Power transmission and transformation project environment water conservation risk early warning analysis method based on multi-factor index evaluation

Abstract

The invention provides a power transmission and transformation project environmental water conservation risk early warning analysis method based on multi-factor index evaluation, and the method comprises the steps: carrying out the field collection of a construction region according to the risk evaluation demand corresponding to each risk factor, obtaining corresponding field data, and carrying out the independent scoring of each field data, obtaining a factor risk value corresponding to each risk factor in the construction area, performing overall risk assessment on the factor risk values by using an exponential-Golomb coding method to obtain an overall risk index of the construction area, determining an overall risk level of the construction area, and performing numerical optimization on each factor risk value to obtain an overall risk index of the construction area; the method comprises the steps of obtaining a level risk value of a corresponding risk factor, determining an early warning project corresponding to a construction area according to a risk level, adjusting an early warning sequence of the early warning project according to the level risk value, generating a risk early warning scheme of the construction area, and carrying out early warning. The method can comprehensively and accurately evaluate the environmental water protection risk of the power transmission and transformation project, and timely sends an early warning signal.

Description

Technical Field

[0001] This invention relates to the field of construction environmental and water conservation analysis and early warning technology, and in particular to a method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index assessment. Background Technology

[0002] Traditional environmental and water conservation supervision and management during the construction phase relies mainly on manual inspections and post-event handling, which is significantly delayed and costly. Existing methods are largely based on qualitative judgments, lacking quantitative assessment standards and making it difficult to objectively measure risk levels and trends. Furthermore, some construction companies still adhere to the outdated concept of "construction first, remediation later," only initiating environmental and water conservation remediation and ecological restoration after construction is completed or even after an environmental incident occurs. This passive remediation model is not only costly and slow in ecological restoration, but also causes irreversible negative impacts on the local environment. Therefore, how to dynamically and accurately identify and assess risks during construction, thereby achieving pre-event warnings and in-event control, and minimizing construction-related environmental pollution and soil erosion, has become an urgent technical challenge.

[0003] Therefore, this invention provides a method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation. Summary of the Invention

[0004] This invention provides a method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index assessment. This method can comprehensively and accurately assess the environmental and water conservation risks of power transmission and transformation projects and issue early warning signals in a timely manner.

[0005] This invention provides a method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation, including: Step 1: Collect data on-site in the construction area according to the risk assessment requirements corresponding to each risk factor, and obtain on-site data corresponding to each risk factor; Step 2: Independently score each of the aforementioned field data to obtain the factor risk value corresponding to each of the aforementioned risk factors for the construction area; Step 3: Use the index Columbus coding method to conduct an overall risk assessment of the risk values ​​of the factors to obtain the overall risk index of the construction area; Step 4: Determine the overall risk level of the construction area based on the overall risk index, and perform numerical optimization on the risk value of each factor to obtain the level risk value of the corresponding risk factor; Step 5: Determine the early warning items corresponding to the construction area based on the risk level, adjust the early warning order of the early warning items according to the risk level value, generate a risk early warning plan for the construction area, and issue an early warning.

[0006] In one feasible approach Step 1 includes: Step 11: Search for each of the aforementioned risk factors in the big data to obtain several related factor information corresponding to each of the aforementioned risk factors. Construct several matching risk features corresponding to the aforementioned risk factors based on the related factor information. Create risk assessment requirements corresponding to the aforementioned risk factors based on the matching risk features. Step 12: Conduct pre-monitoring of the construction area to obtain on-site environmental and water conservation samples of the construction area. Match each risk assessment requirement with the on-site environmental and water conservation samples to obtain the monitoring location corresponding to each risk assessment requirement and generate a risk monitoring example map of the construction area. Step 13: Conduct on-site monitoring of the construction area according to the risk monitoring example diagram, match the monitoring data with the corresponding risk assessment requirements, perform redundancy cleaning on the monitoring data according to the risk assessment requirements, and statistically analyze several cleaned monitoring data corresponding to each risk factor to generate corresponding on-site data.

[0007] In one feasible approach The process of redundancy cleaning of the monitoring data according to the risk assessment requirements includes: Based on the risk assessment requirements, an assessment knowledge graph corresponding to the risk factors is constructed. Several key assessment points of the risk factors are identified in the assessment knowledge graph, and the point relationships between different key assessment points are determined. Based on the matching relationship between the monitoring data and the risk assessment requirements and the point relationship between different key assessment points, determine the matching key assessment point corresponding to each monitoring data and the associated key assessment point corresponding to each monitoring data. The monitoring data is cleaned for the first time using the evaluation content corresponding to the matching key evaluation points to obtain the corresponding initial cleaned data. The initial cleaned data is then labeled using the corresponding associated key evaluation points. When the labeling relationship of the data labeling results is consistent with the corresponding point relationship, the data cleaning is completed. Conversely, the same data segments between the initial cleaned data and the corresponding monitoring data are regarded as allowed cleaned data segments, and the allowed cleaned data segments are cleaned a second time using the corresponding matching key evaluation points.

