Power grid construction period environment water conservation monitoring method, device and equipment based on edge calculation

Abstract

The invention provides a power grid construction period environment water conservation monitoring method, device and equipment based on edge calculation, and relates to the technical field of data processing. The method comprises the steps of obtaining a key area proportion and a key area dispersion coefficient of a target construction project in power grid construction, and determining a project construction state according to the key area proportion, the key area dispersion coefficient and a project evaluation condition; determining a target analysis matching mode and a target analysis matching parameter type corresponding to the analysis point location according to the project construction state; wherein the types of the analysis matching modes comprise point location correlation matching and region clustering matching; and obtaining parameter data corresponding to the target analysis matching parameter type, determining an erosion dominant coefficient according to the parameter data, and calculating a water conservation quality coefficient of the target construction area according to the erosion dominant coefficient. According to the invention, the environment condition of the target construction project can be effectively monitored according to the multi-source monitoring data, and the monitoring timeliness of the environment and water conservation state is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and equipment for monitoring environmental and water conservation during the power grid construction period based on edge computing. Background Art

[0002] During the construction of the power grid, the monitoring of the environmental and water conservation status in the construction area has a key impact on the implementation of the project. The monitoring of the environmental and water conservation status involves multi-source data. In order to improve the efficiency of the data analysis process and ensure the timeliness of the monitoring results of the environmental and water conservation status, the existing environmental and water conservation multi-source monitoring data analysis system usually chooses edge computing to analyze the multi-source monitoring data. However, the actual power grid construction period lasts for a long time, and the monitoring data related to the environmental and water conservation status are numerous and interrelated. How to effectively screen and combine the monitoring data during the edge computing process to improve the timeliness and accuracy of the data analysis process is a problem that needs to be solved urgently by technical personnel in this field.

[0003] Chinese patent application publication number CN110895741A discloses an information management system for environmental and water conservation during the construction period of railway construction projects, including an access layer, which serves as a unified access portal and integrated front-end display, providing multiple interfaces for interacting with various users; an application layer, which is used to provide project management, construction management, monitoring data management and interface services; it includes a project basic management subsystem, a project early management subsystem, a construction process management subsystem, a monitoring data management subsystem, a special measures management subsystem and a project acceptance management subsystem; a support layer, which provides various application supports for the application layer based on various software services; the basic layer includes a data center, an existing railway business system, a positioning base station, a GIS map and a service interface. However, the above solution has the following problems: it fails to conduct effective combined analysis of the monitoring data based on the distribution of construction areas in actual construction projects, resulting in poor efficiency of the analysis process for multi-source monitoring data, and further leads to low real-time monitoring results for the environmental and water conservation status. Summary of the Invention

[0004] The embodiments of the present application provide a method, device and equipment for monitoring environmental and water conservation during the power grid construction period based on edge computing to solve the problem of low real-time monitoring results of environmental and water conservation status.

[0005] In a first aspect, an embodiment of the present application provides an edge computing-based environmental and water conservation monitoring method for a power grid construction period, comprising:

[0006] Obtaining the proportion of key areas and the dispersion coefficient of key areas of target construction projects in power grid construction, and determining the project construction status based on the proportion of key areas, the dispersion coefficient of key areas, and project assessment conditions; wherein the key areas are target construction areas whose environmental and water conservation risk coefficients are greater than a preset environmental and water conservation risk coefficient;

[0007] Determine the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status; wherein the types of analysis matching methods include point correlation matching and regional cluster matching; and the types of analysis matching parameters include: regional edge index, regional monitoring index and key dispersion coefficient;

[0008] Obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominant coefficient based on the parameter data, and calculate the environmental water conservation quality coefficient of the target construction area based on the erosion dominant coefficient.

[0009] In a possible implementation, determining the project construction status according to the proportion of the key areas, the dispersion coefficient of the key areas, and project evaluation conditions includes:

[0010] If the proportion of the key areas is greater than the preset proportion of the key areas, or the dispersion coefficient of the key areas is less than or equal to the preset dispersion coefficient of the key areas, it is determined that the project construction status of the target construction project is the first preset construction status;

[0011] If the proportion of the key areas is less than or equal to the preset proportion of the key areas, and the dispersion coefficient of the key areas is greater than the preset dispersion coefficient of the key areas, it is determined that the project construction status of the target construction project is the second preset construction status.

[0012] In a possible implementation, determining the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status includes:

[0013] When the project construction status of the target construction project is the first preset construction status, the target analysis matching mode is point-related matching, and the target analysis matching parameter types include a regional edge index and a regional monitoring index;

[0014] When the project construction status of the target construction project is in the second preset construction status, the target analysis matching method is regional cluster matching, and the target analysis matching parameter type includes a key dispersion coefficient.

[0015] In a possible implementation, when the target construction project is in a first preset construction state, obtaining parameter data corresponding to the target analysis matching parameter type and determining the erosion dominant coefficient based on the parameter data include:

[0016] Obtaining a regional edge index and a regional monitoring index, and classifying key analysis points according to the regional edge index and the regional monitoring index to obtain first-category monitoring points and second-category monitoring points; wherein, the key analysis points are analysis points whose monitoring data difference value is greater than a first preset monitoring data difference value or whose change duration is greater than a preset change duration; the monitoring data include: soil particle size and soil moisture content;

[0017] For a type of monitoring point, a matching analysis combination is generated based on the regional radiation coefficient and the duration of the relevant radiation stage;

[0018] For the second-class monitoring points, the matching analysis combination is determined based on whether there is a relevant dense area cluster within the preset relevant assessment range;

[0019] The corresponding dominant assessment method and erosion dominant coefficient are determined for each matching analysis combination.

[0020] In a possible implementation, determining the corresponding matching analysis combination according to whether a relevant dense area set exists within a preset relevant evaluation range includes:

[0021] If there is a collection of related dense areas within the preset related assessment range, the matching analysis combination is determined according to the erosion radiation quality index;

[0022] If there is no relevant dense area set within the preset relevant assessment range, the matching analysis combination is determined based on the overlap of regional change trends.

[0023] In a possible implementation, determining the corresponding dominant assessment method and erosion dominant coefficient for each matching analysis combination includes:

[0024] If the matching analysis combination of key analysis points is determined based on the overlap of regional change trends, the erosion dominance coefficient is determined based on the reference radiation coefficient of the matching analysis combination;

[0025] If the matching analysis combination of key analysis points is determined based on the erosion radiation quality index, the erosion dominance coefficient is determined based on the range radiation attenuation coefficient of the matching analysis combination.

[0026] In a possible implementation, the key analysis points are classified according to the regional edge index and the regional monitoring index to obtain the first-category monitoring points and the second-category monitoring points, including:

[0027] If the regional edge index is less than or equal to the preset regional edge index or the regional monitoring index is greater than the preset regional monitoring index, the corresponding key analysis point is a Class I monitoring point;

[0028] If the regional edge index is greater than the preset regional edge index and the regional monitoring index is less than or equal to the preset regional monitoring index, the corresponding key analysis point is a Class II monitoring point.

[0029] In a possible implementation, determining the matching analysis combination according to the erosion radiation quality index includes:

[0030] Determine the second-class monitoring points contained in the set of relevant dense areas with the largest erosion radiation quality index, and record them as a set of matching analysis combinations;

[0031] The matching analysis combination is determined based on the overlap of regional change trends, including:

[0032] Determine the second-class monitoring points whose regional change trend overlap is greater than the preset regional change trend overlap, and record them as a set of matching analysis combinations.

