vectors as the sequence of key vectors, and performing attention gating analysis based on the local field on the sequence of the query vector, the value vector and the key vector to obtain the first regional unit heavy metal content query matching implicit optimization representation.

[0037] More specifically, in an embodiment of the present application, the semantic embedding coding vector of the area unit heavy metal content detection result corresponding to the first area unit is processed to obtain a semantic value feature vector of the first area unit detection result, including: using a value embedding coding matrix to process the semantic embedding coding vector of the area unit heavy metal content detection result corresponding to the first area unit to obtain a semantic value feature vector of the first area unit detection result.

[0038] More specifically, Figure 4 In the soil heavy metal ecological damage identification and assessment method based on regional scale according to the embodiment of the present application, the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit is used as the query vector, the semantic value feature vector of the first regional unit detection result is used as the value vector and the semantic embedding coding vectors of the plurality of regional unit heavy metal content detection result are used as the sequence of key vectors, and the sequence of the query vector, the value vector and the key vector are subjected to attention gating analysis based on the local domain to obtain a flowchart of the implicit optimization representation of the query matching of the first regional unit heavy metal content. Figure 4As shown, the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit is used as a query vector, the semantic value feature vector of the first regional unit detection result is used as a value vector and the semantic embedding coding vectors of the multiple regional unit heavy metal content detection results are used as a sequence of key vectors, and the sequence of the query vector, the value vector and the key vector are subjected to attention gating analysis based on the local field to obtain the implicit optimization representation of the query matching of the first regional unit heavy metal content, including: S1421, one-dimensional convolution coding is performed on the semantic embedding coding vectors of the multiple regional unit heavy metal content detection results to obtain the regional A sequence of local neighborhood semantic association feature vectors of unit heavy metal content detection results; S1422, using the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit as a query vector, the semantic value feature vector of the first regional unit detection result as a value vector and the semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results as a sequence of key vectors, inputting the sequence of the query vector, the value vector and the key vector into a first multi-head attention query module based on a converter structure to obtain a sequence of semantic granularity query matching implicit vectors of regional unit heavy metal content detection results; S1423, using the The semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit is used as a query vector, the semantic value feature vector of the first regional unit detection result is used as a value vector, and the sequence of the local neighborhood semantic association feature vectors of the regional unit heavy metal content detection result is used as a sequence of key vectors, and the sequence of the query vector, the value vector and the key vector is input into a second multi-head attention query module based on a converter structure to obtain a sequence of implicit vectors for semantic neighborhood granularity query matching of the regional unit heavy metal content detection result; S1424, calculating the sequence of implicit vectors for semantic neighborhood granularity query matching of the regional unit heavy metal content detection result The column and the positional mean vector of the sequence of the semantic neighborhood granularity query matching implicit vectors of the regional unit heavy metal content detection results are obtained to obtain the regional unit heavy metal content detection result semantic granularity query matching feature vector and the regional unit heavy metal content detection result semantic neighborhood granularity query matching feature vector; S1425, fusing the regional unit heavy metal content detection result semantic granularity query matching feature vector and the regional unit heavy metal content detection result semantic neighborhood granularity query matching feature vector to obtain the first regional unit heavy metal content query matching implicit optimization representation vector as the first regional unit heavy metal content query matching implicit optimization representation.

[0039] More specifically, in an embodiment of the present application, the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit is used as a query vector, the semantic value feature vector of the first regional unit detection result is used as a value vector and the semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results are used as a sequence of key vectors, and the sequence of the query vector, the value vector and the key vector are input into a first multi-head attention query module based on a converter structure to obtain a sequence of semantic granularity query matching implicit vectors of the regional unit heavy metal content detection result, including: calculating the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit and the semantic embedding coding vector of the regional unit heavy metal content detection result The product of the transpose vectors of the vectors, dividing the obtained semantic association matrix of the first regional unit heavy metal content detection result by the square root of the length of the semantic embedding coding vector of the regional unit heavy metal content detection result by position to obtain the semantic association modulation matrix of the first regional unit heavy metal content detection result; inputting the semantic association modulation matrix of the first regional unit heavy metal content detection result into the softmax function to obtain the semantic association activation matrix of the first regional unit heavy metal content detection result; multiplying the semantic association activation matrix of the first regional unit heavy metal content detection result by the semantic value feature vector of the first regional unit detection result to obtain the semantic granularity query matching implicit vector of the regional unit heavy metal content detection result.

