Environment-friendly consultation intelligent decision-making system based on big data and life cycle evaluation

Abstract

The invention relates to the technical field of data analysis, in particular to an environment-friendly consultation intelligent decision-making system based on big data and life cycle evaluation, and the system comprises an information summarization module which is used for setting a target region for executing environment-related information acquisition, segmenting the target region, and obtaining environment-related information in each sub-target region based on a segmentation result; according to the invention, through target region segmentation, three-party comprehensive monitoring is carried out on meteorological information, geographic parameter information and ecological information of each segmented target region, and a deployment recommendation index of each segmented region for a wind generating set is digitally presented; therefore, the degree of dependence of the design of the installation position of the wind generating set on the experience of workers is reduced, the position suitable for installation of the wind generating set in the target area can be intelligently and accurately captured, and the power generation benefit of the installed wind generating set is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to an environmental consulting intelligent decision-making system based on big data and life cycle assessment. Background Art

[0002] Environmental protection equipment refers to various devices, devices, and systems used in the field of environmental protection, designed to prevent and control environmental pollution and improve the quality of the ecological environment. These devices utilize various physical, chemical, and biological techniques to treat, purify, and recycle pollutants generated in production and daily life, thereby reducing pollutant emissions, minimizing environmental harm, and achieving harmonious coexistence between humans and nature. Widely used in various fields, including industry, agriculture, and municipal administration, they are essential tools for achieving sustainable development in modern society. Wind turbines are one example of such environmental protection equipment.

[0003] Large-scale wind farms are mostly located around coastal areas. During the project implementation process, the installation locations of wind turbines need to be designed and surveyed to ultimately determine the optimal installation location within the coastal area. Currently, this work relies entirely on the staff's own experience to design the installation location. The actual benefits of the designed location are directly related to the staff's professional experience, resulting in the possibility that the designed wind turbine installation location may be poor or have room for further optimization.

[0004] To this end, we proposed an environmental consulting intelligent decision-making system based on big data and life cycle assessment. Summary of the Invention

[0005] In response to the above-mentioned shortcomings of the existing technology, the present invention provides an environmental consulting intelligent decision-making system based on big data and life cycle assessment, which solves the technical problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] Environmental consulting intelligent decision-making system based on big data and life cycle assessment, including:

[0008] An information aggregation module is used to set a target area for acquiring execution environment-related information, segment the target area, and acquire environment-related information in each sub-target area based on the segmentation results; a monitoring module is used to receive environment-related information of each sub-target area in the information aggregation module, and monitor the dynamic matching degree between the sub-target area and the wind turbine generator set based on the environment-related information; an analysis module is used to obtain the dynamic matching degree monitoring results between the sub-target area and the wind turbine generator set in the monitoring module, and analyze the recommendation index for deploying wind turbine generator sets in the sub-target area based on the monitoring results; an evaluation module is used to evaluate the life cycle of the sub-target area when deploying wind turbine generator sets; a queue module is used to sort the sub-target areas based on the recommendation index and life cycle of deploying wind turbine generator sets in the sub-target areas to generate a sub-target area queue; an output module is used to customize the design deployment quantity of wind turbine generator sets, select a corresponding number of sub-target areas in the sub-target area queue based on the design deployment quantity of wind turbine generator sets, and output them;

[0009] When outputting the sub-target area, the output module takes the front sub-target area in the sub-target area queue as the priority output target, and the output target of the output module is the mobile computer device held by the system end user.

[0010] Furthermore, the environmental related information includes meteorological information, geographical parameter information, and ecological information. During the setting phase, the target area is determined based on a range defined by a number of position coordinates customized by the system end user. When the target area is segmented, the adjacent distances between the center points of the sub-target areas obtained by segmentation are all greater than the preset installation spacing of the wind turbine generator sets.

[0011] Among them, when the sub-target area obtains environmental related information, meteorological information includes wind speed and wind direction, geographic parameter information includes land elevation, land vegetation coverage, road building facility coverage, seabed depth, seawater flow rate, and ecological information includes the number of biological species and whether it is in the animal migration path.

[0012] Furthermore, the information summary module is provided with submodules at the lower level, including:

[0013] A storage unit is used to receive the environment-related information obtained by the information aggregation module, and to differentiate and store the environment-related information based on the sub-target area of ​​the environment information source;

[0014] When the meteorological information in the environmental related information is stored in the different storage intervals, it is sorted and stored based on the acquisition time, and the meteorological information is continuously acquired based on a specified period.