[0033] In a possible implementation, when the target construction project is in the second preset construction state, obtaining parameter data corresponding to the target analysis matching parameter type and determining the erosion dominant coefficient based on the parameter data include:

[0034] Obtain the key dispersion coefficient and determine whether the key analysis area is in a cluster distribution or discrete distribution state based on the key dispersion coefficient;

[0035] For the key analysis areas of cluster distribution status, the erosion dominance coefficient is determined based on the regional change similarity coefficient and the overlap of the change stages;

[0036] For critical analysis areas with discrete distribution states, the erosion dominance coefficient is determined based on the duration of the change.

[0037] In a second aspect, an embodiment of the present application provides an edge computing-based environmental and water conservation monitoring device for a power grid construction period, comprising:

[0038] A construction assessment module is used to obtain the proportion of key areas and the dispersion coefficient of key areas of target construction projects in power grid construction, and determine the project construction status based on the proportion of key areas, the dispersion coefficient of key areas, and project assessment conditions; wherein the key areas are target construction areas whose environmental and water conservation risk coefficients are greater than a preset environmental and water conservation risk coefficient;

[0039] A determination module is used to determine the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status; wherein the types of analysis matching methods include point correlation matching and regional cluster matching; and the types of analysis matching parameters include: regional edge index, regional monitoring index and key dispersion coefficient;

[0040] The calculation module is used to obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominant coefficient based on the parameter data, and calculate the environmental water quality coefficient of the target construction area based on the erosion dominant coefficient.

[0041] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method in the first aspect or any possible implementation of the first aspect is implemented.

[0042] In the embodiment of the present application, the proportion of key areas and the dispersion coefficient of key areas of the target construction project in the power grid construction are obtained, and the project construction status is determined based on the proportion of key areas, the dispersion coefficient of key areas and the project evaluation conditions. This can characterize the situation in which soil erosion is easily concentrated in the construction area of ​​the target construction project, so as to determine whether the abnormal data obtained is suitable for combined analysis, and the richness of the relevant data in the process of combined analysis, and provide a basis for determining a targeted analysis matching method, thereby ensuring the effective combination of multi-source monitoring data. Among them, the key area is the target construction area where the environmental and water conservation risk coefficient is greater than the preset environmental and water conservation risk coefficient. The target analysis matching method and target analysis matching parameter type corresponding to the analysis point are determined according to the project construction status, and the basis for the division of the targeted matching analysis combination is set, which improves the effectiveness of the division of the matching analysis combination, and thereby improves the accuracy of the erosion dominant coefficient of the key analysis points obtained subsequently. Obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominant coefficient based on the parameter data, and calculate the environmental and water conservation quality coefficient of the target construction area based on the erosion dominant coefficient, so that the process of determining the erosion dominant coefficient for key analysis points is more in line with the actual scenario, avoiding the impact of other areas with soil and water conservation anomalies on its monitoring data, thereby ensuring the timeliness and effectiveness of the determined early warning areas. This application realizes effective monitoring of the environmental conditions of the target construction project based on multi-source monitoring data, improves the efficiency of the analysis process of multi-source monitoring data, and enhances the timeliness of monitoring of environmental and water conservation status. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of an implementation method for monitoring environmental and water conservation during power grid construction period based on edge computing provided in one embodiment of the present application;

[0044] Figure 2 This is a flowchart of an implementation method for monitoring environmental and water conservation during power grid construction based on edge computing, provided in another embodiment of the present application;

[0045] Figure 3 This is a schematic diagram of the structure of an edge computing-based environmental and water conservation monitoring device for a power grid construction period provided in an embodiment of the present application;

[0046] Figure 4 Schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] This application is applied to the monitoring of environmental conservation and soil and water conservation in the construction area during the construction of the power grid, and determines whether there are any abnormal conditions by analyzing multi-source monitoring data. The target construction project is a power grid construction project that needs to be monitored for environmental conservation and soil and water conservation, and the area where there are construction behaviors that interfere with environmental conservation and soil and water conservation is recorded as the target construction area. In this application, there are several target monitoring points in each target construction area to obtain monitoring data related to environmental conservation and soil and water conservation. The categories of monitoring data in this application include but are not limited to soil particle size and soil moisture content. There are also several analysis nodes to be allocated in this application for edge computing of monitoring data.

[0048] This application does not limit the target monitoring points, the location of the analysis nodes to be assigned, and the specific equipment models. Users can set them according to the actual work scenario.

[0049] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0050] Figure 1 This is a flow chart of the implementation of the edge computing-based environmental and water conservation monitoring method for the power grid construction period provided in the embodiment of this application. Figure 1 As shown, the following steps are included:

[0051] S101, obtaining the proportion of key areas and the dispersion coefficient of key areas of target construction projects in power grid construction, and determining the project construction status based on the proportion of key areas, the dispersion coefficient of key areas and project evaluation conditions; wherein the key areas are target construction areas whose environmental and water conservation risk coefficients are greater than the preset environmental and water conservation risk coefficients.

[0052] The execution entities of each embodiment of the present application can be servers, processors, microprocessors and other devices with data processing functions. In the actual implementation process, the specific implementation method of the execution entity can be selected according to actual needs. This embodiment does not impose any special restrictions on this, as long as it is a device with data processing functions.

[0053] In this application, a cyclic regional assessment cycle is used to perform environmental and water conservation monitoring during the power grid construction period to improve the real-time monitoring and monitoring efficiency. The duration of the regional assessment cycle can be set in advance. The higher the user's requirements for the timeliness of abnormal data warnings, the shorter the duration of the regional assessment cycle. At the end of each regional assessment cycle, the project construction status and analysis matching method are determined based on the proportion of key areas and the dispersion coefficient of key areas, and the monitoring data of each target monitoring point is obtained.

[0054] In one possible implementation, the regional assessment cycle is 7 to 15 days. Optionally, the regional assessment cycle is 7 days, 10 days, or 15 days. The regional assessment cycle should not be too long, as this will result in low monitoring efficiency, nor too short, as this will cause excessive data processing pressure.

[0055] During the specific implementation process, in order to improve the monitoring efficiency, it is necessary to determine the key analysis points from several target monitoring points of the target construction project in order to conduct in-depth analysis of the key analysis points.

[0056] Specifically, for a single target monitoring point, if the monitoring data difference value of the target monitoring point is greater than the first preset monitoring data difference value or the change duration is greater than the preset change duration, the target monitoring point is determined to be a key analysis point.

[0057] The monitoring data difference value is the absolute value of the difference between the monitoring data obtained in the current regional assessment cycle and the previous regional assessment cycle, and the change duration is the product of the number of consecutive times the monitoring data difference value is greater than the second preset monitoring data difference value and the duration of the regional assessment cycle. The first preset monitoring data difference value is the average of the monitoring data difference values ​​of each key analysis point. The second preset monitoring data difference value is the average of the monitoring data difference values ​​during the change duration corresponding to each key analysis point.

[0058] In one possible implementation, the preset change duration is 45 to 60 days. Optionally, the preset change duration is 45 days, 50 days, or 60 days. The higher the user's requirements for the timeliness of abnormal data warnings, the longer the preset change duration.