[0008] In one feasible approach Step 2 includes: Step 21: Generate a corresponding numerical classification standard based on the risk classification standard corresponding to each risk factor. When the numerical classification standard contains fuzzy values, search for several numerical classification standards corresponding to the risk factor in the big data, and perform numerical clustering on the numerical classification standards using the grey weighted clustering analysis method. Step 22: Construct an effective numerical grading standard for the risk factors based on the clustering results, use the numerical grading standard to score the field data, obtain several risk factor levels for the construction area, and generate a risk assessment report for the construction area. Step 23: Based on the risk assessment report, derive the level risk value corresponding to each risk factor, construct the grading threshold corresponding to each numerical grading standard, optimize the accuracy of the level risk value, and generate the factor risk value corresponding to each risk factor.

[0009] In one feasible approach Step 3 includes: Step 31: Determine the grading standard corresponding to each risk factor according to the risk assessment requirements corresponding to each risk factor, identify the risk level of each factor's risk value under the corresponding risk factor, and encode each risk level using the index Columbus coding method to obtain the corresponding risk code; Step 32: Calculate the overall risk index of the construction area using formula (1); (1) in, This represents the overall risk index of the construction area, with a value range of [0, 7000]. This indicates the risk code corresponding to the first risk factor. This indicates the risk code corresponding to the second risk factor. This indicates the risk code corresponding to the third risk factor. This indicates the risk code corresponding to the fourth risk factor. This indicates the risk code corresponding to the fifth risk factor. This indicates the risk code corresponding to the sixth risk factor. This indicates the risk code corresponding to the seventh risk factor. This represents the risk code corresponding to the eighth risk factor, and int represents the floor function. Step 33: Obtain the calculation result of formula (1), determine the risk-related information of the construction area, and construct the corresponding risk resolution text and risk response text for display.

[0010] In one feasible approach Step 4 includes: Step 41: Divide the construction area into risk levels according to the overall risk index, determine the overall risk level of the construction area, and construct an environmental protection and water conservation risk model for the construction area based on the overall risk level. Step 42: Input the risk value of each of the aforementioned factors into the environmental protection and water conservation risk model to conduct risk simulation and obtain the risk impact of each of the aforementioned risk factors on the construction area; Step 43: In the environmental protection and water conservation risk model, describe the data impact of each risk to obtain the level risk value corresponding to each risk factor.

[0011] In one feasible approach Also includes: Calculate the factor risk value and level risk value corresponding to each of the aforementioned risk factors, and construct a risk comparison table for the construction area. Obtain construction information for power transmission and transformation projects, and analyze the characteristics of several construction impacts of power transmission and transformation projects on the construction area in conjunction with the aforementioned risk comparison table; The construction impact characteristics are marked in the construction area, the impact characteristics of different environmental sub-areas in the construction area are determined, and a risk visualization of the construction area is constructed and displayed.

[0012] In one feasible approach Step 5 includes: Step 51: Determine the triggered warning items in the construction area according to the risk level, set the corresponding first warning sequence for the corresponding risk factors according to the risk value of the factors, and configure the corresponding triggered warning items for each risk factor. Step 52: Sort the triggered warning items according to the first warning order to generate a first warning plan. Use the level risk value to evaluate the process risk of the initial warning plan to obtain several execution risk process points of the initial warning plan and the execution risk value corresponding to each execution risk process point. Step 53: Adjust the first warning sequence according to the execution risk value until the total execution risk value is minimized, obtain the second warning sequence corresponding to each of the triggered warning items, generate a risk warning scheme for the construction area, and issue a risk warning for the construction area according to the risk warning scheme.

[0013] In one feasible approach Also includes: Determine the early warning level for the construction area based on the risk level, and issue an early warning instruction; The system controls the equipment in the area corresponding to the warning level to respond to the warning command and perform the corresponding warning work.

[0014] The beneficial effects of the above technical solution are as follows: To integrate environmental protection and soil conservation concepts into the power transmission and transformation construction process and reduce the impact of construction on environmental pollution and soil erosion, firstly, on-site data is collected according to the risk assessment requirements corresponding to each risk factor. This ensures that the acquired on-site data is highly matched with the risk factors, avoids interference from irrelevant data, and provides accurate and effective basic data for subsequent assessments, improving the practicality and reliability of the data. Then, each on-site data is independently scored to obtain the factor risk value, which can eliminate the mutual influence between different risk factors and obtain the specific risk status of the construction area under each risk factor. Finally, the indexed Golomb coding method is used to conduct an overall risk assessment of the factor risk values, resulting in... Once the overall risk situation of the construction area is assessed, the overall risk level is determined based on the overall risk index. Simultaneously, the risk values ​​of individual factors are numerically optimized to obtain the level risk value. This clarifies the overall risk level of the construction area and more accurately reflects the level characteristics of each risk factor, providing a more detailed basis for subsequent early warnings. Finally, early warning items are determined based on the risk level, and the order of early warnings is adjusted according to the level risk value. This ensures that the generated risk warning plan prioritizes high-level risk factors, ensuring that the warnings are focused and logically ordered. Relevant personnel can take timely and effective countermeasures based on the warning plan, improving the efficiency and targeting of risk warnings and helping to quickly reduce the risks in the construction area.