[0015] Furthermore, the monitoring module is provided with submodules at the lower level, including:

[0016] a retrieving unit, configured to retrieve the sub-target area environment-related information stored in the storage unit, and forward the retrieved sub-target area environment-related information to the monitoring module;

[0017] A visualization unit is configured to receive a monitoring result of a dynamic matching degree between a sub-target area and a wind turbine generator set in the monitoring module, and generate a visualization model representing the dynamic matching degree between the sub-target area and the wind turbine generator set based on the monitoring result;

[0018] Among them, each time the retrieval unit executes the sub-target area environment-related information retrieval operation, the environment-related information of each sub-target area is retrieved based on a custom number of meteorological information continuous acquisition cycles of no less than three, so that each time the monitoring module runs, a monitoring operation is performed on the dynamic matching degree between each sub-target area and the wind turbine generator set.

[0019] Furthermore, the generation logic of the visualization model in the visualization unit is expressed as:

[0020] Construct several spherical models with the same diameter in three-dimensional space. The number of spherical models is equal to the number of sub-target areas. Use the spherical models as the carriers of the visualization model.

[0021] Based on the continuous operation of the monitoring module, a series of dynamic matching degree monitoring results of each sub-target area corresponding to the number of sub-target areas is obtained, which is recorded as [P(a)1,P(a)2,...P(a) x-1 ,P(a) x ]、[P(b)1,P(b)2,...P(b) x-1 ,P(b) x ]、[P(c)1,P(c)2,...P(c) x-1 ,P(c) x ], ...;

[0022] Each set of sequence is applied to a sphere model to generate the visualization model;

[0023] The dynamic matching degree monitoring results are obtained in sequence as the radius, and any point on the surface of the sphere model is used as the center of the sphere. The sphere is constructed on the surface of the sphere model in combination with the radius;

[0024] Except for the first construction of a sphere on the surface of the sphere model, it obeys:

[0025] COND1: The next constructed sphere surface is tangent to the previous constructed sphere surface;

[0026] COND2: When COND1 cannot be met, the center point of the area on the surface of the sphere model where the sphere is not constructed is used as the center of the sphere, and the dynamic matching degree monitoring result is used as the radius to construct a sphere on the surface of the sphere model;

[0027] The process ends when the surface of the sphere model is completely covered by the constructed sphere. The sphere model whose surface is completely covered by the sphere is recorded as a visualization model.

[0028] Among them, P(a)1 represents the result of the first dynamic matching degree monitoring of sub-target area a.

[0029] [P(a)1,P(a)2,...P(a) x-1 ,P(a) x ]、[P(b)1,P(b)2,...P(b) x-1 ,P(b) x ]、[P(c)1,P(c)2,...P(c) x-1 ,P(c) x ], ...The definitions of the parameters are similar.

[0030] Furthermore, the monitoring logic of the dynamic matching degree between the sub-target area and the wind turbine generator set in the monitoring module is expressed as follows:

[0031]

[0032] Where: p is the dynamic matching degree between the sub-target area and the wind turbine generator set; n is the total amount of meteorological information; s i is the wind speed in the i-th group of meteorological information; W i is the wind direction in the i-th group of meteorological information; h max 、h min is the maximum and minimum land elevation in the geographic parameter information; k B 、k V is the land vegetation coverage rate and road construction facility coverage rate in the geographic parameter information; m is the number of biological species in the ecological information; f(i) is the judgment function, which represents the judgment value of the i-th species. When the migration path of the i-th species is in the sub-target area, the value is 1, otherwise, the value is 0; d max d min v is the maximum and minimum seabed depth in the geographic parameter information; max 、v min The maximum and minimum values ​​of seawater velocity in the geographic parameter information;

[0033] in, Express When the sub-target area is land, it is calculated by formula (1); when the sub-target area is water, it is calculated by formula (2); when the sub-target area contains land and water, it is calculated based on formula (1) and formula (2), and the two calculation results are normalized and averaged. After all sub-target areas are calculated once by formula (1) and formula (2), all calculation results are normalized.

[0034] Furthermore, the normalization processing logic of the dynamic matching degree between the sub-target area and the wind turbine generator set is:

[0035]

[0036] Where: P is the dynamic matching degree between the sub-target area and the wind turbine after normalization; p max The maximum value among the dynamic matching calculation results of each sub-target area and the wind turbine generator set;

[0037] Among them, when the sub-target area corresponding to p is land, p max Based on the dynamic matching calculation results of all sub-target areas that are land and wind turbines, when the sub-target area corresponding to p is water area, p max The determination is based on the calculation results of the dynamic matching degree between all sub-target areas that are water areas and wind turbines.