[0059] During the implementation of the embodiment of the present application, there are several construction process monitoring records, and any construction process monitoring record records the difference value of monitoring data, duration of change, environmental and water conservation risk coefficient, regional edge index, erosion interference coefficient, proportion of key areas, dispersion coefficient of key areas, regional monitoring index, duration of relevant radiation phase, number of target monitoring areas contained in each relevant dense area set, overlap of regional change trend and key dispersion coefficient during at least one monitoring process of the environmental and water conservation situation of the construction process of the target construction project, and each construction process monitoring record corresponds to a qualified mark. The qualified mark records whether the timeliness of the abnormal data warning meets the user's needs. Among them, the user determines whether the timeliness of the abnormal data warning meets the needs based on the self-set indicators. For example, the self-set indicators can be the effectiveness of the early warning execution. The effectiveness of the early warning execution is the data improvement effect of the corresponding key analysis points after the user receives the early warning information to improve the water and soil in the construction area.

[0060] By dynamically obtaining the proportion and dispersion coefficient of key areas and combining it with project assessment conditions to determine the construction status, we can make a global judgment on the environmental and water conservation risks of power grid construction projects, providing a basis for subsequent data matching and analysis.

[0061] S102, determining the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status; wherein the types of analysis matching methods include point correlation matching and regional cluster matching; and the types of analysis matching parameters include: regional edge index, regional monitoring index and key dispersion coefficient.

[0062] Among them, the appropriate analysis matching method and parameter type are selected based on the construction status to ensure the pertinence and effectiveness of the data combination and improve the analysis efficiency of multi-source monitoring data.

[0063] S103, obtaining parameter data corresponding to the target analysis matching parameter type, determining the erosion dominant coefficient according to the parameter data, and calculating the environmental water conservation quality coefficient of the target construction area according to the erosion dominant coefficient.

[0064] By calculating the environmental water conservation quality coefficient through the erosion dominance coefficient, a quantitative assessment of the environmental risks in the construction area can be achieved, providing accurate data support for abnormal warnings.

[0065] In the embodiment of the present application, the proportion of key areas and the dispersion coefficient of key areas of the target construction project in the power grid construction are obtained, and the project construction status is determined based on the proportion of key areas, the dispersion coefficient of key areas and the project evaluation conditions. This can characterize the situation in which soil erosion is easily concentrated in the construction area of ​​the target construction project, so as to determine whether the abnormal data obtained is suitable for combined analysis, and the richness of the relevant data in the process of combined analysis, and provide a basis for determining a targeted analysis matching method, thereby ensuring the effective combination of multi-source monitoring data. Among them, the key area is the target construction area where the environmental and water conservation risk coefficient is greater than the preset environmental and water conservation risk coefficient. The target analysis matching method and target analysis matching parameter type corresponding to the analysis point are determined according to the project construction status, and the basis for the division of the targeted matching analysis combination is set, which improves the effectiveness of the division of the matching analysis combination, and thereby improves the accuracy of the erosion dominant coefficient of the key analysis points obtained subsequently. Obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominance coefficient based on the parameter data, and calculate the environmental water conservation quality coefficient of the target construction area based on the erosion dominance coefficient. This makes the determination process of the erosion dominance coefficient for key analysis points more consistent with the actual scenario, avoids the impact of other areas with soil and water conservation anomalies on its monitoring data, and thus ensures the timeliness and effectiveness of the determined early warning areas. This application realizes effective monitoring of the environmental conditions of the target construction project based on multi-source monitoring data.

[0066] By dynamically analyzing the project construction status and matching the corresponding parameter types, targeted screening and combination of multi-source monitoring data are achieved, effectively improving the processing efficiency of environmental and water conservation monitoring data, ensuring the real-time and accuracy of monitoring results, and solving the problem that traditional systems cannot flexibly adapt to changes in the distribution of construction areas.

[0067] In one possible implementation, the project construction status is determined based on the proportion of key areas, the key area dispersion coefficient, and project evaluation conditions, including:

[0068] If the proportion of key areas is greater than the preset proportion of key areas, or the dispersion coefficient of key areas is less than or equal to the preset dispersion coefficient of key areas, the project construction status of the target construction project is determined to be the first preset construction status;

[0069] If the proportion of key areas is less than or equal to the preset proportion of key areas, and the dispersion coefficient of key areas is greater than the preset dispersion coefficient of key areas, the project construction status of the target construction project is determined to be the second preset construction status.

[0070] In this application, the target construction area of ​​the target construction project is obtained, and the environmental and water conservation risk coefficient of each target construction area is periodically detected, and the target construction area whose environmental and water conservation risk coefficient is greater than the preset environmental and water conservation risk coefficient is recorded as a key construction area (i.e., a key area). Among them, the key area ratio = the number of key construction areas of the target construction project / the number of target construction areas of the target construction project. The key area dispersion coefficient is determined based on the number of edge construction areas of the target construction project and the reference edge index. The key area dispersion coefficient is the product of the number of edge construction areas of the target construction project and the reference edge index. The edge construction area is a target construction area whose regional edge index is greater than the preset regional edge index, and the reference edge index is the average value of the regional edge index of each target construction area.

[0071] For a single target construction area, the environmental and water conservation risk coefficient is determined based on the vegetation coverage percentage and average soil particle size within the target construction area. The environmental and water conservation risk coefficient = ln(vegetation coverage percentage / average soil particle size), where vegetation coverage percentage = the sum of the areas occupied by all vegetation-covered construction sub-areas within the target construction area / the area occupied by the target construction area. The target construction area is divided into several equal-sized construction sub-areas. If a construction sub-area contains forest vegetation, shrub vegetation, or herbaceous plants, it is considered to have vegetation coverage. The regional edge index = 1 / the number of target construction areas with erosion interference relationships with the target construction area.

[0072] Users can set the preset environmental and water conservation risk coefficient and the preset regional edge index based on their actual work scenarios. For example, users can set them based on construction process monitoring records. The higher the user's requirements for the timeliness of abnormal data warnings, the smaller the preset environmental and water conservation risk coefficient value.

[0073] Optionally, the preset environmental and water conservation risk coefficient is the minimum value of the environmental and water conservation risk coefficient of each key construction area in the construction process monitoring record that meets the user's requirements for timeliness of abnormal data warning.

[0074] Optionally, the preset regional edge index is the minimum value of the regional edge index of each edge construction area in the construction process monitoring record that meets the user's requirement for timeliness of abnormal data warning.

[0075] For any two target construction areas, if the erosion interference coefficient between the two target construction areas is greater than the preset erosion interference coefficient, it is determined that there is an erosion interference relationship between the two target construction areas. The erosion interference coefficient is the sum of the products of the runoff overlap and the wind direction coverage with the corresponding interference weight coefficients. Runoff overlap = the volume of precipitation in one target construction area that is concentrated in another target construction area / the precipitation in the target construction area. The wind direction coverage is the sum of the products of the wind direction inclination deviation of each wind direction obtained during the current regional evaluation period and the corresponding influence coefficient. For a single wind direction obtained, the wind direction inclination deviation is the angle formed by the wind direction and the line connecting the center points of the two target construction areas. The influence coefficient is positively correlated with the number of times the wind direction is obtained during the current regional evaluation period.

[0076] The interference weight coefficient corresponding to runoff overlap is positively correlated with the precipitation in the target construction area during the current regional assessment cycle. The interference weight coefficient corresponding to wind direction coverage is positively correlated with the reference wind intensity in the target construction area during the current regional assessment cycle. The reference wind intensity is the average of the wind intensities obtained during the current regional assessment cycle. In this application, wind conditions in the target construction area are tested within any regional assessment cycle to complete the acquisition of wind direction and wind intensity several times.

