Photovoltaic power station ecological environment monitoring point optimization method and device

Abstract

The invention provides a photovoltaic power station ecological environment monitoring point optimization method and device, and relates to the technical field of monitoring. The method comprises the following steps: generating m original data sets based on monitoring data of n monitoring point positions for monitoring m monitoring indexes, and generating a to-be-optimized list according to identifiers of the n monitoring point positions; when the first monitoring index of the first monitoring point position is traversed, determining the monitoring data of the first monitoring indexes of other n-1 monitoring point positions as a comparison data set; after the m monitoring indexes of the n monitoring point positions are traversed, target differences between the n comparison data sets of each monitoring index and the corresponding original data sets are determined respectively; adding the identifier of the target object meeting the preset difference condition to a saving list; and selecting a target object meeting a preset optimization condition from the saving list and determining the target object as the optimization object of the round. Based on the technical scheme, the monitoring cost of the ecological environment of the photovoltaic power station can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of monitoring technology, and in particular to a method and device for optimizing monitoring points of an ecological environment of a photovoltaic power station. Background Art

[0002] With global climate change and the rise of human energy demand, the global photovoltaic power generation capacity has accelerated in recent years, and photovoltaic power generation technology has developed rapidly. Photovoltaic power stations and the ecological environment in their locations usually have a feedback relationship. Therefore, in order to observe the ecological and environmental impact of photovoltaic power stations and ensure the production safety of photovoltaic power stations, it is necessary to conduct long-term observations of their ecological and environmental elements.

[0003] Photovoltaic power stations will have an impact on the ecological environment of their locations due to processes such as construction and shading of the surface. This impact varies due to climate and environmental background, and it is necessary to spread out observations on a spatiotemporal scale. Environmental factors such as regional temperature, dust and vegetation will also significantly affect the power generation and production safety of photovoltaic power stations. Therefore, observations of the ecological and environmental factors of photovoltaic power stations usually require a large number of points to be deployed in space to enhance their representativeness.

[0004] However, the service life of monitoring equipment (5-8 years) is usually much shorter than the operation cycle of a photovoltaic power station (20-25 years). During the operation of a photovoltaic power station, in order to conduct the above-mentioned ecological and environmental impact monitoring, the monitoring system needs to be replaced in batches. At present, after setting up multiple monitoring points at the photovoltaic power station site, the monitoring points are usually not optimized. However, there is a certain redundancy in the number of points and equipment arranged in the early stage, which will lead to excessively high costs for monitoring the ecological environment of photovoltaic power stations.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In order to overcome the problems existing in the related art, the embodiments of the present disclosure provide a method and device for optimizing the monitoring points of the ecological environment of a photovoltaic power station, which can reduce the cost of the ecological environment monitoring of a photovoltaic power station.

[0007] According to a first aspect of an embodiment of the present disclosure, a method for optimizing monitoring points of an ecological environment of a photovoltaic power station is provided, the method comprising: generating m original data sets based on monitoring data of n monitoring points that monitor m monitoring indicators, and generating a list to be optimized according to the identifiers of the n monitoring points, wherein one monitoring indicator corresponds to one original data set, m is a positive integer, and n is an integer greater than 1; starting to traverse the monitoring points according to the list to be optimized, and when traversing to the first monitoring point, starting to traverse the monitoring indicators according to the m monitoring indicators, and when traversing to the first monitoring indicator, determining the monitoring data of the first monitoring indicator of the other n-1 monitoring points as the optimization target; Comparison data sets, each monitoring indicator corresponds to n comparison data sets, and one comparison data set corresponds to one monitoring point; the first monitoring point is any one of the n monitoring points, and the first monitoring indicator is any one of the m monitoring indicators; after traversing the m monitoring indicators of the n monitoring points, respectively determine the target difference between the n comparison data sets and the corresponding original data sets of each monitoring indicator; add the identifier of the target object that meets the preset difference condition to the savable list, and the target object is the monitoring point or the monitoring equipment in the monitoring point; select the target object that meets the preset optimization condition from the savable list to determine it as the optimization object of this round.