[0038] Furthermore, when analyzing the recommendation index for deploying wind turbines in the sub-target area in the analysis module, an analysis operation is performed based on the visualization model corresponding to the sub-target area:

[0039]

[0040] Where: NPS is the recommended index for deploying wind turbines in the sub-target area; Q MAX The maximum exposed volume of the sphere on the surface of the visualization model; Q norr is the volume of the sphere determined by the maximum diameter of the visualization model; Q0 is the volume of the visualization model; M is the total number of spheres that intersect with each other on the surface of the visualization model; Q g is the spatial volume of the intersection area between the g-th sphere and the sphere it intersects; θ is the normalization factor:

[0041] Here, θ is in the range of (0, 1).

[0042] Furthermore, the evaluation logic of the life cycle of the sub-target area in the evaluation module when deploying wind turbine generator sets is expressed as follows:

[0043]

[0044] Where: C is the expected life cycle of the sub-target area when deploying wind turbines; L0 is the designed service life cycle of the wind turbine; f(W) is the wind speed correction function; f(T) is the temperature correction function; f(H) is the humidity correction function; f(S) is the sand or salt spray correction function; D is the correction coefficient;

[0045] Among them, when the queue module generates the sub-target area queue, it uses the product of the evaluation result of the evaluation module and the NPS as a reference, so that the sub-target areas with larger corresponding product results are arranged in front, and the sub-target areas with smaller corresponding product results are arranged in the back.

[0046] Furthermore, the information aggregation module is interactively connected to a storage unit via a wireless network, the information aggregation module is interactively connected to a monitoring module via a wireless network, the monitoring module is interactively connected to a retrieval unit and a visualization unit at a lower level via a wireless network, the retrieval unit is interactively connected to the storage unit via a wireless network, the monitoring module is interactively connected to an analysis module, an evaluation module and a queue module via a wireless network, and the analysis module, evaluation module and queue module are interactively connected to an output module via a wireless network.

[0047] Compared with the known public technology, the technical solution provided by the present invention has the following advantages:

[0048] Beneficial effects:

[0049] 1. The present invention divides the target area into segments, conducts a three-way integrated monitoring of the meteorological information, geographic parameter information and ecological information of each segmented target area, and digitally presents the recommended index for the deployment of wind turbines in each segmented area, thereby reducing the degree of dependence of the wind turbine installation location design on the staff's own experience, so that the location suitable for the installation of wind turbines in the target area can be intelligently and accurately captured, effectively improving the power generation efficiency of the wind turbine after installation.

[0050] 2. During the design stage of the installation location of the wind turbine generator set, the present invention further combines the life cycle evaluation of the target area when deploying wind turbine generator sets to correct the matching degree of the installation location of each wind turbine generator set, further improving the accuracy of the recommended installation location of the wind turbine generator set, so that the design and planning of the installation location of the wind turbine generator set based on the participation of this system can output the optimal solution more quickly and with lower labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0052] Figure 1 This is a structural diagram of an environmental consulting intelligent decision-making system based on big data and life cycle assessment;

[0053] Figure 2This is an example schematic diagram of a visualization model of the dynamic matching degree between the neutron target area and the wind turbine generator set according to the present invention. DETAILED DESCRIPTION

[0054] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] The present invention will be further described below with reference to the embodiments.

[0056] Example 1:

[0057] The environmental protection consulting intelligent decision-making system based on big data and life cycle assessment in this embodiment is as follows: Figure 1 Shown, including:

[0058] An information aggregation module is used to set a target area for executing environment-related information acquisition, segment the target area, and acquire environment-related information in each sub-target area based on the segmentation results;

[0059] The information summary module is divided into submodules, including:

[0060] A storage unit is used to receive the environment-related information obtained by the information aggregation module, and to differentiate and store the environment-related information based on the sub-target area of ​​the environment information source;

[0061] Among them, when the meteorological information in the environmental related information is stored in the different storage intervals, it is sorted and stored based on the acquisition time, and the meteorological information is continuously acquired based on the specified period;

[0062] A monitoring module is used to receive the environmental related information of each sub-target area in the information summary module, and monitor the dynamic matching degree between the sub-target area and the wind turbine generator set based on the environmental related information;

[0063] The monitoring module is equipped with submodules, including:

[0064] a retrieving unit, configured to retrieve the sub-target area environment-related information stored in the storage unit, and forward the retrieved sub-target area environment-related information to the monitoring module;

[0065] A visualization unit is configured to receive a monitoring result of a dynamic matching degree between a sub-target area and a wind turbine generator set in the monitoring module, and generate a visualization model representing the dynamic matching degree between the sub-target area and the wind turbine generator set based on the monitoring result;

[0066] Each time the retrieval unit performs a retrieval operation for the sub-target area environment-related information, the retrieval unit retrieves the environment-related information for each sub-target area based on a user-defined number of continuous meteorological information acquisition cycles of not less than three, so that each time the monitoring module runs, a monitoring operation is performed on the dynamic matching degree between each sub-target area and the wind turbine generator set;

[0067] The generation logic of the visualization model in the visualization unit is expressed as:

[0068] Construct several spherical models with the same diameter in three-dimensional space. The number of spherical models is equal to the number of sub-target areas. Use the spherical models as the carriers of the visualization model.