[0077] The preset erosion interference coefficient value can be determined by the user based on the actual work scenario. For example, the user can set it based on the construction process monitoring records. The higher the user's requirement for timely abnormal data warning, the smaller the preset erosion interference coefficient value. Optionally, the preset erosion interference coefficient is the average of the erosion interference coefficients between two target construction areas determined to have an erosion interference relationship in the construction process monitoring records that meet the user's requirement for timely abnormal data warning.

[0078] The user can determine the value of the preset key area ratio and the preset key area dispersion coefficient according to the actual work scenario. Optionally, the preset key area ratio is the minimum value of the key area ratio of the target construction area in the first preset construction state in the construction process monitoring record that meets the user's requirements for the timeliness of abnormal data warning. Optionally, the value of the preset key area ratio is 0.35 to 0.5. Preferably, the value of the preset key area ratio is 0.35.

[0079] Optionally, the value of the preset key area dispersion coefficient is the maximum value of the key area dispersion coefficient of the target construction area in the first preset construction state in the construction process monitoring record that meets the user's requirements for timeliness of abnormal data warning.

[0080] In this embodiment, based on the two-dimensional evaluation of the proportion of key areas and the dispersion coefficient, the classification standards for different construction statuses are clarified, subjective judgment errors are avoided, the concentration degree of high-risk areas for soil and water loss is accurately identified, and a basis is provided for differentiated monitoring strategies, avoiding the waste of monitoring resources due to the distribution characteristics of the construction area and enhancing the environmental adaptability of the monitoring system.

[0081] In one possible implementation, the target analysis matching method and target analysis matching parameter type corresponding to the analysis point are determined according to the project construction status, including:

[0082] When the project construction status of the target construction project is the first preset construction status, the target analysis matching method is point-related matching, and the target analysis matching parameter types include regional edge index and regional monitoring index;

[0083] When the project construction status of the target construction project is in the second preset construction status, the target analysis matching method is regional cluster matching, and the target analysis matching parameter type includes a key dispersion coefficient.

[0084] In this embodiment, through the state-driven analysis mode switching mechanism, the optimal analysis mode is matched to the monitoring requirements of different construction states (point correlation or regional clustering), ensuring the efficiency and pertinence of monitoring data processing, and significantly improving the environmental and water conservation monitoring efficiency in multiple scenarios.

[0085] In one possible implementation, when the target construction project is in a first preset construction state, obtaining parameter data corresponding to the target analysis matching parameter type, and determining the erosion dominant coefficient based on the parameter data include:

[0086] Obtaining a regional edge index and a regional monitoring index, and classifying key analysis points according to the regional edge index and the regional monitoring index to obtain first-class monitoring points and second-class monitoring points; wherein, the key analysis points are analysis points whose monitoring data difference value is greater than a first preset monitoring data difference value or whose change duration is greater than a preset change duration; the monitoring data include: soil particle size and soil moisture content;

[0087] For a type of monitoring point, a matching analysis combination is generated based on the regional radiation coefficient and the duration of the relevant radiation stage;

[0088] For the second-class monitoring points, the matching analysis combination is determined based on whether there is a relevant dense area cluster within the preset relevant assessment range;

[0089] The corresponding dominant assessment method and erosion dominant coefficient are determined for each matching analysis combination.

[0090] In one possible implementation, key analysis points are classified according to the regional edge index and the regional monitoring index to obtain first-class monitoring points and second-class monitoring points, including:

[0091] If the regional edge index is less than or equal to the preset regional edge index or the regional monitoring index is greater than the preset regional monitoring index, the corresponding key analysis point is a Class I monitoring point;

[0092] If the regional edge index is greater than the preset regional edge index and the regional monitoring index is less than or equal to the preset regional monitoring index, the corresponding key analysis point is a Class II monitoring point.

[0093] In a possible implementation, determining a matching analysis combination according to the erosion radiation quality index includes:

[0094] Determine the second-class monitoring points contained in the set of relevant dense areas with the largest erosion radiation quality index, and record them as a set of matching analysis combinations;

[0095] The matching analysis combination is determined based on the overlap of regional change trends, including:

[0096] Determine the second-class monitoring points whose regional change trend overlap is greater than the preset regional change trend overlap, and record them as a set of matching analysis combinations.

[0097] Among them, for a single key analysis point, the regional monitoring index is the sum of the number of key analysis points in each target construction area that has an erosion interference relationship with the target construction area where the key analysis point is located.

[0098] If the regional edge index of the target construction area where the key analysis point is located is less than or equal to the preset regional edge index, or the regional monitoring index of the key analysis point is greater than the preset regional monitoring index, then the key analysis point is determined to be in a Class I point monitoring state; if the regional edge index of the target construction area where the key analysis point is located is greater than the preset regional edge index and the regional monitoring index is less than or equal to the preset regional monitoring index, then the key analysis point is determined to be in a Class II point monitoring state. For any key analysis point, only one matching analysis combination is determined.

[0099] The preset regional monitoring index can be set based on the construction process monitoring records. The higher the user's requirements for the timeliness of abnormal data warnings, the larger the value of the preset regional monitoring index. Optionally, the preset regional edge index is the maximum value of the regional edge index of each type of monitoring point in the construction process monitoring records that meets the user's requirements for the timeliness of abnormal data warnings; the preset regional monitoring index is the average value of the regional monitoring index of each type of monitoring point in the construction process monitoring records that meets the user's requirements for the timeliness of abnormal data warnings.

[0100] Specifically, for a type of monitoring point, a matching analysis combination is generated based on the regional radiation coefficient and the relevant radiation stage duration. Among them, for a single type of monitoring point, when determining the matching analysis combination of the type of monitoring point based on the regional radiation coefficient and the relevant radiation stage duration, the regional radiation coefficient and the relevant radiation stage duration of any target monitoring point contained in the determined matching data combination are both within the corresponding matching coefficient range. For a single target monitoring point, the regional radiation coefficient is the erosion radiation coefficient between the target construction area where the target monitoring point is located and the target construction area where the type of monitoring point is located. The relevant radiation stage duration is the overlapping time of the data change stage of the target monitoring point and the data change stage of the type of monitoring point. Since there are many interference factors in the type of monitoring point, the data of the combined analysis is further screened by the regional radiation coefficient and the relevant radiation stage duration to ensure the validity of the data combination.

[0101] If the regional radiation coefficient of a target monitoring point is greater than the preset regional radiation coefficient, it is determined that the regional radiation coefficient exists in the corresponding matching coefficient range. If the relevant radiation stage duration of a target monitoring point is greater than the preset relevant radiation stage duration, it is determined that the relevant radiation stage duration exists in the corresponding matching coefficient range. Among them, the values ​​of the preset regional radiation coefficient and the preset relevant radiation stage duration can be set according to the construction process monitoring record. The higher the user's requirements for the timeliness of abnormal data warning, the larger the value of the preset regional radiation coefficient and the larger the value of the preset relevant radiation stage duration. Optionally, the preset regional radiation coefficient is the average value of the erosion interference coefficient of the target construction area where a type of monitoring point is located and the matching analysis combination is divided; the value method of the preset relevant radiation stage duration is the average value of the relevant radiation stage duration of each target monitoring point in the matching analysis combination determined according to the regional radiation coefficient and the relevant radiation stage duration.