[0015] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the workflow of the environmental and water conservation risk early warning analysis method for power transmission and transformation projects based on multi-factor index evaluation in an embodiment of the present invention; Figure 2 This is a schematic diagram of the workflow of step 2 in the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation in an embodiment of the present invention. Detailed Implementation

[0018] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0019] Example 1: This example provides a method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation, such as... Figure 1 As shown, it includes: Step 1: Collect data on-site in the construction area according to the risk assessment requirements corresponding to each risk factor, and obtain on-site data corresponding to each risk factor; Step 2: Independently score each of the aforementioned field data to obtain the factor risk value corresponding to each of the aforementioned risk factors for the construction area; Step 3: Use the index Columbus coding method to conduct an overall risk assessment of the risk values ​​of the factors to obtain the overall risk index of the construction area; Step 4: Determine the overall risk level of the construction area based on the overall risk index, and perform numerical optimization on the risk value of each factor to obtain the level risk value of the corresponding risk factor; Step 5: Determine the early warning items corresponding to the construction area based on the risk level, adjust the early warning order of the early warning items according to the risk level value, generate a risk early warning plan for the construction area, and issue an early warning.

[0020] In this example, risk factors include: construction disturbance, tower foundation and site offset, slope runoff, engineering measures or facilities, temporary measures or facilities, vegetation restoration, environmental events and other additional disturbances; In this example, independent scoring means designing a scoring rule for each risk factor, quantifying the score based on the actual situation or severity of each risk factor, and the higher the score, the higher the risk. In this example, the field data refers to the data collected in the construction area that is related to the risk factors; In this example, the factor risk value represents the risk value presented by the construction area under a risk factor; In this example, the role of the index Columbus coding method is as follows: because this coding method has the characteristics of efficient data compression and processing, it can reduce information loss when integrating multi-factor risk values, so that the overall risk index can more objectively reflect the comprehensive risk situation of the construction area. In this example, the warning project refers to a project that provides risk warnings for the construction area.

[0021] The working principle and beneficial effects of the above technical solution are as follows: To integrate environmental protection and water conservation concepts into power transmission and transformation construction and reduce the impact of construction on environmental pollution and soil erosion, on-site data is first collected according to the risk assessment requirements corresponding to each risk factor. This ensures that the acquired on-site data is highly matched with the risk factors, avoids interference from irrelevant data, and provides accurate and effective basic data for subsequent assessments, improving the practicality and reliability of the data. Then, each on-site data is independently scored to obtain the factor risk value, which can eliminate the mutual influence between different risk factors and obtain the specific risk status of the construction area under each risk factor. Finally, the index Golomb coding method is used to conduct an overall risk assessment of the factor risk values. By obtaining a comprehensive risk assessment of the construction area, the overall risk level is determined based on the overall risk index. Simultaneously, the risk values ​​of individual factors are numerically optimized to obtain the level risk values. This clarifies the overall risk level of the construction area and more accurately reflects the level characteristics of each risk factor, providing a more detailed basis for subsequent early warnings. Finally, early warning items are determined based on the risk level, and the order of early warnings is adjusted according to the level risk values. This ensures that the generated risk warning plan prioritizes high-level risk factors, ensuring that the warnings are focused and logically ordered. Relevant personnel can take timely and effective countermeasures based on the warning plan, improving the efficiency and relevance of risk warnings and helping to quickly reduce the risks in the construction area.

[0022] Example 2: Based on Example 1, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation, step 1 includes: Step 11: Search for each of the aforementioned risk factors in the big data to obtain several related factor information corresponding to each of the aforementioned risk factors. Construct several matching risk features corresponding to the aforementioned risk factors based on the related factor information. Create risk assessment requirements corresponding to the aforementioned risk factors based on the matching risk features. Step 12: Conduct pre-monitoring of the construction area to obtain on-site environmental and water conservation samples of the construction area. Match each risk assessment requirement with the on-site environmental and water conservation samples to obtain the monitoring location corresponding to each risk assessment requirement and generate a risk monitoring example map of the construction area. Step 13: Conduct on-site monitoring of the construction area according to the risk monitoring example diagram, match the monitoring data with the corresponding risk assessment requirements, perform redundancy cleaning on the monitoring data according to the risk assessment requirements, and statistically analyze several cleaned monitoring data corresponding to each risk factor to generate corresponding on-site data.

[0023] In this example, the relevant factor information represents information related to risk factors in big data; In this example, the purpose of searching for relevant factors in big data is: 1. to fully explore the potential correlations and characteristics of risk factors by leveraging the breadth and depth of big data, so that risk assessment needs are more in line with actual conditions; 2. to keep abreast of the latest relevant concepts updated in big data. In this example, the matching risk feature represents the characteristics exhibited by risks that match risk factors; In this example, pre-monitoring refers to the process of collecting environmental data about the construction area; In this example, the risk monitoring sample diagram represents the result of summarizing the monitoring information required for each monitoring location in the construction area, which can clearly guide monitoring personnel to collect data at key locations.