[0069] Based on the continuous operation of the monitoring module, a series of dynamic matching degree monitoring results of each sub-target area corresponding to the number of sub-target areas is obtained, which is recorded as [P(a)1,P(a)2,...P(a) x-1 ,P(a) x ]、[P(b)1,P(b)2,...P(b) x-1 ,P(b) x ]、[P(c)1,P(c)2,...P(c) x-1 ,P(c) x ], ...;

[0070] Each set of sequence is applied to a sphere model to generate the visualization model;

[0071] The dynamic matching degree monitoring results are obtained in sequence as the radius, and any point on the surface of the sphere model is used as the center of the sphere. The sphere is constructed on the surface of the sphere model in combination with the radius;

[0072] Except for the first construction of a sphere on the surface of the sphere model, it obeys:

[0073] COND1: The next constructed sphere surface is tangent to the previous constructed sphere surface;

[0074] COND2: When COND1 cannot be met, the center point of the area on the surface of the sphere model where the sphere is not constructed is used as the center of the sphere, and the dynamic matching degree monitoring result is used as the radius to construct a sphere on the surface of the sphere model;

[0075] The process ends when the surface of the sphere model is completely covered by the constructed sphere. The sphere model whose surface is completely covered by the sphere is recorded as a visualization model.

[0076] Among them, P(a)1 represents the result of the first dynamic matching degree monitoring of sub-target area a.

[0077] [P(a)1,P(a)2,...P(a) x-1,P(a) x ]、[P(b)1,P(b)2,...P(b) x-1 ,P(b) x ]、[P(c)1,P(c)2,...P(c) x-1 ,P(c) x ], ... The definitions of various parameters are similar;

[0078] Through the above logic, the visualization model constructed in the visualization unit is provided with the specified construction logic.

[0079] The monitoring logic of the dynamic matching degree between the sub-target area and the wind turbine generator set in the monitoring module is expressed as follows:

[0080]

[0081] Where: p is the dynamic matching degree between the sub-target area and the wind turbine generator set; n is the total amount of meteorological information; s i is the wind speed in the i-th group of meteorological information; W i is the wind direction in the i-th group of meteorological information; h max 、h min is the maximum and minimum land elevation in the geographic parameter information; k B 、k V is the land vegetation coverage rate and road construction facility coverage rate in the geographic parameter information; m is the number of biological species in the ecological information; f(i) is the judgment function, which represents the judgment value of the i-th species. When the migration path of the i-th species is in the sub-target area, the value is 1, otherwise, the value is 0; d max d min v is the maximum and minimum seabed depth in the geographic parameter information; max 、v min The maximum and minimum values ​​of seawater velocity in the geographic parameter information;

[0082] in, Express The average operation is performed. When the sub-target area is land, it is calculated by formula (1). When the sub-target area is water, it is calculated by formula (2). When the sub-target area contains land and water, it is calculated based on formula (1) and formula (2), and the two calculation results are normalized and averaged. After all sub-target areas are calculated once by formula (1) and formula (2), all calculation results are normalized.

[0083] Through the above logical formula, the dynamic matching degree between the sub-target area and the wind turbine generator set is calculated, and the land and water areas are distinguished. The calculation is implemented with different calculation logics, and the calculation results are normalized to finally represent the dynamic matching degree between the sub-target area and the wind turbine generator set, providing necessary operation data support for the operation of subsequent modules in this system.

[0084] The normalization processing logic of the dynamic matching degree between the sub-target area and the wind turbine generator set is:

[0085]

[0086] Where: P is the dynamic matching degree between the sub-target area and the wind turbine after normalization; p max The maximum value among the dynamic matching calculation results of each sub-target area and the wind turbine generator set;

[0087] Among them, when the sub-target area corresponding to p is land, p max Based on the dynamic matching calculation results of all sub-target areas that are land and wind turbines, when the sub-target area corresponding to p is water area, p max Determined based on the calculation results of the dynamic matching degree between all sub-target areas that are water areas and wind turbine generator sets;

[0088] An analysis module is used to obtain the dynamic matching degree monitoring results between the sub-target area and the wind turbine generator set in the monitoring module, and analyze the recommendation index for deploying the wind turbine generator set in the sub-target area based on the monitoring results;

[0089] When analyzing the recommended index for deploying wind turbines in the sub-target area in the analysis module, the analysis operation is performed based on the corresponding visualization model of the sub-target area:

[0090]

[0091] Where: NPS is the recommended index for deploying wind turbines in the sub-target area; Q MAX The maximum exposed volume of the sphere on the surface of the visualization model; Q norr is the volume of the sphere determined by the maximum diameter of the visualization model; Q0 is the volume of the visualization model; M is the total number of spheres that intersect with each other on the surface of the visualization model; Q g is the spatial volume of the intersection area between the g-th sphere and the sphere it intersects; θ is the normalization factor:

[0092] The above logic formula defines the logic for obtaining the recommended index for deploying wind turbines in the sub-target area.