[0102] In this embodiment, the dual-parameter classification method of regional edge index and monitoring index is used to achieve refined classification of key points. Combined with the combination strategy of radiation coefficient and stage duration, the interference data is effectively filtered, the accuracy of correlation analysis of abnormal data is improved, and the reliability of erosion dominant coefficient calculation is enhanced.

[0103] In a possible implementation, according to whether there is a relevant dense area set within a preset relevant evaluation range, the corresponding matching analysis combination is determined to include:

[0104] If there is a collection of related dense areas within the preset related assessment range, the matching analysis combination is determined according to the erosion radiation quality index;

[0105] If there is no relevant dense area set within the preset relevant assessment range, the matching analysis combination is determined based on the overlap of regional change trends.

[0106] For a single Class II monitoring point, the preset assessment range is a circular area centered at the center of the target construction area where the Class II monitoring point is located, with the erosion reference length as its radius. The erosion reference length is determined based on the duration of the data change phase for the Class II monitoring point and the data change reference value. The erosion reference length is positively correlated with the data change coefficient: data change coefficient = data change phase duration × data change reference value. The center of the target construction area is the center of the target construction area.

[0107] For a single Class II monitoring point, a related dense area set is a collection of several target monitoring areas. For a single related dense area set, any key analysis area contains at least one related dense area within the related dense area set, and the number of target monitoring areas included in the related dense area set is greater than the preset number of related sets. For any two key analysis areas, if there is an erosion interference relationship between the two key analysis areas and the distance between the center points of the two key analysis areas is less than the preset dense area distance, the two key analysis areas are considered to be related dense areas. The preset number of related sets and the preset dense area distance can be set based on the construction process monitoring records. The higher the user's requirements for the timeliness of abnormal data warnings, the larger the preset number of related sets and the smaller the preset dense area distance. Optionally, the preset number of related sets is the average number of target monitoring areas included in each related dense area set; the preset dense area distance is the maximum distance between the center point of each key analysis area and the center point of the related dense area within the related dense area.

[0108] For a single Class II monitoring point, if there is a related dense area set within the preset related assessment range of the Class II monitoring point, a matching analysis combination is determined based on the erosion radiation quality index of each related dense area set. For a single related dense area set, the erosion radiation quality index is determined based on the set reference erosion coefficient and the periodic interference tendency overlap. The set of key analysis points included in the related dense area set with the largest erosion radiation quality index is recorded as the matching analysis combination of the Class II monitoring point. The erosion radiation quality index = ln (set reference erosion coefficient × periodic interference tendency overlap), the set reference erosion coefficient is the average erosion interference coefficient between each key analysis area in the related dense area set and the Class II monitoring point, the periodic interference tendency overlap = 1 / the average angle formed by each interference tendency in the related dense area set and the dominant wind direction in the current regional assessment period. For any key analysis area in the related dense area set, the interference tendency is the line connecting the key analysis area and the center point of the key analysis area where the Class II monitoring point is located, and the dominant wind direction is the wind direction with the most wind direction and wind intensity obtained in the current regional assessment period.

[0109] If a single Class II monitoring point does not have a cluster of related densely populated areas within the preset relevant assessment range, a matching analysis combination is determined based on the overlap of regional change trends, and the degree of data change at the Class II monitoring point at each cycle during the trend assessment phase is tested. The duration of the trend assessment phase is user-determined; the more timely the user's abnormal data warnings, the shorter the trend assessment phase. Optionally, the trend assessment phase is 60 days.

[0110] For any cycle-alternation moment during the trend assessment phase, and for any two adjacent regional assessment cycles, the end moment of the initial regional assessment cycle and the start moment of the changed regional assessment cycle are recorded as a cycle-alternation moment. The cycle data change ratio corresponding to each cycle-alternation moment during the trend assessment phase is tested for the two types of monitoring points and each key analysis point. The cycle data change ratio = the absolute value of the difference between the monitoring data obtained from the two regional assessment cycles corresponding to the cycle-alternation moment / the monitoring data obtained from the initial regional assessment cycle corresponding to the cycle-alternation moment. At a single cycle-alternation moment, if the cycle data change ratio between a key analysis point and the two types of monitoring points is less than the preset data change ratio difference, a change trend overlap is recorded for that key analysis point. For a single key analysis point, the regional change trend overlap degree = the number of change trend overlaps corresponding to that key analysis point / the number of cycle-alternation moments during the trend assessment phase. The set of key analysis points whose regional change trend overlap degree exceeds the preset regional change trend overlap degree is recorded as a matching analysis combination.

[0111] The value of the preset regional change trend coincidence can be set based on the construction process monitoring records. The higher the user's requirement for the timeliness of abnormal data warning, the larger the value of the preset regional change trend coincidence. The construction process monitoring records of the matching analysis combination determined based on the regional change trend coincidence are recorded as trend reference records. Optionally, the preset regional change trend coincidence is the minimum value of the regional change trend coincidence of each key analysis point in the matching analysis combination in the trend reference record that meets the timeliness requirement of the abnormal data warning.

[0112] In this embodiment, the intelligent selection of two types of monitoring point matching strategies is achieved by judging the existence of dense area sets: the erosion radiation quality index method strengthens the quantitative evaluation of regional interference impact, while the trend overlap rule improves the ability to capture abnormal trends of isolated points, significantly enhancing the system's anti-interference ability.

[0113] In one possible implementation, determining a corresponding dominant assessment method and erosion dominant coefficient for each matching analysis combination includes:

[0114] If the matching analysis combination of key analysis points is determined based on the overlap of regional change trends, the erosion dominance coefficient is determined based on the reference radiation coefficient of the matching analysis combination;

[0115] If the matching analysis combination of key analysis points is determined based on the erosion radiation quality index, the erosion dominance coefficient is determined based on the range radiation attenuation coefficient of the matching analysis combination.

[0116] Among them, for a single key analysis point, the erosion dominant coefficient is determined according to the duration of the change of the key analysis point. The erosion dominant coefficient is positively correlated with the duration of the change. The erosion dominant coefficient is reduced and adjusted according to the compensation index. The reduction value of the erosion dominant coefficient is positively correlated with the compensation index.

[0117] If a matching analysis combination is determined based on the regional radiation coefficient and the duration of the relevant radiation phase, or the degree of overlap of regional change trends, the compensation index is positively correlated with the reference radiation coefficient. The reference radiation coefficient is the average of the erosion interference coefficients between each key analysis area corresponding to the matching analysis combination and the key analysis area where the key analysis point is located. If a matching analysis combination is determined based on the erosion radiation quality index, the compensation index is negatively correlated with the range radiation attenuation coefficient. The range attenuation coefficient is the sum of the product of the distance between the center point of each key analysis area in the matching analysis combination and the center point of the key analysis point and the corresponding weight coefficient. The weight coefficient of each key analysis area is positively correlated with the erosion interference coefficient.

[0118] In this embodiment, the evaluation parameters are dynamically adjusted according to the generation method of the matching combination. Through the differentiated application of the radiation coefficient and the attenuation coefficient, the analysis deviation under different data combination modes is effectively eliminated, ensuring the objectivity and accuracy of the calculation of the erosion dominant coefficient.

[0119] In this embodiment, based on the maximum erosion radiation quality index and the trend coincidence threshold, the extreme value screening method and the threshold judgment method are used to achieve accurate extraction of abnormal data in dense areas and isolated points, respectively, ensuring the representativeness and effectiveness of the matching combination and improving the system's early warning response speed.