[0024] The working principle and beneficial effects of the above technical solution are as follows: By using big data to find relevant information about risk factors, matching risk characteristics is constructed and risk assessment requirements are created. Then, by pre-monitoring the construction area, on-site environmental and water conservation samples are obtained. The risk assessment requirements are matched with the samples to determine the monitoring locations and generate risk monitoring example maps. This ensures that the collected samples are highly correlated with the risk assessment requirements, reducing invalid monitoring and improving monitoring efficiency. Further, after conducting on-site monitoring based on the risk monitoring example maps, the monitoring data is cleaned to remove redundancy, and then matched with the risk assessment requirements to generate on-site data. Through this method, the risk assessment requirements can be analyzed in detail, making the on-site data corresponding to each risk factor clear and logical, thus improving the reliability of the entire risk early warning analysis method.

[0025] Example 3: Based on Example 2, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index assessment includes a process of redundancy cleaning of the monitoring data according to the risk assessment requirements, comprising: Based on the risk assessment requirements, an assessment knowledge graph corresponding to the risk factors is constructed. Several key assessment points of the risk factors are identified in the assessment knowledge graph, and the point relationships between different key assessment points are determined. Based on the matching relationship between the monitoring data and the risk assessment requirements and the point relationship between different key assessment points, determine the matching key assessment point corresponding to each monitoring data and the associated key assessment point corresponding to each monitoring data. The monitoring data is cleaned for the first time using the evaluation content corresponding to the matching key evaluation points to obtain the corresponding initial cleaned data. The initial cleaned data is then labeled using the corresponding associated key evaluation points. When the labeling relationship of the data labeling results is consistent with the corresponding point relationship, the data cleaning is completed. Conversely, the same data segments between the initial cleaned data and the corresponding monitoring data are regarded as allowed cleaned data segments, and the allowed cleaned data segments are cleaned a second time using the corresponding matching key evaluation points.

[0026] In this example, the key evaluation points represent the key points that need to be analyzed when evaluating the construction area using risk factors; In this example, the point relationship represents the logical relationship between different key evaluation points; In this example, the associated key evaluation point represents a key evaluation point that has a point relationship with the matching key evaluation point; In this example, the purpose of building a knowledge graph before data cleaning is to comprehensively sort out the core evaluation dimensions and interrelationships of risk factors, so that the cleaning process closely follows the essence of risk assessment needs, avoids the accidental deletion of effective data or the retention of redundant data due to fuzzy cleaning standards, and improves the accuracy of cleaning.

[0027] The working principle and beneficial effects of the above technical solution are as follows: By constructing a knowledge graph for risk factor evaluation, key evaluation points and their relationships are clarified. Then, by determining the matching key evaluation points and associated key evaluation points for each monitoring data, initial cleaning is performed to quickly remove redundant data that is irrelevant to the core evaluation requirements. If necessary, secondary cleaning is performed on the same data segments of the initial cleaned data and the original monitoring data to avoid data distortion caused by repeated cleaning. This provides reliable data support for subsequent steps such as single-factor independent scoring and overall evaluation using the index Columbus coding method, thereby improving the accuracy and effectiveness of the environmental protection and water conservation risk early warning analysis method for the entire power transmission and transformation project.

[0028] Example 4: Based on Example 1, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation, step 2, is as follows: Figure 2 As shown, it includes: Step 21: Generate a corresponding numerical classification standard based on the risk classification standard corresponding to each risk factor. When the numerical classification standard contains fuzzy values, search for several numerical classification standards corresponding to the risk factor in the big data, and perform numerical clustering on the numerical classification standards using the grey weighted clustering analysis method. Step 22: Construct an effective numerical grading standard for the risk factors based on the clustering results, use the numerical grading standard to score the field data, obtain several risk factor levels for the construction area, and generate a risk assessment report for the construction area. Step 23: Based on the risk assessment report, derive the level risk value corresponding to each risk factor, construct the grading threshold corresponding to each numerical grading standard, optimize the accuracy of the level risk value, and generate the factor risk value corresponding to each risk factor.

[0029] In this example, fuzzy numerical values ​​represent grading values ​​for which the numerical grading standard cannot be determined. In this example, the grey weighted clustering analysis method represents the process of first adding grey weights to the numerical partitioning criteria, and then performing clustering. In this example, the risk rating represents the standard for classifying risk factors into risk levels.

[0030] The working principle and beneficial effects of the above technical solution are as follows: To address potential ambiguities in numerical grading standards, relevant numerical classification standards are located using big data. Then, gray weighted clustering analysis is employed for numerical clustering. Based on the effective numerical grading standards obtained from clustering, the on-site data is scored. Combined with the risk factor levels, a risk assessment report is generated, accurately reflecting the actual situation of the construction area under various risk factors. Furthermore, the risk assessment report is used to derive the risk level values, and the accuracy is optimized using grading thresholds. Finally, factor risk values ​​are generated. This approach ensures the correlation between risk values ​​and actual risks based on the assessment report, while also improving the accuracy of the values ​​through grading thresholds. This allows the factor risk values ​​to more realistically and accurately reflect the risk level of each risk factor, laying a high-quality foundation for subsequent overall risk assessment and early warning scheme generation.