[0040] More specifically, in an embodiment of the present application, the semantic granularity query matching feature vector of the regional unit heavy metal content detection result and the semantic neighborhood granularity query matching feature vector of the regional unit heavy metal content detection result are merged to obtain a first regional unit heavy metal content query matching implicit optimization representation vector, including: respectively calculating the positional subtraction, positional dot multiplication and positional addition between the semantic granularity query matching feature vector of the regional unit heavy metal content detection result and the semantic neighborhood granularity query matching feature vector of the regional unit heavy metal content detection result to obtain a regional unit heavy metal content detection result semantic granularity-neighborhood granularity difference vector, a regional unit heavy metal content detection result semantic granularity-neighborhood granularity dot product vector and a regional unit heavy metal content detection result semantic granularity-neighborhood granularity difference vector. The method comprises the following steps: obtaining a semantic granularity-neighborhood granularity sum vector of the result; cascading the semantic granularity-neighborhood granularity difference vector of the regional unit heavy metal content detection result, the semantic granularity-neighborhood granularity dot product vector of the regional unit heavy metal content detection result and the semantic granularity-neighborhood granularity sum vector of the regional unit heavy metal content detection result to obtain a semantic granularity-neighborhood granularity cascade vector of the regional unit heavy metal content detection result; performing one-dimensional convolution encoding on the semantic granularity-neighborhood granularity cascade vector of the regional unit heavy metal content detection result, and performing local window-based maximum pooling processing on the obtained semantic granularity-neighborhood granularity convolution encoding vector of the regional unit heavy metal content detection result to obtain the first regional unit heavy metal content query matching implicit optimization representation vector.

[0041] In the embodiment of the present application, specifically, query matching implicit optimization is performed on a sequence of feature vectors consisting of a semantic embedding coding vector of a regional unit heavy metal content detection result corresponding to a first regional unit among the multiple regional units and a semantic embedding coding vector of a plurality of regional unit heavy metal content detection results, which can be expressed as:

[0042] v v =v q W q +b q

[0043] K={k1,k2,...,k i ,...,k n}

[0044] K′=Conv 1×l ({k1,k2,...,k i ,...,k n})

[0045] K′={k1′,k2′,...,k i ′,...,k m ′}

[0046]

[0047] Among them, v q is the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit, W q is the value embedding encoding matrix, b q is the value bias vector, v v is the semantic value feature vector of the detection result of the first area unit, K represents the semantic embedding coding vector of the heavy metal content detection results of the multiple area units, k1, k2, ..., k i ,...,k n is the semantic embedding coding vector of the first, second, ..., i-th, ..., n-th regional unit heavy metal content detection result in the plurality of regional unit heavy metal content detection result semantic embedding coding vectors, Conv 1×l (·) is a one-dimensional convolutional code, l is an integer multiple of the length of the semantic embedding coding vector of the heavy metal content detection result of the regional unit, K′ is a sequence of local neighborhood semantic association feature vectors of the heavy metal content detection result of the regional unit, k1′, k2′, ..., k i ′,...,k m ′ is the first, second, ..., i-th, ..., m-th local neighborhood semantic association feature vector of the heavy metal content detection result of the regional unit in the sequence of the local neighborhood semantic association feature vector of the heavy metal content detection result of the regional unit, k i T and k i ' T k i and k i ′, d1 and d2 are the lengths of the semantic embedding encoding vector of the heavy metal content detection result of the regional unit and the local neighborhood semantic association feature vector of the heavy metal content detection result of the regional unit, softmax(·) is the softmax function, v ki is the semantic granularity query matching implicit vector of the heavy metal content detection result of the regional unit in the sequence of semantic granularity query matching implicit vectors of the heavy metal content detection result of the regional unit, n is the number of feature vectors in the sequence of semantic granularity query matching implicit vectors of the heavy metal content detection result of the regional unit, v ni is the semantic neighborhood granularity query matching implicit vector of the i-th regional unit heavy metal content detection result in the sequence of semantic neighborhood granularity query matching implicit vectors of the regional unit heavy metal content detection result, m is the number of feature vectors in the sequence of semantic neighborhood granularity query matching implicit vectors of the regional unit heavy metal content detection result, v k is the semantic granularity query matching feature vector of the heavy metal content detection result of the regional unit, vn is the semantic neighborhood granularity query matching feature vector of the heavy metal content detection result of the regional unit, ⊙ and are position-wise subtraction, position-wise multiplication, and position-wise addition, respectively. [·,·,·] is a cascade process. conv1D(·) is a one-dimensional convolutional encoding operation. MaxPool(·) is a maximum pooling operation. p It is the implicit optimization representation vector of the query matching of the heavy metal content of the first regional unit.