[0120] In one possible implementation, when the target construction project is in the second preset construction state, obtaining parameter data corresponding to the target analysis matching parameter type, and determining the erosion dominant coefficient based on the parameter data include:

[0121] Obtain the key dispersion coefficient and determine whether the key analysis area is in a cluster distribution or discrete distribution state based on the key dispersion coefficient;

[0122] For the key analysis areas of cluster distribution status, the erosion dominance coefficient is determined based on the regional change similarity coefficient and the overlap of the change stages;

[0123] For critical analysis areas with discrete distribution states, the erosion dominance coefficient is determined based on the duration of the change.

[0124] For a single key analysis area, the key dispersion coefficient = key proportion / environmental and water conservation risk coefficient of the key analysis area, and the key proportion = the number of key analysis areas with erosion interference relationships with the key analysis area / the number of target construction areas with erosion interference relationships with the key analysis area. If the key dispersion coefficient is greater than the preset key dispersion coefficient, the set of key analysis areas with erosion interference relationships with the key analysis area is recorded as the cluster analysis set. The preset key dispersion coefficient value can be determined based on the actual work scenario. For example, the user can set it based on the construction process monitoring records. A method for determining the value of a preset key analysis coefficient is provided, and the average value of the key dispersion coefficients of the key analysis areas in the clustered distribution state in the construction process monitoring records that meet the user's requirements for the timeliness of abnormal data warning is recorded as the preset key analysis coefficient. The distribution state is determined according to the key dispersion coefficients of each key analysis area, and the method for determining the erosion dominant coefficient of the key analysis point corresponding to the key analysis area is determined according to the distribution state. By combining the environmental and water conservation risk coefficient of the key analysis area itself and the interference it suffers, if the key dispersion coefficient is small, it indicates that the risk itself is small and the interference of the abnormal data of the corresponding key analysis point is large. Therefore, further analysis is performed on the key analysis points corresponding to such areas, and the erosion dominant coefficient of the key analysis point is determined according to the regional change similarity coefficient and the overlap of the change stage.

[0125] Among them, for a single key analysis point, the erosion dominant coefficient is determined according to the duration of the change of the key analysis point. The erosion dominant coefficient is positively correlated with the duration of the change. If the key analysis point is in a clustered distribution state, the clustering adjustment parameter is determined according to the regional change similarity coefficient and the overlap of the change stage. The clustering adjustment parameter is the sum of the regional change similarity coefficient and the overlap of the change stage. The erosion dominant coefficient of the key analysis point is reduced according to the clustering adjustment parameter. The reduction value of the erosion dominant coefficient is positively correlated with the clustering adjustment parameter. If the key analysis point is in a discrete distribution state, the erosion dominant coefficient of the target construction area where the key analysis point is located is not adjusted.

[0126] The key analysis points included in the cluster analysis set of the key analysis area corresponding to the key analysis point are obtained and recorded as the matching analysis points of the key analysis point. The data change ratio difference value between each matching analysis point and the key analysis point is detected, and the key analysis points whose data change ratio difference value is less than the preset data change ratio difference value are recorded as change similarity points. The regional change similarity coefficient is the number of change similarity points. For a single matching analysis point, the data change ratio difference value is the absolute value of the difference between the data change ratio of the matching analysis point and the key analysis point. Data change ratio = the absolute value of the difference between the monitoring data obtained in the current regional evaluation cycle and the previous regional evaluation cycle / the monitoring data obtained in the previous regional evaluation cycle. The value of the preset data change ratio difference value can be determined according to the actual work scenario. For example, the user can set it according to the construction process monitoring record. The higher the user's requirements for the timeliness of abnormal data warning, the larger the value of the preset data change ratio difference value. Optionally, the preset data change ratio difference value is 0.05; the change stage overlap = 1 / the average of the absolute values ​​of the differences between the change durations of each similar change point and the key analysis point.

[0127] Specifically, the edge analysis module responds to the quality analysis conditions and determines the environmental and water conservation quality coefficient of each target construction area based on the erosion dominance coefficient of each key analysis point;

[0128] For a single target construction area with key analysis points, the environmental water conservation quality coefficient is positively correlated with the reference erosion dominant coefficient of the target construction area. The reference erosion dominant coefficient is the average of the erosion dominant coefficients of each key analysis point in the target construction area.

[0129] The quality analysis condition is that the erosion dominant coefficients of each key analysis point are determined.

[0130] Among them, the target construction area whose environmental and water conservation quality coefficient is greater than the preset environmental and water conservation quality coefficient is recorded as a warning area. If a warning area exists, a warning message is sent to the user, prompting that there is an abnormality in environmental conservation or soil and water conservation in the warning area. The value of the preset environmental and water conservation quality coefficient can be determined by the user according to the actual work scenario, for example, it can be set according to the monitoring records of the construction process. The higher the user's requirements for the timeliness of abnormal data warning, the smaller the value of the preset environmental and water conservation quality coefficient. Optionally, the value of the preset environmental and water conservation quality coefficient is the minimum value of the environmental and water conservation quality coefficients of each warning area.

[0131] Specifically, the edge analysis module determines the allocation index of each analysis node to be allocated based on the point association parameters and node processing parameters, and determines the target analysis node of each key analysis point according to the allocation index;

[0132] The distribution index is positively correlated with the point association parameter;

[0133] The allocation index is negatively correlated with the node processing parameters.

[0134] Among them, for a single key analysis point, the allocation index of each analysis node to be allocated is determined according to the point association parameter and the node processing parameter, and the analysis node to be allocated with the largest allocation index is set as the target analysis node of the key analysis point to determine the erosion dominant coefficient of the key analysis point. For a single analysis node to be allocated, the point association parameter = the number of key analysis points associated with the analysis node to be allocated / the number of target monitoring points associated with the analysis node to be allocated, the node processing parameter is the number of key analysis points that need to perform erosion dominant coefficient analysis on the analysis node to be allocated within the current regional assessment period, and the allocation index = ln (point association parameter / node processing parameter).

[0135] In this embodiment, differentiated processing of regional distribution characteristics is achieved through clustering / discrete state identification of key dispersion coefficients: the dual-parameter adjustment mechanism in the clustering state effectively suppresses the influence of inter-regional interference, and the single-parameter simplified analysis in the discrete state improves data processing efficiency and balances monitoring accuracy and real-time performance.

[0136] The present application provides an embodiment. In embodiment 1, the key area ratio of the target construction project is 0.43, and the key area dispersion coefficient is 5. At this time, the preset key area ratio is 0.35, and the preset key area dispersion coefficient is 3. The target construction project is determined to be in the first preset construction state. The monitoring data of the target construction project is analyzed and matched based on point-related matching. For one of the key analysis points, if the regional edge index of the key analysis point is 0.25 and the regional monitoring index is 8, at this time, the preset regional edge index is 0.5, and the preset regional monitoring index is 6. The key analysis point is in a type I point monitoring state. A matching analysis combination of the key analysis point is determined based on the regional radiation coefficient and the duration of the relevant radiation stage. At this time, the erosion dominant coefficient is determined based on the duration of the change of the key analysis point. The erosion dominant coefficient is positively correlated with the duration of the change. The erosion dominant coefficient is reduced and adjusted based on the compensation index. The compensation index is positively correlated with the reference radiation coefficient of the matching analysis combination of the key analysis point.