[0031] Example 5: Based on Example 1, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation, step 3 includes: Step 31: Determine the grading standard corresponding to each risk factor according to the risk assessment requirements corresponding to each risk factor, identify the risk level of each factor's risk value under the corresponding risk factor, and encode each risk level using the index Columbus coding method to obtain the corresponding risk code; Step 32: Calculate the overall risk index of the construction area using formula (1); (1) in, This represents the overall risk index of the construction area, with a value range of [0, 7000]. This indicates the risk code corresponding to the first risk factor. This indicates the risk code corresponding to the second risk factor. This indicates the risk code corresponding to the third risk factor. This indicates the risk code corresponding to the fourth risk factor. This indicates the risk code corresponding to the fifth risk factor. This indicates the risk code corresponding to the sixth risk factor. This indicates the risk code corresponding to the seventh risk factor. This represents the risk code corresponding to the eighth risk factor, and int represents the floor function. Step 33: Obtain the calculation result of formula (1), determine the risk-related information of the construction area, and construct the corresponding risk resolution text and risk response text for display.

[0032] In this example, the first risk factor is construction disturbance, which has five classification standards: NA=-1: The actual construction disturbance area is controlled within the approved environmental impact assessment and water conservation plan, that is, it does not exceed the standard; =0: The actual construction disturbance slightly exceeds the approved area (0 < the proportion of construction disturbance area exceeding the standard < 10%). =1: The actual construction disturbance moderately exceeds the approved area (10% ≤ the proportion of construction disturbance area exceeding the standard < 20%), and there is a small area of ​​disturbance exceeding the standard; =2: The actual construction disturbance is more serious than the approved area (20% ≤ the proportion of construction disturbance area exceeding the standard < 30%), and there is a large area of ​​disturbance exceeding the standard; =3: The actual construction disturbance seriously exceeds the approved area (the proportion of construction disturbance area exceeding the standard is ≥30%), and there is a large area of ​​excessive disturbance. The second risk factor is the offset of the tower base and station site, which has five classification standards, namely: =-1: The actual station site, tower base location, and route length are completely consistent with the approved route, with no deviation, and the route does not change when passing through sensitive areas or key areas for soil and water conservation or key treatment areas; =0: The line is slightly deviated, the actual path length of the transmission line increases by more than the approved path length (<10%), or the lateral displacement of the line in the hilly area exceeds 300 meters and the cumulative length of that part of the line reaches (<10%), or the lateral displacement of the transmission line exceeds 500 meters and the cumulative length reaches (<10%) of the original path length, or the site displacement does not exceed 200 meters. =1: Moderate line deviation, the actual path length of the transmission line increases by more than the approved line length (10%-20%), or the lateral displacement of part of the line in the hilly area exceeds 300 meters and the cumulative length reaches the length of that part of the line (10%-20%), or the cumulative lateral displacement of the transmission line exceeds 500 meters and the cumulative length reaches the original path length (10%-20%), or the site deviation distance is 200-400 meters; =2: The line deviation is relatively serious, the actual path length of the transmission line increases by more than the approved line length (20%-30%), or the lateral displacement of some lines in hilly areas exceeds 300 meters and the cumulative length reaches the length of that part of the line (20%-30%), or the cumulative length of the lateral displacement of the transmission line exceeds 500 meters and the cumulative length reaches the original path length (20%-30%), or the site displacement is 400-500 meters; =3: The line is seriously deviated, the actual path length of the transmission line increases by more than the approved line length (≥30%), or the lateral displacement of part of the line in the hilly area exceeds 300 meters and the cumulative length of that part of the line reaches (≥30%), or the cumulative length of the lateral displacement of the transmission line exceeds 500 meters and exceeds the original path length (≥30%), or the site displacement exceeds 500 meters, or the transmission and transformation project path, site, etc. change, resulting in entering a new ecologically sensitive area, or the project disturbance newly involves key areas for soil and water conservation or key areas for treatment; The third risk factor is slope-side muck sluice handling, which has five classification standards, namely: =-1: There is no slag spillage along the slope in the project; =0: There are a small number of occasional downslope chutes (1 downslope chute), or the impact range is small (chute length <1m). =1: There are a small number of occasional downhill slag chutes (2 downhill slag chutes), or the impact range is small (1m≤slag chute length<3m). =2: There are obvious slope sludge chutes (3 sludge chutes), or the impact range is moderate (3m≤sludge chute length<5m), and there is a risk of soil erosion; =3: There are a large number of slope sludge chutes (≥4 locations), or the impact is severe (sludge chutes length ≥5m), causing significant soil and water loss; The fourth risk factor represents engineering measures or facilities, which have five classification standards: =-1: All engineering measures (such as retaining walls, drainage ditches, engineering slope protection, land consolidation, farmland restoration, accident oil tanks, sound barriers, box-in, sewage treatment, etc.) have been implemented in accordance with the requirements of the water conservation approval; =0: A small number of engineering measures were not fully implemented (0% < percentage of incomplete implementation < 20%), which does not affect the main function; =1: Some engineering measures were not implemented as required by the water conservation approval (20% ≤ the proportion of inadequate implementation < 40%), posing a risk of soil erosion; =2: Many engineering measures were not implemented as required by the water conservation approval (40% ≤ the proportion of inadequate implementation < 50%), posing a significant risk of soil erosion; =3: Multiple engineering measures have not been implemented (the proportion of inadequate implementation is ≥50%), or important engineering measures (retaining walls, drainage ditches) have not been implemented, posing significant soil erosion or environmental risks; The fifth risk factor represents temporary measures or facilities, which have five grading criteria: =-1: All temporary measures (such as temporary barriers, drainage, covering, paving, temporary greening, limiting the disturbance area, garbage cleaning, oil separation measures for oily machinery, sedimentation tanks, mud tanks, water spraying for dust suppression, car wash pools (at the substation), temporary toilets, etc.) have been implemented in accordance with the requirements of the water conservation approval. =0: A small number of temporary measures have minor deficiencies (0% < percentage of inadequate implementation < 20%), or the measures are not implemented properly (such as some areas not being covered, or minor blockage of drainage ditches), and the risk is controllable; =1: Some work sites lack temporary measures (20% ≤ inadequate implementation rate < 40%), posing a risk of soil erosion; =2: Many work sites lack temporary measures (40% ≤ the proportion of inadequate implementation < 50%), or key temporary measures (such as excavation face covering, temporary support for steep slopes) are missing, posing an immediate risk of soil erosion or dust. =3: Most work sites lack temporary measures (the proportion of inadequate implementation is ≥50%), causing significant environmental impacts (such as ditch erosion, mud entering rivers, and severe dust pollution). The sixth risk factor represents vegetation restoration, which has five grading criteria: =-1: The vegetation at the construction completion point (including temporary land occupation) has been well restored (vegetation restoration rate ≥ water conservation plan target value), and the coverage and survival rate meet the standards; =0: The restoration of some areas at the construction completion point is lagging behind (80%≤vegetation restoration rate<water conservation plan target value), but is within the reasonable maintenance period; =1: Many areas at the construction completion point were not restored in time (70%≤vegetation restoration rate<80%). =2: Large areas at the construction completion point were not restored in a timely manner (50%≤vegetation restoration rate<70%), resulting in exposed ground surfaces; =3: The vegetation in most areas at the construction completion point has not been restored (vegetation restoration rate <50%). The seventh risk factor represents environmental pollution events, which have two classification standards: =-1: No other environmental and water conservation risk events or problems were found, or the occurrence was minor; =3: Water / air / noise / solid waste pollution incidents occur, and complaints and administrative penalties are received, causing adverse effects; The eighth risk factor represents other interfering factors, which have different grading standards, and the corresponding level is uploaded by the manager.