[0008] Optionally, the method also includes: after the current round of optimization is completed, deleting the identifier of the target object to be optimized in the list to be optimized; restarting to traverse each monitoring indicator of each monitoring point according to the list to be optimized after deleting the identifier of the target object to be optimized, and determining a comparison data set for each monitoring indicator of each monitoring point.

[0009] Optionally, a monitoring point includes only one monitoring device, and one monitoring device monitors m monitoring indicators; if the target difference of the m monitoring indicators of the first monitoring point meets the preset difference condition, the identifier of the first monitoring point is added to the saving list.

[0010] Optionally, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one of m monitoring indicators, and multiple monitoring devices monitor m monitoring indicators; if the target difference of all monitoring indicators of all monitoring devices at the first monitoring point is less than the preset difference, the point identifier of the first monitoring point is added to the saving list.

[0011] Optionally, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one monitoring indicator, multiple monitoring devices monitor m monitoring indicators, and the list to be optimized also includes the monitoring device identification; if at the first monitoring point, only the target difference of all monitoring indicators of the first device is less than the preset difference, then the device identification of the first device at the first monitoring point is added to the saving list.

[0012] Optionally, if the identifiers of some monitoring equipment in the saving list are all monitoring points, the monitoring equipment that meets the preset optimization conditions is selected as the optimized monitoring equipment; if the identifiers of some monitoring points in the saving list are all monitoring points, the monitoring points that meet the preset optimization conditions are selected as the optimized monitoring points.

[0013] Optionally, the two-sample t-test p-values ​​of n comparison data sets and the corresponding original data sets for each monitoring indicator of each monitoring point are determined respectively; for the first monitoring indicator, if the two-sample t-test p-values ​​of the first original data set and the first comparison data set are less than a preset threshold, it is determined that the difference between the first original data set and the first comparison data set is greater than the preset difference; if the two-sample t-test p-values ​​of the first original data set and the first comparison data set are greater than or equal to the preset threshold, it is determined that the difference between the first original data set and the first comparison data set is less than or equal to the preset difference; wherein, the first original data set is the original data set of the first monitoring indicator; and the first comparison data set is the comparison data set of the first monitoring indicator of the first monitoring point.

[0014] Optionally, after traversing the m monitoring indicators of n monitoring points, if the identifiers of some monitoring equipment in the saving list are all monitoring points, the average double-sample t-test p-value of the monitoring indicators of all monitoring equipment to be optimized in the saving list is calculated; the monitoring equipment at the monitoring point with the largest average double-sample t-test p-value is determined as the monitoring equipment for this round of optimization.

[0015] Optionally, after traversing the m monitoring indicators of n monitoring points, if the identifiers of all monitoring points are in the saving list, the average double-sample t-test p-value of the monitoring indicators of all monitoring points to be optimized in the saving list is calculated; the monitoring point with the largest average double-sample t-test p-value is determined as the monitoring point for this round of optimization.

[0016] According to a second aspect of an embodiment of the present disclosure, a device for optimizing monitoring points of an ecological environment in a photovoltaic power station is provided, and the device for optimizing monitoring points of an ecological environment in a photovoltaic power station includes: a generation module, a determination module, a difference comparison module, an identification adding module and a selection module; the generation module is used to generate m original data sets based on the monitoring data of n monitoring points that monitor m monitoring indicators, and to generate a list to be optimized according to the identifications of the n monitoring points, one monitoring indicator corresponds to one original data set, m is a positive integer, and n is an integer greater than 1; the determination module is used to start traversing the monitoring points according to the list to be optimized, and when traversing to the first monitoring point, start traversing the monitoring indicators according to the m monitoring indicators, and when traversing to the first monitoring indicator, add the The monitoring data of the first monitoring indicator of the other n-1 monitoring points are determined as the comparison data set, each monitoring indicator corresponds to n comparison data sets, and one comparison data set corresponds to one monitoring point; the first monitoring point is any one of the n monitoring points, and the first monitoring indicator is any one of the m monitoring indicators; a difference comparison module is used to determine the target difference between the n comparison data sets and the corresponding original data sets of each monitoring indicator after traversing the m monitoring indicators of the n monitoring points; an identification adding module is used to add the identification of the target object that meets the preset difference condition to the savable list; a selection module is used to select the target object that meets the preset optimization condition from the savable list as the optimization object of this round.