[0137] See also Figure 2 , which shows a flowchart of an implementation method of an environmental and water conservation monitoring method during the power grid construction period based on edge computing provided by another embodiment of the present application, as detailed below:

[0138] Obtain the proportion of key areas and the dispersion coefficient of key areas;

[0139] Determine the project construction status based on the proportion of key areas and the dispersion coefficient of key areas;

[0140] When in the first preset state, the target analysis and matching mode is determined to be point correlation matching; when in the second preset state, the target analysis and matching mode is determined to be regional cluster matching;

[0141] After determining the target analysis matching method, in the first preset state, obtain the regional edge index and regional monitoring index, and determine the classification of key analysis points. For the first type of monitoring points, generate a matching combination: regional radiation coefficient + related radiation duration. For the second type of monitoring points, further determine whether there is a related dense area set within the preset relevant assessment range. When there is a related dense area set, determine the matching analysis combination based on the erosion radiation quality index, and determine the erosion dominant coefficient based on the range radiation attenuation coefficient; when there is no related dense area set, determine the matching analysis combination based on the regional change trend overlap, and determine the erosion dominant coefficient based on the reference radiation coefficient;

[0142] After determining the target analysis matching method, in the second preset state, the key dispersion coefficient is obtained, and the key analysis area is judged to be clustered or discretely distributed based on the key dispersion coefficient; for the key analysis area in the cluster distribution state, the erosion dominance coefficient is determined based on the regional change similarity coefficient and the overlap of the change stages; for the key analysis area in the discrete distribution state, the erosion dominance coefficient is determined based on the duration of the change;

[0143] Finally, the environmental water conservation quality coefficient of the target construction area is calculated and output based on the erosion dominance coefficient.

[0144] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0145] The following are device embodiments of the present application. For details not fully described therein, please refer to the corresponding method embodiments described above.

[0146] Figure 3 The following is a schematic diagram of the structure of the edge computing-based environmental and water conservation monitoring device for the power grid construction period provided by the embodiment of the present application. For the sake of convenience, only the parts related to the embodiment of the present application are shown, which are detailed as follows:

[0147] like Figure 3 As shown in the figure, the edge computing-based environmental and water conservation monitoring device during the power grid construction period includes:

[0148] The construction assessment module 301 is used to obtain the proportion of key areas and the dispersion coefficient of key areas of target construction projects in power grid construction, and determine the project construction status based on the proportion of key areas, the dispersion coefficient of key areas, and project assessment conditions; wherein the key areas are target construction areas whose environmental and water conservation risk coefficients are greater than a preset environmental and water conservation risk coefficient;

[0149] Determination module 302 is used to determine the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status; wherein the types of analysis matching methods include point correlation matching and regional cluster matching; and the types of analysis matching parameters include: regional edge index, regional monitoring index and key dispersion coefficient;

[0150] The calculation module 303 is used to obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominant coefficient based on the parameter data, and calculate the environmental water conservation quality coefficient of the target construction area based on the erosion dominant coefficient.

[0151] In a possible implementation, the construction assessment module 301 is specifically configured to:

[0152] If the proportion of the key areas is greater than the preset proportion of the key areas, or the dispersion coefficient of the key areas is less than or equal to the preset dispersion coefficient of the key areas, it is determined that the project construction status of the target construction project is the first preset construction status;

[0153] If the proportion of the key areas is less than or equal to the preset proportion of the key areas, and the dispersion coefficient of the key areas is greater than the preset dispersion coefficient of the key areas, it is determined that the project construction status of the target construction project is the second preset construction status.

[0154] In a possible implementation, the determining module 302 is specifically configured to:

[0155] When the project construction status of the target construction project is the first preset construction status, the target analysis matching mode is point-related matching, and the target analysis matching parameter types include a regional edge index and a regional monitoring index;

[0156] When the project construction status of the target construction project is in the second preset construction status, the target analysis matching method is regional cluster matching, and the target analysis matching parameter type includes a key dispersion coefficient.

[0157] In a possible implementation, the calculation module 303 is specifically configured to:

[0158] Obtaining a regional edge index and a regional monitoring index, and classifying key analysis points according to the regional edge index and the regional monitoring index to obtain first-category monitoring points and second-category monitoring points; wherein, the key analysis points are analysis points whose monitoring data difference value is greater than a first preset monitoring data difference value or whose change duration is greater than a preset change duration; the monitoring data include: soil particle size and soil moisture content;

[0159] For a type of monitoring point, a matching analysis combination is generated based on the regional radiation coefficient and the duration of the relevant radiation stage;

[0160] For the second-class monitoring points, the matching analysis combination is determined based on whether there is a relevant dense area cluster within the preset relevant assessment range;

[0161] The corresponding dominant assessment method and erosion dominant coefficient are determined for each matching analysis combination.

[0162] In a possible implementation, the calculation module 303 is specifically configured to:

[0163] If there is a collection of related dense areas within the preset related assessment range, the matching analysis combination is determined according to the erosion radiation quality index;

[0164] If there is no relevant dense area set within the preset relevant assessment range, the matching analysis combination is determined based on the overlap of regional change trends.

[0165] In a possible implementation, the calculation module 303 is specifically configured to:

[0166] If the matching analysis combination of key analysis points is determined based on the overlap of regional change trends, the erosion dominance coefficient is determined based on the reference radiation coefficient of the matching analysis combination;

[0167] If the matching analysis combination of key analysis points is determined based on the erosion radiation quality index, the erosion dominance coefficient is determined based on the range radiation attenuation coefficient of the matching analysis combination.

[0168] In a possible implementation, the determining module 302 is specifically configured to:

[0169] If the regional edge index is less than or equal to the preset regional edge index or the regional monitoring index is greater than the preset regional monitoring index, the corresponding key analysis point is a Class I monitoring point;

[0170] If the regional edge index is greater than the preset regional edge index and the regional monitoring index is less than or equal to the preset regional monitoring index, the corresponding key analysis point is a Class II monitoring point.

[0171] In a possible implementation, the determining module 302 is specifically configured to:

[0172] Determine the second-class monitoring points contained in the set of relevant dense areas with the largest erosion radiation quality index, and record them as a set of matching analysis combinations;

[0173] The matching analysis combination is determined based on the overlap of regional change trends, including:

[0174] Determine the second-class monitoring points whose regional change trend overlap is greater than the preset regional change trend overlap, and record them as a set of matching analysis combinations.

[0175] In a possible implementation, the calculation module 303 is specifically configured to:

[0176] Obtain the key dispersion coefficient and determine whether the key analysis area is in a cluster distribution or discrete distribution state based on the key dispersion coefficient;

[0177] For the key analysis areas of cluster distribution status, the erosion dominance coefficient is determined based on the regional change similarity coefficient and the overlap of the change stages;

[0178] For critical analysis areas with discrete distribution states, the erosion dominance coefficient is determined based on the duration of the change.

[0179] In this embodiment, the proportion of key areas and the dispersion coefficient of key areas of the target construction project in the power grid construction are obtained, and the project construction status is determined based on the proportion of key areas, the dispersion coefficient of key areas and the project evaluation conditions. This can characterize the situation in which soil erosion is prone to occur in the construction area of ​​the target construction project, so as to determine whether the abnormal data obtained is suitable for combined analysis, and the richness of the relevant data in the process of combined analysis, provide a basis for determining a targeted analysis matching method, and ensure the effective combination of multi-source monitoring data. Among them, the key area is the target construction area where the environmental and water conservation risk coefficient is greater than the preset environmental and water conservation risk coefficient. The target analysis matching method and target analysis matching parameter type corresponding to the analysis point are determined according to the project construction status, and the basis for the division of the targeted matching analysis combination is set, which improves the effectiveness of the division of the matching analysis combination, and thereby improves the accuracy of the erosion dominance coefficient of the key analysis points obtained subsequently. Obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominant coefficient based on the parameter data, and calculate the environmental and water conservation quality coefficient of the target construction area based on the erosion dominant coefficient, so that the process of determining the erosion dominant coefficient for key analysis points is more in line with the actual scenario, avoiding the impact of other areas with soil and water conservation anomalies on its monitoring data, thereby ensuring the timeliness and effectiveness of the determined early warning areas. This application realizes effective monitoring of the environmental conditions of the target construction project based on multi-source monitoring data, improves the efficiency of the analysis process of multi-source monitoring data, and enhances the timeliness of monitoring of environmental and water conservation status.