[0033] The working principle and beneficial effects of the above technical solution are as follows: First, the grading standards are determined based on the risk assessment requirements. Then, the risk level is identified by combining the risk values ​​of factors. Next, the risk level is coded using the index Columbus coding method, which ensures that the coding results accurately map the risk level of each risk factor and avoids the problem of the coding being disconnected from the actual risk. Then, the overall risk index is calculated through a specific formula. The codes of multiple risk factors are accumulated in exponential form and rounded down, which integrates the risk information of each factor and clearly defines the range of overall risk through the numerical range. Managers can intuitively see the risk level in the construction area. Finally, risk-related information is determined based on the calculation results, and risk resolution text and risk response text are constructed and displayed. This not only allows managers to clearly understand the meaning behind the overall risk index, but also allows them to directly obtain the direction and measures for dealing with risks, transforming the abstract index into concrete action guidance and improving the practicality of the risk assessment results.

[0034] Example 6: Based on Example 1, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation, step 4 includes: Step 41: Divide the construction area into risk levels according to the overall risk index, determine the overall risk level of the construction area, and construct an environmental protection and water conservation risk model for the construction area based on the overall risk level. Step 42: Input the risk value of each of the aforementioned factors into the environmental protection and water conservation risk model to conduct risk simulation and obtain the risk impact of each of the aforementioned risk factors on the construction area; Step 43: In the environmental protection and water conservation risk model, describe the data impact of each risk to obtain the level risk value corresponding to each risk factor.

[0035] The working principle and beneficial effects of the above technical solution are as follows: Based on the overall risk index, the risk level of the construction area is divided into categories, the overall risk level is determined, and an environmental protection and water conservation risk model is constructed. Then, the risk value of each factor is input into the environmental protection and water conservation risk model for risk simulation. The risk impact of each risk factor on the construction area can be analyzed separately, eliminating the interference of other factors and clearly presenting the specific impact degree and mechanism of each factor. Finally, the risk impact is described by data in the environmental protection and water conservation risk model to obtain the level risk value. By utilizing the function of the model, the level characteristics of the risk factors and their contribution to the overall risk are further accurately reflected.

[0036] Example 7: Based on Example 6, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation further includes: Calculate the factor risk value and level risk value corresponding to each of the aforementioned risk factors, and construct a risk comparison table for the construction area. Obtain construction information for power transmission and transformation projects, and analyze the characteristics of several construction impacts of power transmission and transformation projects on the construction area in conjunction with the aforementioned risk comparison table; The construction impact characteristics are marked in the construction area, the impact characteristics of different environmental sub-areas in the construction area are determined, and a risk visualization of the construction area is constructed and displayed.