[0093] Among them, θ is in the range of (0, 1);

[0094] An evaluation module, used to evaluate the life cycle of the sub-target area when deploying wind turbines;

[0095] A queue module is used to sort the sub-target areas based on the recommended index and life cycle of the wind turbines deployed in the sub-target areas to generate a sub-target area queue;

[0096] An output module is used to customize the number of wind turbine generator sets designed for deployment, select a corresponding number of sub-target areas from the sub-target area queue based on the number of wind turbine generator sets designed for deployment, and output the result;

[0097] Wherein, when outputting the sub-target area, the output module takes the front-position sub-target area in the sub-target area queue as the priority output target, and the output target of the output module is the mobile computer device held by the system end user;

[0098] The information aggregation module is interactively connected to a storage unit via a wireless network, the information aggregation module is interactively connected to a monitoring module via a wireless network, the monitoring module is interactively connected to a retrieval unit and a visualization unit at its lower level via a wireless network, the retrieval unit is interactively connected to the storage unit via a wireless network, the monitoring module is interactively connected to an analysis module, an evaluation module and a queue module via a wireless network, and the analysis module, evaluation module and queue module are interactively connected to an output module via a wireless network.

[0099] In this embodiment, the information summary module runs to set a target area for executing environment-related information acquisition, divides the target area, and obtains environment-related information in each sub-target area based on the segmentation result. The storage unit synchronously receives the environment-related information obtained in the information summary module, and distinguishes and stores the environment-related information based on the sub-target area of ​​the environment information source. The monitoring module is post-operated to receive the environment-related information of each sub-target area in the information summary module, and monitors the dynamic matching degree between the sub-target area and the wind turbine generator set based on the environment-related information. The retrieval unit synchronously retrieves the stored environment-related information of the sub-target area in the storage unit, and forwards the retrieved environment-related information of the sub-target area to the monitoring module. The visualization unit receives the information of the sub-target area and the wind turbine generator set in the monitoring module in real time. The dynamic matching degree monitoring results of the group are obtained, and a visualization model representing the dynamic matching degree between the sub-target area and the wind turbine generator set is generated based on the monitoring results. The analysis module further obtains the dynamic matching degree monitoring results between the sub-target area and the wind turbine generator set in the monitoring module, and analyzes the recommendation index for deploying wind turbines in the sub-target area based on the monitoring results. The evaluation module then evaluates the life cycle of the sub-target area when deploying wind turbines, and sorts the sub-target areas through the queue module based on the recommendation index and life cycle of deploying wind turbines in the sub-target area to generate a sub-target area queue. Finally, the output module customizes the design deployment number of wind turbines, selects the corresponding number of sub-target areas in the sub-target area queue based on the design deployment number of wind turbines, and outputs them.

[0100] Through the operation of the system in the above embodiment, intelligent recommendations for the design and deployment locations of wind turbines are provided for coastal areas, which is effectively different from the current manual design decisions, has higher accuracy and efficiency, and makes the design and deployment of wind turbines more economical and scientific.

[0101] See also Figure 2 As shown, based on the arrows in the figure, the process of transforming the spherical model into a visual model is demonstrated.

[0102] like Figure 1 As shown, the environmental related information includes meteorological information, geographical parameter information, and ecological information. During the setting phase, the target area is determined based on the range defined by a number of position coordinates edited by the system end user. When the target area is segmented, the adjacent distances between the center points of the sub-target areas obtained by segmentation are all greater than the preset wind turbine installation spacing;

[0103] Among them, when the sub-target area obtains environmental related information, meteorological information includes wind speed and wind direction, geographic parameter information includes land elevation, land vegetation coverage, road building facility coverage, seabed depth, seawater flow rate, and ecological information includes the number of biological species and whether it is in the animal migration path.

[0104] Through the above settings, the installation spacing of the wind turbine generator sets is set, and the specific content of the target area environment-related information is limited.