[0048] In step S150, the implicit optimization representation is matched based on the query of the heavy metal content of the first regional unit to obtain an evaluation result. Specifically, in an embodiment of the present application, the implicit optimization representation is matched based on the query of the heavy metal content of the first regional unit to obtain an evaluation result, including: inputting the implicit optimization representation vector of the query matching of the heavy metal content of the first regional unit into the ecological damage identification and evaluation module based on the classifier to obtain an evaluation result, and the evaluation result is used to indicate whether there is an abnormality in the ecological damage of heavy metals in the soil of the first regional unit. That is, the implicit optimization representation of the query matching of the first regional unit heavy metal content obtained by query matching optimization using the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit and the semantic embedding coding vector of the heavy metal content detection result of the multiple regional units is classified, so as to automatically determine whether there is an abnormality in the ecological damage of heavy metals in the soil of the first regional unit. In this way, through automated sampling equipment and advanced detection technology, rapid on-site detection of soil samples can be achieved. In addition, by obtaining multiple regional units through regional division, it is possible to more finely analyze the subtle changes in the heavy metal content in the soil, capture the complex relationship between different heavy metal elements, and thus improve the accuracy of the evaluation.

[0049] Here, in the technical solution of the present application, the semantic embedding coding vector of the heavy metal content detection result of the regional unit corresponding to the first regional unit among the multiple regional units is used as the query feature vector and the sequence of feature vectors composed of the semantic embedding coding vectors of the heavy metal content detection results of the multiple regional units is used as the sequence of imitation key feature vectors, and when the query feature vector and the sequence of imitation key feature vectors are implicitly optimized for query matching, it is considered that the query feature vector essentially belongs to the subset feature of the sequence of imitation key feature vectors, and there are relatively significant spatial distribution differences between each imitation key feature vector in the sequence of imitation key feature vectors. Therefore, when performing attention gated query encoding, the query matching results based on the local correlation space topology difference and the feature set distribution redundancy will also have cross-domain dynamic matching differences. Therefore, it is expected to improve the detail semantic aggregation expression effect of the implicit optimization representation vector of the query matching of the heavy metal content of the first regional unit based on the cross-dynamic attention gate encoding difference.

[0050] In a preferred example, the first area unit heavy metal content query matching implicit optimization representation vector is passed through a classifier-based ecological damage identification and assessment module to obtain an assessment result, including: calculating the sum of the absolute values ​​of each eigenvalue of the first area unit heavy metal content query matching implicit optimization representation vector to obtain the first area unit heavy metal content query matching implicit optimization and modulation value, and calculating the square root of the sum of the squares of each eigenvalue of the first area unit heavy metal content query matching implicit optimization representation vector to obtain the second area unit heavy metal content query matching implicit optimization and modulation value; performing dot-wise subtraction on the first area unit heavy metal content query matching implicit optimization representation vector and the second area unit heavy metal content query matching implicit optimization and modulation value, and then performing dot-wise multiplication with the number of eigenvalues ​​of the first area unit heavy metal content query matching implicit optimization representation vector and the reciprocal of the first area unit heavy metal content query matching implicit optimization and modulation value, and taking the reciprocal of each eigenvalue to obtain the first area unit heavy metal content query matching implicit optimization representation vector. Metal content query matching implicit optimization phase conversion vector; after subtracting the first area unit heavy metal content query matching implicit optimization representation vector from the first area unit heavy metal content query matching implicit optimization and modulation value, respectively perform dot multiplication with the square root of the number of eigenvalues ​​of the first area unit heavy metal content query matching implicit optimization representation vector and the reciprocal of the second area unit heavy metal content query matching implicit optimization and modulation value, and take the reciprocal of each eigenvalue to obtain the second area unit heavy metal content query matching implicit optimization phase conversion vector; subtract the dot product vector of the second area unit heavy metal content query matching implicit optimization phase conversion vector from the first area unit heavy metal content query matching implicit optimization phase conversion vector to obtain the optimized first area unit heavy metal content query matching implicit optimization representation vector; the optimized first area unit heavy metal content query matching implicit optimization representation vector is passed through the classifier-based ecological damage identification and assessment module to obtain the assessment result.