[0037] In this example, the construction impact characteristics represent the effects of construction on the construction area.

[0038] The working principle and beneficial effects of the above technical solution are as follows: By analyzing the environmental impact of the construction process and generating a risk visualization, it further assists managers in understanding the current environmental situation, effectively reduces the amount of manual calculation and improves the accuracy of the calculation results, making it easier for managers to make corresponding environmental decisions in a short period of time.

[0039] Example 8: Based on Example 1, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation, step 5 includes: Step 51: Determine the triggered warning items in the construction area according to the risk level, set the corresponding first warning sequence for the corresponding risk factors according to the risk value of the factors, and configure the corresponding triggered warning items for each risk factor. Step 52: Sort the triggered warning items according to the first warning order to generate a first warning plan. Use the level risk value to evaluate the process risk of the initial warning plan to obtain several execution risk process points of the initial warning plan and the execution risk value corresponding to each execution risk process point. Step 53: Adjust the first warning sequence according to the execution risk value until the total execution risk value is minimized, obtain the second warning sequence corresponding to each of the triggered warning items, generate a risk warning scheme for the construction area, and issue a risk warning for the construction area according to the risk warning scheme.

[0040] In this example, the initial early warning plan represents the early warning plan currently being implemented in the construction area; In this example, the "already triggered warning project" indicates projects in the construction area that require warnings. In this example, the first warning order and the second warning order can be the same or different.

[0041] The working principle and beneficial effects of the above technical solution are as follows: Based on the risk level, the triggered warning items are identified, and the first warning sequence is set in conjunction with the factor risk value. Simultaneously, corresponding warning items are configured for each risk factor, initially determining the urgency of each triggered warning item. Then, the process risk assessment of the first warning plan is conducted using the risk level value, identifying the execution risk process points and corresponding execution risk values. This allows for in-depth analysis of the potential risks that the plan may face during execution, and the warning sequence is adjusted based on the execution risk value. The second warning sequence is determined with the goal of minimizing the total execution risk value. This ensures that the final risk warning plan not only considers the initial risk level, but also allows managers to prioritize high-risk and easily executable warning tasks, efficiently advancing risk control while minimizing the total execution risk value. Furthermore, it reduces resource waste and decision-making hesitation during execution, making warning actions more systematic and significantly improving the response speed and execution effectiveness of risk warnings.

[0042] Example 9: Based on Example 8, the method for early warning analysis of environmental and water conservation risks in power transmission and transformation projects based on multi-factor index evaluation further includes: Determine the early warning level for the construction area based on the risk level, and issue an early warning instruction; The system controls the equipment in the area corresponding to the warning level to respond to the warning command and perform the corresponding warning work.

[0043] The working principle and beneficial effects of the above technical solution are as follows: when there is a risk in the construction area, an initial warning is issued first, and then a more in-depth warning is issued based on the subsequent analysis, thereby reducing the probability of accidents.

[0044] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A power transmission and transformation project water environment protection risk early warning analysis method based on a multi-factor index evaluation, characterized in that, The method comprises the following steps: Step 1: collecting on-site data corresponding to each risk factor according to the risk evaluation requirement corresponding to each risk factor; Step 2: independently scoring each on-site data to obtain a factor risk value corresponding to each risk factor in the construction area; Step 3: performing overall risk evaluation on the factor risk value by using the exponential columbus coding method to obtain an overall risk index of the construction area; Step 4: determining the overall risk level of the construction area according to the overall risk index, and performing numerical optimization on each factor risk value to obtain a grade risk value corresponding to the risk factor; Step 5: determining a warning project corresponding to the construction area according to the risk grade, adjusting the warning order of the warning project according to the grade risk value, generating a risk warning scheme of the construction area, and performing warning.

2. The multi-factor index evaluation-based power transmission project environmental risk early warning analysis method according to claim 1, wherein, The step 1 comprises the following steps: Step 11: searching for each risk factor in the big data to obtain a plurality of pieces of related factor information corresponding to each risk factor, constructing a plurality of matching risk characteristics corresponding to the risk factor according to the related factor information, and creating a risk evaluation requirement corresponding to the risk factor according to the matching risk characteristics; Step 12: pre-monitoring the construction area to obtain an on-site environmental water and soil sample of the construction area, matching each risk evaluation requirement with the on-site environmental water and soil sample to obtain a monitoring position corresponding to each risk evaluation requirement, and generating a risk monitoring sample diagram of the construction area; Step 13: on-site monitoring the construction area according to the risk monitoring sample diagram, matching the monitoring data with the corresponding risk evaluation requirement, performing redundant cleaning on the monitoring data according to the risk evaluation requirement, counting a plurality of pieces of cleaned monitoring data corresponding to each risk factor, and generating corresponding on-site data.