[0017] Optionally, the identification adding module is used to delete the identification of the target object to be optimized in the list to be optimized after the current round of optimization is completed; the determination module is also used to restart traversing each monitoring indicator of each monitoring point according to the list to be optimized after deleting the identification of the target object to be optimized, and determine the comparison data set for each monitoring indicator of each monitoring point.

[0018] Optionally, a monitoring point includes only one monitoring device, and one monitoring device monitors m monitoring indicators; the identification adding module is specifically used to add the identification of the first monitoring point to the saving list if the target difference of the m monitoring indicators of the first monitoring point meets the preset difference condition.

[0019] Optionally, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one of m monitoring indicators, and multiple monitoring devices monitor m monitoring indicators; an identification adding module is specifically used to add the point identification of the first monitoring point to the saving list if the target difference of all monitoring indicators of all monitoring devices at the first monitoring point is less than a preset difference.

[0020] Optionally, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one monitoring indicator, multiple monitoring devices monitor m monitoring indicators, and the list to be optimized also includes monitoring device identification; an identification adding module is specifically used to add the device identification of the first device at the first monitoring point to the saving list if the target difference of all monitoring indicators of only the first device at the first monitoring point is less than the preset difference.

[0021] Optionally, a selection module is specifically used to select monitoring equipment that meets preset optimization conditions as optimized monitoring equipment if all the identifications of some monitoring equipment in the saving list are monitoring points; if all the identifications of some monitoring equipment in the saving list are monitoring points, then monitoring points that meet preset optimization conditions are selected as optimized monitoring points.

[0022] Optionally, the difference comparison module is specifically used to determine the two-sample t-test p-value of n comparison data sets and the corresponding original data sets for each monitoring indicator of each monitoring point; for the first monitoring indicator, if the two-sample t-test p-value of the first original data set and the first comparison data set is less than a preset threshold, it is determined that the difference between the first original data set and the first comparison data set is greater than the preset difference; if the two-sample t-test p-value of the first original data set and the first comparison data set is greater than or equal to the preset threshold, it is determined that the difference between the first original data set and the first comparison data set is less than or equal to the preset difference; wherein, the first original data set is the original data set of the first monitoring indicator; and the first comparison data set is the comparison data set of the first monitoring indicator of the first monitoring point.

[0023] Optionally, a selection module is specifically used to calculate the average double-sample t-test p-value of the monitoring indicators of all monitoring equipment to be optimized in the saving list after traversal of m monitoring indicators of n monitoring points is completed. If the identifications of some monitoring equipment in the saving list are all monitoring points, the monitoring equipment at the monitoring point with the largest average double-sample t-test p-value is determined as the monitoring equipment for this round of optimization.

[0024] Optionally, a selection module is specifically used to calculate the average double-sample t-test p-value of the monitoring indicators of all monitoring points to be optimized in the saving list after traversal of the m monitoring indicators of n monitoring points is completed. The monitoring point with the largest average double-sample t-test p-value is determined as the monitoring point to be optimized in this round.

[0025] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for optimizing the monitoring points of the ecological environment of a photovoltaic power station as described in the first aspect is implemented.

[0026] According to a fourth aspect of an embodiment of the present disclosure, a computer device, a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor are provided, wherein the computer-readable instructions, when executed by the processor, implement the photovoltaic power station ecological environment monitoring point optimization method as described in the first aspect.