[0051] Here, the optimization representation of the implicit optimization representation vector matching the query of the heavy metal content in the first area unit is:

[0052]

[0053] v i ∈V∈R n

[0054] Wherein, V is the implicit optimization representation vector of the query matching of the heavy metal content in the first regional unit, v irepresents the eigenvalue of the ith position in the implicit optimization representation vector of the query matching of the heavy metal content of the first regional unit, n represents the number of eigenvalues ​​of the implicit optimization representation vector of the query matching of the heavy metal content of the first regional unit, α represents the implicit optimization and modulation value of the query matching of the heavy metal content of the first regional unit, β represents the implicit optimization and modulation value of the query matching of the heavy metal content of the second regional unit, ⊙ represents the point multiplication by position, Indicates point-by-point subtraction, [·] ⊙-1 is the inverse of each eigenvalue in the vector, V1 represents the implicit optimization phase transition vector of the query matching of the heavy metal content of the first area unit, V2 represents the implicit optimization phase transition vector of the query matching of the heavy metal content of the second area unit, ω represents the weighted hyperparameter, and V′ represents the optimized implicit optimization representation vector of the query matching of the heavy metal content of the first area unit.

[0055] In the preferred example, the difference of the feature value of the implicit optimization representation vector of the query matching of the first regional unit heavy metal content relative to the feature set of the entire vector of the implicit optimization representation vector of the query matching of the first regional unit heavy metal content is used as the semantic change intensity information, and the phase-like conversion corresponding to the position-based intensity modulation is performed through different and modulation representation forms, so that the aggregation enhancement of the semantic change phase perception can improve the axial aggregation receptive field along the feature aggregation direction by performing a spatial translation operation based on alternating stacking under the vector set scale balance of the implicit optimization representation vector of the query matching of the first regional unit heavy metal content, thereby improving the perception effect of the aggregation semantics of the implicit optimization representation vector of the query matching of the first regional unit heavy metal content for the semantic change of details, so as to improve the expression effect of the implicit optimization representation vector of the query matching of the first regional unit heavy metal content, and improve the accuracy of the evaluation result obtained by passing it through the ecological damage identification and evaluation module based on the classifier. In this way, through automated sampling equipment and advanced detection technology, on-site rapid detection of soil samples can be achieved. In addition, by dividing the region into multiple regional units, it is possible to more finely analyze the subtle changes in the heavy metal content in the soil and capture the complex relationship between different heavy metal elements, thereby improving the accuracy of the assessment.

[0056] In summary, the soil heavy metal ecological damage identification and assessment method based on the regional scale of the embodiment of the present application is explained, which divides the area to be assessed into multiple regional units, and uses ICP-MS detection technology to analyze the heavy metal content of each of the multiple regional units to obtain the detection results of multiple regional units, and uses data analysis technology based on deep learning to embed the detection results of the heavy metal content of each regional unit, so as to automatically judge whether there is an abnormality in the soil heavy metal ecological damage of the first regional unit based on the query matching representation between the first regional unit and the semantic embedding features of the heavy metal content of each region. In this way, rapid on-site detection of soil samples can be achieved, and by more finely capturing the subtle changes in the heavy metal content in the soil, it is conducive to improving the accuracy of the assessment.