3. The multi-factor index evaluation-based power transmission project ring water conservation risk early warning analysis method according to claim 2, characterized in that, The process of performing redundant cleaning on the monitoring data according to the risk evaluation requirement comprises the following steps: constructing an evaluation knowledge graph corresponding to the risk factor according to the risk evaluation requirement, identifying a plurality of key evaluation points of the risk factor in the evaluation knowledge graph, and determining point relationships between different key evaluation points; determining a matching key evaluation point corresponding to each monitoring data and an associated key evaluation point corresponding to each monitoring data according to the matching relationship between the monitoring data and the risk evaluation requirement and the point relationships between different key evaluation points; performing first data cleaning on the monitoring data by using the evaluation content corresponding to the matching key evaluation point to obtain corresponding initial cleaning data, performing data labeling on the initial cleaning data by using the corresponding associated key evaluation point, and completing data cleaning when the labeling relationship of the data labeling result is consistent with the corresponding point relationship; otherwise, regarding the same data segment between the initial cleaning data and the corresponding monitoring data as an allowed cleaning data segment, and performing second data cleaning on the allowed cleaning data segment by using the corresponding matching key evaluation point.

4. The multi-factor index evaluation-based power transmission project environmental risk early warning analysis method according to claim 1, characterized in that, The step 2 comprises the following steps: Step 21: generating corresponding numerical grading standards according to the risk division standards corresponding to each risk factor, searching for several numerical division standards corresponding to the risk factor in big data when the numerical grading standards contain fuzzy values, and using gray weight clustering analysis method to perform numerical clustering on the numerical division standards; Step 22: constructing effective numerical grading standards corresponding to the risk factor according to the clustering results, scoring the field data using the numerical grading standards, obtaining several risk factor grades of the construction area, and generating a risk evaluation report of the construction area; Step 23: deriving a grade risk value corresponding to each risk factor according to the risk evaluation report, constructing a grading threshold corresponding to each numerical grading standard to optimize the grade risk value in terms of accuracy, and generating a factor risk value corresponding to each risk factor.

5. The multi-factor index evaluation-based power transmission project environmental risk early warning analysis method according to claim 1, characterized in that, The step 3 comprises: Step 31: determining a grading standard corresponding to each risk factor according to the risk evaluation requirement corresponding to each risk factor, identifying the risk grade of each factor risk value under the corresponding risk factor, and using the exponential columbus coding method to encode each risk grade respectively to obtain the corresponding risk code; Step 32: calculating the overall risk index of the construction area using formula (1); （1） wherein, represents the overall risk index of the construction area, and the value range is [0, 7000], represents the risk code corresponding to the first risk factor, represents the risk code corresponding to the second risk factor, represents the risk code corresponding to the third risk factor, represents the risk code corresponding to the fourth risk factor, represents the risk code corresponding to the fifth risk factor, represents the risk code corresponding to the sixth risk factor, represents the risk code corresponding to the seventh risk factor, represents the risk code corresponding to the eighth risk factor, and int represents a floor function. Step 33: obtaining the calculation result of formula (1), determining the risk related information of the construction area, and constructing and displaying the corresponding risk resolution text and risk response text.

6. The multi-factor index evaluation-based power transmission project ring water conservation risk early warning analysis method according to claim 1, characterized in that, The step 4 comprises: Step 41: dividing the risk level of the construction area according to the overall risk index, determining the overall risk level of the construction area, and constructing a water conservation risk model of the construction area based on the overall risk level; Step 42: inputting each factor risk value into the water conservation risk model respectively to simulate the risk, and obtaining the risk influence of each risk factor on the construction area; Step 43: data describing each risk influence in the water conservation risk model to obtain the grade risk value corresponding to each risk factor.

7. The power transmission project ring water conservation risk early warning analysis method based on multi-factor index evaluation according to claim 6, wherein, Further comprising: Statistically determining the factor risk value and the grade risk value corresponding to each risk factor, and constructing a risk reference table of the construction area; Obtaining the power transmission and transformation engineering construction information, combining the risk reference table, and analyzing the several construction influence presentation characteristics of the power transmission and transformation engineering on the construction area; Marking the construction influence presentation characteristics in the construction area, determining the influenced presentation characteristics corresponding to different environmental protection sub-areas in the construction area, constructing a risk visualization diagram of the construction area, and displaying the risk visualization diagram.

8. The multi-factor index evaluation-based power transmission project ring water and soil conservation risk early warning analysis method according to claim 1, characterized in that, The step 5 comprises: Step 51: determining the touched early warning item of the construction area according to the risk level, setting a corresponding first early warning order for the corresponding risk factor according to the factor risk value, and configuring a corresponding touched early warning item for each risk factor respectively; Step 52: according to the first early warning order, the touched early warning items are sorted to generate a first early warning scheme, the early warning scheme is evaluated by using the level risk value to obtain several execution risk process points of the early warning scheme and the execution risk value corresponding to each execution risk process point; Step 53: according to the execution risk value, the first early warning order is adjusted until the total execution risk value is minimum, the second early warning order corresponding to each touched early warning item is obtained, the risk early warning scheme of the construction area is generated, and the construction area is early warned according to the risk early warning scheme.

9. The power transmission project ring water conservation risk early warning analysis method based on multi-factor index evaluation according to claim 8, wherein, Further comprising: determining the early warning level of the construction area according to the risk level, and issuing an early warning instruction; controlling the area equipment corresponding to the early warning level to respond to the early warning instruction to perform corresponding early warning work.

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