[0027] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In the disclosed embodiment, for n monitoring points that monitor m monitoring indicators, the historical monitoring data of the m monitoring indicators of the n monitoring points can be used as the original data set of each indicator, and then each monitoring indicator of the n monitoring points can be traversed. For each monitoring point, the monitoring data of the monitoring point in the original data set is removed to obtain a comparison data set, and the difference between each comparison data set and the original data set is compared. If the difference is not significant, it means that the data removal of the point has little effect on the monitoring representativeness of the data, so that the monitoring point can be optimized, and a list of savings can be obtained based on the difference determined for each monitoring point, and then the monitoring point or monitoring equipment with the smallest monitoring representativeness effect is selected from the list as the object of this optimization. When the monitoring instrument needs to be updated, its site selection is optimized, and the representativeness of its monitoring can be guaranteed while reducing the site, reducing the procurement, construction and maintenance costs of the ecological environment monitoring equipment, and improving the comprehensive benefits of the photovoltaic power station. By gradually selecting the optimized monitoring nodes, the number of ecological environment monitoring points can be gradually reduced during its operation without losing the representativeness of the monitoring, so as to reduce the cost of monitoring its ecological environment impact.

[0028] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0030] Figure 1 A schematic diagram of the system architecture of a photovoltaic power plant ecological environment monitoring point optimization method provided in an embodiment of the present disclosure.

[0031] Figure 2 A schematic flow chart of a method for optimizing monitoring points of an ecological environment in a photovoltaic power station provided in an embodiment of the present disclosure.

[0032] Figure 3 A schematic diagram of an optimization process for ecological environment monitoring points with multiple monitoring points and multiple factors provided in an embodiment of the present disclosure.

[0033] Figure 4A schematic diagram of a soil temperature index optimization process for a soil temperature and humidity monitoring network point provided in an embodiment of the present disclosure.

[0034] Figure 5 A schematic diagram of a soil moisture index optimization process for a soil temperature and humidity monitoring network point provided in an embodiment of the present disclosure.

[0035] Figure 6 A schematic diagram of monitoring points after point optimization provided in an embodiment of the present disclosure.

[0036] Figure 7 This is a hardware structure diagram of the computer equipment where the photovoltaic power station ecological environment monitoring point optimization device is located in the embodiment of the present disclosure.

[0037] Figure 8 A schematic diagram of the structure of a photovoltaic power station ecological environment monitoring point optimization device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0039] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0040] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0041] Next, the embodiments of the present disclosure are described in detail.

[0042] Figure 1 A schematic diagram of the system architecture of a photovoltaic power plant ecological environment monitoring point optimization method provided in an embodiment of the present disclosure.

[0043] like Figure 1 As shown, the system architecture 100 may include one or more of terminal devices such as a smart phone 101, a portable computer 102, a desktop computer 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0044] The terminal device can be any electronic device with data processing function, which has a display screen for showing the user the location of each ecological environment monitoring point in the photovoltaic power station and the location of the monitoring point that can be removed after optimization, etc. The electronic device includes but is not limited to the above-mentioned desktop computer, portable computer, smart phone and tablet computer, etc.

[0045] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0046] The method provided in the embodiment of the present disclosure may be executed by a terminal device, and accordingly, the device may be set in the terminal device. However, it is easy for a person skilled in the art to understand that the method provided in the embodiment of the present disclosure may also be executed by a server 105, and accordingly, the device may also be set in the server 105, which is not particularly limited in this exemplary embodiment.