[0105] Example 2:

[0106] In terms of specific implementation, based on Example 1, this example refers to Figure 2 The environmental protection consulting intelligent decision-making system based on big data and life cycle assessment in Example 1 is further described in detail:

[0107] The evaluation logic of the life cycle of the sub-target area in the evaluation module when deploying wind turbines is expressed as follows:

[0108]

[0109] Where: C is the expected life cycle of the sub-target area when deploying wind turbines; L0 is the designed service life cycle of the wind turbine; f(W) is the wind speed correction function; f(T) is the temperature correction function; f(H) is the humidity correction function; f(S) is the sand or salt spray correction function; D is the correction coefficient;

[0110] Among them, when the queue module generates the sub-target area queue, it uses the product of the evaluation result of the evaluation module and the NPS as a reference, so that the sub-target areas with larger corresponding product results are arranged in front, and the sub-target areas with smaller corresponding product results are arranged in the back.

[0111] The above logic formula is used to calculate the expected life cycle of the sub-target area when deploying wind turbines, providing support for the output module when deploying wind turbines in the output sub-target area.

[0112] It should be noted that:

[0113] f(W) design stage:

[0114] Too high or too low wind speed will affect the life of the unit. You can use the long-term average wind speed V in the area to determine the life of the unit. avg To determine, for example, when V avg When the wind speed is within the optimum design range, f(W) is close to 1. avg When deviating from the optimal range, the value of f(W) is set to be reduced accordingly. By statistically analyzing historical data, a piecewise function similar to the following can be established:

[0115] When V opt-ΔV ≤V avg ≤V opt+ΔV When f(W)=1, where V opt is the optimal design wind speed of the unit, and ΔV is the allowable wind speed fluctuation range;

[0116] When Vavg <V opt-ΔV When f(W)=k1×(V avg / (V opt-ΔV ), k1 is a constant less than 1, used to adjust the effect of low wind speed on life;

[0117] When V avg >V opt+ΔV When f(W)=k2×((V opt+ΔV ) / V avg ), k2 is the same as k1;

[0118] f(T) design stage:

[0119] Extreme high or low temperatures can affect the material properties, lubrication effects, and stability of electronic equipment of the unit. avg and the temperature fluctuation range, for example, when T avg When the unit is within the suitable operating temperature range, f(T) is close to 1. avg When the temperature deviates from the appropriate range, the value of f(T) is set to be reduced accordingly. Assuming that the appropriate operating temperature range is [T min ,T max ], you can create the following function:

[0120] When T min ≤T avg ≤T max When f(T)=1;

[0121] When T avg <T min When f(T)=k3×(1-(T min -T avg ) / ΔT), ΔT is the reference temperature threshold, k3 is the same as k1;

[0122] When T avg >T max When f(T)=k4×(1-(T avg -T max ) / ΔT), k4 is the same as k1;

[0123] f(H) Design stage:

[0124] High humidity may cause equipment corrosion and deterioration of insulation performance, while low humidity may cause static electricity problems. avg To determine, when H avg In the appropriate humidity range, f(H) is close to 1. avg When deviating from this range, the value of f(H) will change accordingly. For example, assuming the appropriate humidity range is [H min ,Hmax ], then the function is:

[0125] When H min ≤H avg ≤H max When f(H)=1;

[0126] When H avg <H min When f(H)=k5×(H avg / H min ), k5 and k1 are the same;

[0127] When H avg <H min When f(H)=k6×(H max / H avg ), k6 and k1 are the same;

[0128] f(S) design stage:

[0129] If the area is dusty or close to the sea with salt spray corrosion, it will accelerate the wear and corrosion of equipment. You can calculate the dust concentration S in the area. dust Or salt spray content S salt To determine, when the dust concentration or salt mist content is low, f(S) is close to 1; when the content is high, the value of f(S) will decrease accordingly. Therefore, the following function can be established:

[0130] When S dust ≤S0 or S salt When ≤S0, f(S)=1, S0 is a preset dust or salt mist concentration threshold;

[0131] When S dust When S0, f(S)=k7×(S0 / S dust ), k7 and k1 are the same;

[0132] When S salt When S0, f(S)=k8×(S0 / S salt ), the same applies to k8 and k1;

[0133] D is a comprehensive correction factor for other factors that may affect the lifespan, including but not limited to grid stability, terrain complexity, and maintenance level. For example, if the grid voltage fluctuates greatly, it may affect the lifespan of the unit's electrical equipment, and the value of D will increase accordingly. If complex terrain makes maintenance difficult, the value of D will also increase. The value range of D can be determined based on specific circumstances through expert evaluation or historical data statistics.