[0180] Figure 4 Schematic diagram of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the electronic device 4 of this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, the steps of the above-described method embodiments are implemented. Alternatively, when the processor 40 executes the computer program 42, the functions of the modules / units in the above-described device embodiments are implemented.

[0181] For example, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the electronic device 4.

[0182] The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation on the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0183] For the sake of convenience and brevity, the division of the above functional modules / units is only used as an example. In actual applications, the above functions can be assigned to different functional modules / units as needed. The above modules / units can be implemented in the form of hardware, software, or a combination of hardware and software.

[0184] In the above embodiments, the descriptions of each embodiment have their own focus. For parts not described or recorded in detail in one embodiment, please refer to the relevant descriptions of other embodiments. Unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features of different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0185] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for monitoring environmental and water conservation during power grid construction period based on edge computing, characterized in that: include: Obtaining the proportion of key areas and the dispersion coefficient of key areas of target construction projects in power grid construction, and determining the project construction status based on the proportion of key areas, the dispersion coefficient of key areas, and project assessment conditions; wherein the key areas are target construction areas whose environmental and water conservation risk coefficients are greater than a preset environmental and water conservation risk coefficient; Determine the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status; wherein the types of analysis matching methods include point correlation matching and regional cluster matching; and the types of analysis matching parameters include: regional edge index, regional monitoring index and key dispersion coefficient; Obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominant coefficient based on the parameter data, and calculate the environmental water conservation quality coefficient of the target construction area based on the erosion dominant coefficient.

2. The edge computing-based environmental and water conservation monitoring method for power grid construction period according to claim 1 is characterized in that: Determining the project construction status according to the proportion of the key areas, the dispersion coefficient of the key areas and the project evaluation conditions includes: If the proportion of the key areas is greater than the preset proportion of the key areas, or the dispersion coefficient of the key areas is less than or equal to the preset dispersion coefficient of the key areas, it is determined that the project construction status of the target construction project is the first preset construction status; If the proportion of the key areas is less than or equal to the preset proportion of the key areas, and the dispersion coefficient of the key areas is greater than the preset dispersion coefficient of the key areas, it is determined that the project construction status of the target construction project is the second preset construction status.

3. The edge computing-based environmental and water conservation monitoring method for power grid construction period according to claim 2 is characterized in that: The determining of the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status includes: When the project construction status of the target construction project is the first preset construction status, the target analysis matching mode is point-related matching, and the target analysis matching parameter types include a regional edge index and a regional monitoring index; When the project construction status of the target construction project is in the second preset construction status, the target analysis matching method is regional cluster matching, and the target analysis matching parameter type includes a key dispersion coefficient.

4. The method for monitoring environmental and water conservation during power grid construction period based on edge computing according to claim 3 is characterized in that: When the target construction project is in a first preset construction state, obtaining parameter data corresponding to the target analysis matching parameter type and determining the erosion dominant coefficient according to the parameter data include: Obtaining a regional edge index and a regional monitoring index, and classifying key analysis points according to the regional edge index and the regional monitoring index to obtain first-category monitoring points and second-category monitoring points; wherein, the key analysis points are analysis points whose monitoring data difference value is greater than a first preset monitoring data difference value or whose change duration is greater than a preset change duration; the monitoring data include: soil particle size and soil moisture content; For a type of monitoring point, a matching analysis combination is generated based on the regional radiation coefficient and the duration of the relevant radiation stage; For the second-class monitoring points, the matching analysis combination is determined based on whether there is a relevant dense area cluster within the preset relevant assessment range; The corresponding dominant assessment method and erosion dominant coefficient are determined for each matching analysis combination.

5. The method for monitoring environmental and water conservation during power grid construction period based on edge computing according to claim 4 is characterized in that: The determining of the matching analysis combination based on whether a relevant dense area set exists within the preset relevant evaluation range includes: If there is a collection of related dense areas within the preset related assessment range, the matching analysis combination is determined according to the erosion radiation quality index; If there is no relevant dense area set within the preset relevant assessment range, the matching analysis combination is determined based on the overlap of regional change trends.

6. The method for monitoring environmental and water conservation during power grid construction period based on edge computing according to claim 4 is characterized in that: The determining of the corresponding dominant assessment method and erosion dominant coefficient for each matching analysis combination includes: If the matching analysis combination of key analysis points is determined based on the overlap of regional change trends, the erosion dominance coefficient is determined based on the reference radiation coefficient of the matching analysis combination; If the matching analysis combination of key analysis points is determined based on the erosion radiation quality index, the erosion dominance coefficient is determined based on the range radiation attenuation coefficient of the matching analysis combination.

7. The edge computing-based environmental and water conservation monitoring method for power grid construction period according to claim 6 is characterized in that: Determining the matching analysis combination according to the erosion radiation quality index includes: Determine the second-class monitoring points contained in the set of relevant dense areas with the largest erosion radiation quality index, and record them as a set of matching analysis combinations; The matching analysis combination is determined based on the overlap of regional change trends, including: Determine the second-class monitoring points whose regional change trend overlap is greater than the preset regional change trend overlap, and record them as a set of matching analysis combinations.

8. The method for monitoring environmental and water conservation during power grid construction period based on edge computing according to claim 4 is characterized in that: When the target construction project is in the second preset construction state, obtaining parameter data corresponding to the target analysis matching parameter type and determining the erosion dominant coefficient according to the parameter data include: Obtain the key dispersion coefficient and determine whether the key analysis area is in a cluster distribution or discrete distribution state based on the key dispersion coefficient; For the key analysis areas of cluster distribution status, the erosion dominance coefficient is determined based on the regional change similarity coefficient and the overlap of the change stages; For critical analysis areas with discrete distribution states, the erosion dominance coefficient is determined based on the duration of the change.

9. A device for monitoring environmental and water conservation during power grid construction period based on edge computing, characterized in that: include: A construction assessment module is used to obtain the proportion of key areas and the dispersion coefficient of key areas of target construction projects in power grid construction, and determine the project construction status based on the proportion of key areas, the dispersion coefficient of key areas, and project assessment conditions; wherein the key areas are target construction areas whose environmental and water conservation risk coefficients are greater than a preset environmental and water conservation risk coefficient; A determination module is used to determine the target analysis matching method and target analysis matching parameter type corresponding to the analysis point according to the project construction status; wherein the types of analysis matching methods include point correlation matching and regional cluster matching; and the types of analysis matching parameters include: regional edge index, regional monitoring index and key dispersion coefficient; The calculation module is used to obtain parameter data corresponding to the target analysis matching parameter type, determine the erosion dominant coefficient based on the parameter data, and calculate the environmental water quality coefficient of the target construction area based on the erosion dominant coefficient.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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