[0047] The disclosed embodiment is aimed at n monitoring points that monitor m monitoring indicators. The historical monitoring data of the m monitoring indicators of the n monitoring points can be used as the original data set of each indicator. Then, each monitoring indicator of the n monitoring points can be traversed. For each monitoring point, the monitoring data of the monitoring point in the original data set is removed to obtain a comparison data set. The difference between each comparison data set and the original data set is compared. If the difference is not significant, it means that the data removal of the point has little effect on the representativeness of the data monitoring, so that the monitoring point can be optimized. Based on the difference determined by each monitoring point, a list of savings can be obtained, and then the monitoring point or monitoring equipment with the smallest representativeness of monitoring is selected from the list as the object of this optimization. When the monitoring instrument needs to be updated, its site selection is optimized, and the representativeness of its monitoring can be guaranteed while reducing the site distribution, reducing the procurement, construction and maintenance costs of the ecological environment monitoring equipment, and improving the comprehensive benefits of the photovoltaic power station. By gradually selecting the optimized monitoring nodes, the number of ecological environment monitoring points can be gradually reduced during its operation without losing the representativeness of the monitoring, so as to reduce the cost of monitoring its ecological environment impact.

[0048] like Figure 2As shown, Figure 2 A schematic flow chart of a photovoltaic power station ecological environment monitoring point optimization method provided by an embodiment of the present disclosure includes the following S201 to S205: S201. Generate m original data sets based on monitoring data of n monitoring points that monitor m monitoring indicators, and generate a list to be optimized according to identifiers of the n monitoring points.

[0049] Among them, one monitoring indicator corresponds to one original data set, m is a positive integer, and n is an integer greater than 1.

[0050] It should be noted that, in the embodiment of the present disclosure, when optimizing monitoring points based on the monitoring data of the first monitoring indicator, the first original data set for difference comparison is the historical data of the first monitoring indicator for all monitoring points monitoring the first monitoring indicator. That is, the first original data set remains unchanged.

[0051] For example, there are 10 monitoring points that monitor indicator one, and the historical monitoring data sets of indicator one of the 10 monitoring points are combined into original data set one, and the original data set one remains unchanged in the step of optimizing based on the original data set one.

[0052] It should be noted that, in the embodiment of the present disclosure, before optimizing the monitoring points, the following data preprocessing may be performed on the monitored historical data, and the original data set in 201 is a set of data after data preprocessing.

[0053] (1) Data cleaning processing: The photovoltaic power station ecological environment monitoring point optimization device downloads historical monitoring data from the data storage card of the ecological environment monitoring device. The historical monitoring data includes multiple monitoring element data of multiple monitoring points. After reading in the historical monitoring data, the data cleaning begins. The total number of monitoring points is defined as n, and the total number of monitoring indicators is defined as m. If there is a monitoring point that lacks the monitoring data of the first indicator in the first period, the monitoring data of the first indicator in the first period of all monitoring points that monitor the first indicator among the n monitoring points are deleted.

[0054] It should be noted that in the embodiments of the present disclosure, horizontal comparison between monitoring indicators is not involved. Therefore, when cleaning data, if missing data is cleaned for the first indicator, it is not necessary to delete the data of other indicators in the first period except the first indicator.

[0055] (2) Data synthesis and normalization processing: It should be noted that the monitoring data time frequency of the ecological environment monitoring equipment is 10-30 minutes. According to the method of evaluating the difference between two sets of data selected by the optimization equipment, data synthesis processing can be performed, for example, it can include hourly synthesis, daily synthesis, monthly synthesis and other processing. Datasets.

[0056] S202. Start traversing the monitoring points according to the list to be optimized. When traversing to the first monitoring point, start traversing the monitoring indicators according to the m monitoring indicators. When traversing to the first monitoring indicator, determine the monitoring data of the first monitoring indicator of the other n-1 monitoring points as the comparison data set.

[0057] Each monitoring indicator corresponds to n comparison data sets, one comparison data set corresponds to one monitoring point, the first monitoring point is any one of the n monitoring points, and the first monitoring indicator is any one of the m monitoring indicators.

[0058] S203 . After traversing the m monitoring indicators of the n monitoring points, respectively determine the target difference between the n comparison data sets of each monitoring indicator and the corresponding original data set.

[0059] S204: Add the identifiers of the target objects that meet the preset difference condition to the saving list.

[0060] The target object is a monitoring point or a monitoring device in the monitoring point.