[0134] In summary, in the above embodiment, during operation, the system conducts a three-way comprehensive monitoring of the meteorological information, geographic parameter information and ecological information of each segmented target area, and digitally presents the deployment recommendation index of the wind turbine generator set in each segmented area, thereby reducing the dependence of the wind turbine generator set installation location design on the staff's own experience, so that the location suitable for the installation of wind turbine generator sets in the target area can be intelligently and accurately captured, effectively improving the power generation efficiency of the wind turbine generator set after installation. At the same time, in the design stage of the wind turbine generator set installation location, the life cycle evaluation of the target area when deploying wind turbine generator sets is further combined to correct the matching degree of the installation location of each wind turbine generator set, further improving the accuracy of the wind turbine generator set installation location recommendation, so that the wind turbine generator set installation location design and planning based on the participation of this system can output the best solution more quickly and with lower labor costs.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention 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. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent environmental consulting decision-making system based on big data and life cycle assessment, characterized by: include: An information aggregation module is used to set a target area for executing environment-related information acquisition, segment the target area, and acquire environment-related information in each sub-target area based on the segmentation results; A monitoring module is used to receive the environmental related information of each sub-target area in the information summary module, and monitor the dynamic matching degree between the sub-target area and the wind turbine generator set based on the environmental related information; An analysis module is used to obtain the dynamic matching degree monitoring results between the sub-target area and the wind turbine generator set in the monitoring module, and analyze the recommendation index for deploying the wind turbine generator set in the sub-target area based on the monitoring results; An evaluation module, used to evaluate the life cycle of the sub-target area when deploying wind turbines; A queue module is used to sort the sub-target areas based on the recommended index and life cycle of the wind turbines deployed in the sub-target areas to generate a sub-target area queue; An output module is used to customize the number of wind turbine generator sets designed for deployment, select a corresponding number of sub-target areas from the sub-target area queue based on the number of wind turbine generator sets designed for deployment, and output the result; When outputting the sub-target area, the output module takes the front sub-target area in the sub-target area queue as the priority output target, and the output target of the output module is the mobile computer device held by the system end user.

2. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 1 is characterized in that: The environmental related information includes meteorological information, geographical parameter information, and ecological information. During the setting phase, the target area is determined based on the range defined by a number of position coordinates customized by the system end user. When the target area is segmented, the adjacent distances between the center points of the sub-target areas obtained by segmentation are all greater than the preset wind turbine installation spacing; Among them, when the sub-target area obtains environmental related information, meteorological information includes wind speed and wind direction, geographic parameter information includes land elevation, land vegetation coverage, road building facility coverage, seabed depth, seawater flow rate, and ecological information includes the number of biological species and whether it is in the animal migration path.

3. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 1 is characterized in that: The information summary module is provided with submodules at the lower level, including: A storage unit is used to receive the environment-related information obtained by the information aggregation module, and to differentiate and store the environment-related information based on the sub-target area of ​​the environment information source; When the meteorological information in the environmental related information is stored in the different storage intervals, it is sorted and stored based on the acquisition time, and the meteorological information is continuously acquired based on a specified period.

4. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 1 is characterized in that: The monitoring module is provided with submodules at the lower level, including: a retrieving unit, configured to retrieve the sub-target area environment-related information stored in the storage unit, and forward the retrieved sub-target area environment-related information to the monitoring module; A visualization unit is configured to receive a monitoring result of a dynamic matching degree between a sub-target area and a wind turbine generator set in the monitoring module, and generate a visualization model representing the dynamic matching degree between the sub-target area and the wind turbine generator set based on the monitoring result; Among them, each time the retrieval unit executes the sub-target area environment-related information retrieval operation, the environment-related information of each sub-target area is retrieved based on a custom number of meteorological information continuous acquisition cycles of no less than three, so that each time the monitoring module runs, a monitoring operation is performed on the dynamic matching degree between each sub-target area and the wind turbine generator set.

5. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 4 is characterized in that: The generation logic of the visualization model in the visualization unit is expressed as follows: Construct several spherical models with the same diameter in three-dimensional space. The number of spherical models is equal to the number of sub-target areas. Use the spherical models as the carriers of the visualization model. Based on the continuous operation of the monitoring module, a series of dynamic matching degree monitoring results of each sub-target area corresponding to the number of sub-target areas is obtained, which is recorded as [P(a)1,P(a)2,...P(a) x-1 ,P(a) x ]、[P(b)1,P(b)2,...P(b) x-1 ,P(b) x ]、[P(c)1,P(c)2,...P(c) x-1 ,P(c) x ], ...; Each set of sequence is applied to a sphere model to generate the visualization model; The dynamic matching degree monitoring results are obtained in sequence as the radius, and any point on the surface of the sphere model is used as the center of the sphere. The sphere is constructed on the surface of the sphere model in combination with the radius; Except for the first construction of a sphere on the surface of the sphere model, it obeys: COND1: The next constructed sphere surface is tangent to the previous constructed sphere surface; COND2: When COND1 cannot be met, the center point of the area on the surface of the sphere model where the sphere is not constructed is used as the center of the sphere, and the dynamic matching degree monitoring result is used as the radius to construct a sphere on the surface of the sphere model; The process ends when the surface of the sphere model is completely covered by the constructed sphere. The sphere model whose surface is completely covered by the sphere is recorded as a visualization model. Among them, P(a)1 represents the result of the first dynamic matching degree monitoring of sub-target area a. [P(a)1,P(a)2,...P(a) x-1 ,P(a) x ]、[P(b)1,P(b)2,...P(b) x-1 ,P(b) x ]、[P(c)1,P(c)2,...P(c) x-1 ,P(c) x ], ...The definitions of the various parameters are similar.

6. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 1 is characterized in that: The monitoring logic of the dynamic matching degree between the sub-target area and the wind turbine generator set in the monitoring module is expressed as follows: ； Where: p is the dynamic matching degree between the sub-target area and the wind turbine generator set; n is the total amount of meteorological information; s i is the wind speed in the i-th group of meteorological information; W i is the wind direction in the i-th group of meteorological information; h max 、h min is the maximum and minimum land elevation in the geographic parameter information; k B 、k V is the land vegetation coverage rate and road construction facility coverage rate in the geographic parameter information; m is the number of biological species in the ecological information; f(i) is the judgment function, which represents the judgment value of the i-th species. When the migration path of the i-th species is in the sub-target area, the value is 1, otherwise, the value is 0; d max d min v is the maximum and minimum seabed depth in the geographic parameter information; max 、v min The maximum and minimum values ​​of seawater velocity in the geographic parameter information; in, Express When the sub-target area is land, it is calculated by formula (1); when the sub-target area is water, it is calculated by formula (2); when the sub-target area contains land and water, it is calculated based on formula (1) and formula (2), and the two calculation results are normalized and averaged. After all sub-target areas are calculated once by formula (1) and formula (2), all calculation results are normalized.

7. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 6 is characterized in that: The normalization processing logic of the dynamic matching degree between the sub-target area and the wind turbine generator set is: Where: P is the dynamic matching degree between the sub-target area and the wind turbine after normalization; p max The maximum value among the dynamic matching calculation results of each sub-target area and the wind turbine generator set; Among them, when the sub-target area corresponding to p is land, p max Based on the dynamic matching calculation results of all sub-target areas that are land and wind turbines, when the sub-target area corresponding to p is water area, p max The determination is based on the calculation results of the dynamic matching degree between all sub-target areas that are water areas and wind turbines.

8. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 1 is characterized in that: When analyzing the recommendation index for deploying wind turbines in the sub-target area in the analysis module, the analysis operation is performed based on the corresponding visualization model of the sub-target area: Where: NPS is the recommended index for deploying wind turbines in the sub-target area; Q MAX The maximum exposed volume of the sphere on the surface of the visualization model; Q norr is the volume of the sphere determined by the maximum diameter of the visualization model; Q0 is the volume of the visualization model; M is the total number of spheres that intersect with each other on the surface of the visualization model; Q g is the spatial volume of the intersection area between the g-th sphere and the sphere it intersects; is the normalization factor: in, In the range of (0, 1).

9. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 1 is characterized in that: The evaluation logic of the life cycle of the sub-target area in the evaluation module when deploying wind turbines is expressed as follows: Where: C is the expected life cycle of the sub-target area when deploying wind turbines; L0 is the designed service life cycle of the wind turbine; f(W) is the wind speed correction function; f(T) is the temperature correction function; f(H) is the humidity correction function; f(S) is the sand or salt spray correction function; D is the correction coefficient; Among them, when the queue module generates the sub-target area queue, it uses the product of the evaluation result of the evaluation module and the NPS as a reference, so that the sub-target areas with larger corresponding product results are arranged in front, and the sub-target areas with smaller corresponding product results are arranged in the back.

10. The environmental consulting intelligent decision-making system based on big data and life cycle assessment according to claim 1 is characterized in that: The information aggregation module is interactively connected to a storage unit via a wireless network, the information aggregation module is interactively connected to a monitoring module via a wireless network, the monitoring module is interactively connected to a retrieval unit and a visualization unit at a lower level via a wireless network, the retrieval unit is interactively connected to the storage unit via a wireless network, the monitoring module is interactively connected to an analysis module, an evaluation module and a queue module via a wireless network, and the analysis module, evaluation module and queue module are interactively connected to an output module via a wireless network.

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