[0057] Figure 5 FIG. 1 is a system block diagram of a soil heavy metal ecological damage identification and assessment system based on a regional scale according to an embodiment of the present application. Figure 5As shown, according to the regional scale-based soil heavy metal ecological damage identification and assessment system 100 of the embodiment of the present application, it includes: an area division module 110 to be assessed, which is used to divide the area to be assessed into multiple regional units; a regional unit heavy metal content detection module 120, which is used to detect the heavy metal content of each regional unit in the multiple regional units to obtain multiple regional unit heavy metal content detection results; a regional unit heavy metal content detection data embedding coding module 130, which is used to perform high-dimensional embedding coding on each regional unit heavy metal content detection result in the multiple regional unit heavy metal content detection results respectively to obtain multiple regional unit heavy metal content detection result semantic embedding coding vectors; a regional unit heavy metal content coding vector query matching optimization module 140, which is used to perform query matching implicit optimization on the regional unit heavy metal content detection result semantic embedding coding vector corresponding to the first regional unit in the multiple regional units and the multiple regional unit heavy metal content detection result semantic embedding coding vectors To obtain an implicitly optimized representation of a query match for heavy metal content in a first area unit, the method includes: an area unit detection result semantic value feature generation unit, which is used to process the semantic embedding coding vector of the area unit heavy metal content detection result corresponding to the first area unit to obtain the semantic value feature vector of the first area unit detection result; an area unit heavy metal content query match implicitly optimized representation generation unit, which is used to use the semantic embedding coding vector of the area unit heavy metal content detection result corresponding to the first area unit as a query vector, the semantic value feature vector of the first area unit detection result as a value vector and the plurality of area unit heavy metal content detection result semantic embedding coding vectors as a sequence of key vectors, and perform attention gating analysis based on a local domain on the sequence of the query vector, the value vector and the key vector to obtain an implicitly optimized representation of a query match for heavy metal content in the first area unit; and an evaluation result generation module 150, which is used to obtain an evaluation result based on the implicitly optimized representation of the query match for heavy metal content in the first area unit.

[0058] Here, those skilled in the art will appreciate that the specific functions and operations of the various units and modules in the regional scale soil heavy metal ecological damage identification and assessment system 100 have been described in the above reference. Figures 1 to 4 The description of the soil heavy metal ecological damage identification and assessment method based on the regional scale has been introduced in detail, and therefore, its repeated description will be omitted.

[0059] In summary, the regional scale soil heavy metal ecological damage identification and assessment system 100 based on the embodiment of the present application is explained, which divides the area to be assessed into multiple regional units, and uses ICP-MS detection technology to analyze the heavy metal content of each of the multiple regional units to obtain the detection results of multiple regional units, and uses data analysis technology based on deep learning to embed the detection results of the heavy metal content of each regional unit, so as to automatically judge whether there is an abnormality in the soil heavy metal ecological damage of the first regional unit according to the query matching representation between the first regional unit and the semantic embedding features of the heavy metal content of each region. In this way, rapid on-site detection of soil samples can be achieved, and by more finely capturing the subtle changes in the heavy metal content in the soil, it is conducive to improving the accuracy of the assessment.

[0060] As described above, the soil heavy metal ecological damage identification and assessment system 100 based on the regional scale according to the embodiment of the present application can be implemented in various wireless terminals, such as a server for soil heavy metal ecological damage identification and assessment based on the regional scale. In one example, the soil heavy metal ecological damage identification and assessment system 100 based on the regional scale according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the soil heavy metal ecological damage identification and assessment system 100 based on the regional scale can be a software module in the operating system of the wireless terminal, or it can be an application developed for the wireless terminal; of course, the soil heavy metal ecological damage identification and assessment system 100 based on the regional scale can also be one of the many hardware modules of the wireless terminal.

[0061] Alternatively, in another example, the regional-scale soil heavy metal ecological damage identification and assessment system 100 and the wireless terminal may also be separate devices, and the regional-scale soil heavy metal ecological damage identification and assessment system 100 may be connected to the wireless terminal via a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

Claims

1. A method for identifying and assessing ecological damage of heavy metals in soil based on regional scale, characterized in that: include: Divide the area to be evaluated into regions to obtain multiple regional units; Detecting the heavy metal content of each of the multiple regional units to obtain heavy metal content detection results of the multiple regional units; Performing high-dimensional embedding coding on each of the plurality of regional unit heavy metal content detection results to obtain semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results; Performing query matching implicit optimization on the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit among the multiple regional units and the semantic embedding coding vectors of the multiple regional unit heavy metal content detection results to obtain a query matching implicit optimization representation of the first regional unit heavy metal content, including: processing the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit to obtain a semantic value feature vector of the first regional unit detection result; Using the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit as a query vector, the semantic value feature vector of the first regional unit detection result as a value vector and the semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results as a sequence of key vectors, performing attention gating analysis based on a local domain on the sequence of the query vector, the value vector and the key vector to obtain an implicitly optimized representation of the query match of the first regional unit heavy metal content; Based on the query matching implicit optimization representation of the heavy metal content of the first area unit, an evaluation result is obtained.

2. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 1, characterized in that: The heavy metal content of each of the multiple regional units is detected to obtain the heavy metal content detection results of the multiple regional units, including: using ICP-MS detection technology to analyze the heavy metal content of each of the multiple regional units to obtain the heavy metal content detection results of the multiple regional units.

3. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 2 is characterized in that: High-dimensional embedding coding is performed on each of the multiple regional unit heavy metal content detection results respectively to obtain multiple regional unit heavy metal content detection result semantic embedding coding vectors, including: using a heavy metal content embedding matrix to perform high-dimensional embedding coding on each of the multiple regional unit heavy metal content detection results respectively to obtain multiple regional unit heavy metal content detection result semantic embedding coding vectors.

4. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 3 is characterized in that: The semantic embedding coding vector of the area unit heavy metal content detection result corresponding to the first area unit is processed to obtain the semantic value feature vector of the first area unit detection result, including: using the value embedding coding matrix to process the semantic embedding coding vector of the area unit heavy metal content detection result corresponding to the first area unit to obtain the semantic value feature vector of the first area unit detection result.

5. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 4 is characterized in that: Taking the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit as the query vector, the semantic value feature vector of the first regional unit detection result as the value vector and the semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results as a sequence of key vectors, performing attention gating analysis based on a local domain on the sequence of the query vector, the value vector and the key vector to obtain an implicit optimization representation of the query matching of the first regional unit heavy metal content, including: Performing one-dimensional convolution coding on the semantic embedding coding vectors of the heavy metal content detection results of the multiple regional units to obtain a sequence of local neighborhood semantic association feature vectors of the heavy metal content detection results of the regional units; Taking the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit as a query vector, the semantic value feature vector of the first regional unit detection result as a value vector and the semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results as a sequence of key vectors, inputting the sequence of the query vector, the value vector and the key vector into a first multi-head attention query module based on a converter structure to obtain a sequence of semantic granularity query matching implicit vectors of the regional unit heavy metal content detection result; Taking the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit as the query vector, the semantic value feature vector of the first regional unit detection result as the value vector and the sequence of the local neighborhood semantic association feature vectors of the regional unit heavy metal content detection result as the sequence of key vectors, the query vector, the value vector and the sequence of the key vectors are input into a second multi-head attention query module based on a converter structure to obtain a sequence of implicit vectors of the semantic neighborhood granularity query matching of the regional unit heavy metal content detection result; Calculate the positional mean vector of the sequence of the semantic granularity query matching implicit vectors of the regional unit heavy metal content detection result and the sequence of the semantic neighborhood granularity query matching implicit vectors of the regional unit heavy metal content detection result to obtain the regional unit heavy metal content detection result semantic granularity query matching feature vector and the regional unit heavy metal content detection result semantic neighborhood granularity query matching feature vector; The semantic granularity query matching feature vector of the regional unit heavy metal content detection result and the semantic neighborhood granularity query matching feature vector of the regional unit heavy metal content detection result are fused to obtain a first regional unit heavy metal content query matching implicit optimization representation vector as the first regional unit heavy metal content query matching implicit optimization representation.

6. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 5, characterized in that: The scale of the one-dimensional convolutional coding is equal to an integer multiple of the length of the semantic embedding coding vector of the heavy metal content detection result of the regional unit.

7. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 6, characterized in that: Taking the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit as a query vector, the semantic value feature vector of the first regional unit detection result as a value vector and the semantic embedding coding vectors of the plurality of regional unit heavy metal content detection results as a sequence of key vectors, inputting the sequence of the query vector, the value vector and the key vector into a first multi-head attention query module based on a converter structure to obtain a sequence of semantic granularity query matching implicit vectors of the regional unit heavy metal content detection result, including: Calculate the product of the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit and the transposed vector of the semantic embedding coding vector of the regional unit heavy metal content detection result, and divide the obtained first regional unit heavy metal content detection result semantic association matrix by the square root of the length of the regional unit heavy metal content detection result semantic embedding coding vector by position to obtain the first regional unit heavy metal content detection result semantic association modulation matrix; Inputting the semantic association modulation matrix of the heavy metal content detection results of the first regional unit into a softmax function to obtain a semantic association activation matrix of the heavy metal content detection results of the first regional unit; The semantic association activation matrix of the first area unit heavy metal content detection result is multiplied by the semantic value feature vector of the first area unit detection result to obtain the semantic granularity query matching implicit vector of the area unit heavy metal content detection result.

8. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 7, characterized in that: The semantic granularity query matching feature vector of the regional unit heavy metal content detection result and the semantic neighborhood granularity query matching feature vector of the regional unit heavy metal content detection result are integrated to obtain a first regional unit heavy metal content query matching implicit optimization representation vector, including: Respectively calculating the positional subtraction, positional dot product and positional addition between the semantic granularity query matching feature vector of the regional unit heavy metal content detection result and the semantic neighborhood granularity query matching feature vector of the regional unit heavy metal content detection result to obtain the regional unit heavy metal content detection result semantic granularity-neighborhood granularity difference vector, the regional unit heavy metal content detection result semantic granularity-neighborhood granularity dot product vector and the regional unit heavy metal content detection result semantic granularity-neighborhood granularity sum vector; Cascade processing is performed on the difference vector of the regional unit heavy metal content detection result semantic granularity-neighborhood granularity, the dot product vector of the regional unit heavy metal content detection result semantic granularity-neighborhood granularity and the sum vector of the regional unit heavy metal content detection result semantic granularity-neighborhood granularity to obtain a cascade vector of the regional unit heavy metal content detection result semantic granularity-neighborhood granularity; After performing one-dimensional convolution encoding on the semantic granularity-neighborhood granularity cascade vector of the heavy metal content detection result of the regional unit, the obtained semantic granularity-neighborhood granularity convolution encoding vector of the heavy metal content detection result of the regional unit is subjected to local window-based maximum pooling processing to obtain the implicit optimization representation vector of the query matching of the heavy metal content of the first regional unit.

9. The method for identifying and assessing soil heavy metal ecological damage based on regional scale according to claim 8, characterized in that: Based on the query matching implicit optimization representation of the heavy metal content in the first area unit, an evaluation result is obtained, including: inputting the implicit optimization representation vector of the query matching implicit optimization representation of the heavy metal content in the first area unit into an ecological damage identification and assessment module based on a classifier to obtain an evaluation result, and the evaluation result is used to indicate whether there is any abnormality in the ecological damage situation of soil heavy metals in the first area unit.

10. A soil heavy metal ecological damage identification and assessment system based on regional scale, characterized in that: include: An area division module to be evaluated is used to divide the area to be evaluated into multiple area units; A regional unit heavy metal content detection module, used to detect the heavy metal content of each regional unit in the multiple regional units to obtain multiple regional unit heavy metal content detection results; A regional unit heavy metal content detection data embedding coding module is used to perform high-dimensional embedding coding on each of the multiple regional unit heavy metal content detection results to obtain multiple regional unit heavy metal content detection result semantic embedding coding vectors; The query matching optimization module of the regional unit heavy metal content coding vector is used to perform query matching implicit optimization on the semantic embedding coding vector of the regional unit heavy metal content detection result corresponding to the first regional unit among the multiple regional units and the semantic embedding coding vectors of the multiple regional unit heavy metal content detection results to obtain the first regional unit heavy metal content query matching implicit optimization representation, including: a regional unit detection result semantic value feature generation unit, used to process the regional unit heavy metal content detection result semantic embedding coding vector corresponding to the first regional unit to obtain the first regional unit detection result semantic value feature vector; A region unit heavy metal content query matching implicit optimization representation generation unit is used to use the region unit heavy metal content detection result semantic embedding coding vector corresponding to the first region unit as a query vector, the first region unit detection result semantic value feature vector as a value vector and the plurality of region unit heavy metal content detection result semantic embedding coding vectors as a sequence of key vectors, and perform attention gating analysis based on a local domain on the sequence of the query vector, the value vector and the key vector to obtain the first region unit heavy metal content query matching implicit optimization representation; An evaluation result generation module is used to obtain an evaluation result based on the query matching implicit optimization representation of the heavy metal content of the first regional unit.

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