[0061] It should be noted that the preset difference condition is used to indicate that there are significant differences between the comparison data set after the monitoring data of the target object is removed and the original data set.

[0062] Specifically, a difference monitoring algorithm may be used to monitor the original data set and the comparison data set, such as a two-sample t-test, a Mann-Whitney U test, and the like.

[0063] It should be noted that, since the sample sizes of the original data set and the comparison data set in the embodiment of the present disclosure are not equal, conventional methods such as linear fitting cannot be used for monitoring when selecting a difference monitoring algorithm.

[0064] Exemplarily, it is assumed that there are n monitoring points for monitoring indicator one, namely monitoring point one to monitoring point n. The n groups of indicator data from monitoring point one to monitoring point n are determined as the original data set of indicator one, and the monitoring points are traversed. The monitoring data of indicator one of monitoring point one in the original data set of indicator one is deleted to generate a comparison data set one; the monitoring data of indicator one of monitoring point two in the original data set of indicator one is deleted to generate a comparison data set two; the monitoring data of indicator one of monitoring point n in the original data set of indicator one is deleted to generate a comparison data set n, thereby obtaining n comparison data sets. Determine the target difference one, target difference two, ..., target difference n of the original data set of indicator one and the n comparison data sets respectively. If the target difference one and the target difference two meet the preset difference conditions, the identifiers of monitoring point one and monitoring point two are added to the saving list.

[0065] S205: Select a target object that meets a preset optimization condition from the savable list and determine it as the optimization object for this round.

[0066] It should be noted that in the embodiment of the present disclosure, if the target object is a monitoring point, only one monitoring point is selected for optimization each time; if the target object is a monitoring device in a monitoring point, only one monitoring device is selected for optimization each time.

[0067] It can be understood that in the present disclosure, the monitoring point data is gradually reduced, and the reduced monitoring point data is compared with the original number of monitoring point data to see if there is any obvious gap. If there is no obvious gap, reducing the monitoring points will not affect the representativeness of the monitoring, and the monitoring points can be optimized.

[0068] The disclosed embodiment provides a method for optimizing the monitoring points of the ecological environment of a photovoltaic power station. For n monitoring points that monitor m monitoring indicators, the historical monitoring data of the m monitoring indicators of the n monitoring points can be used as the original data set of each indicator, and then each monitoring indicator of the n monitoring points can be traversed. For each monitoring point, the monitoring data of the monitoring point in the original data set is removed to obtain a comparison data set, and the difference between each comparison data set and the original data set is compared. If the difference is not significant, it means that the data removal of the point has little effect on the representativeness of the data monitoring, so that the monitoring point can be optimized. Based on the difference determined by each monitoring point, a list of savings can be obtained, and then the monitoring point or monitoring equipment with the smallest representativeness of monitoring is selected from the list as the object of this optimization. When the monitoring instrument needs to be updated, its site selection is optimized, and the representativeness of its monitoring can be guaranteed while reducing the site layout, reducing the procurement, construction and maintenance costs of the ecological environment monitoring equipment, and improving the comprehensive benefits of the photovoltaic power station. By gradually selecting the optimized monitoring nodes, the number of ecological environment monitoring points can be gradually reduced during its operation without losing the representativeness of the monitoring, so as to reduce the cost of monitoring its ecological environment impact.

[0069] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided in the embodiment of the present disclosure, the method further includes the following S206 and S207: S206: After the current round of optimization is completed, the identifier of the target object to be optimized in the list to be optimized is deleted.

[0070] S207 , re-traversing each monitoring indicator of each monitoring point according to the list to be optimized after deleting the identifier of the optimized target object, and determining a comparison data set of each monitoring indicator of each monitoring point.

[0071] After the comparison data set is determined, the optimization target object is selected according to the above processing method of S203-S205.

[0072] Exemplarily, if the monitoring points are optimized in the first round, the identifiers of the optimized monitoring points in the saving list are deleted, and then in the second round of optimization, the differences in the data sets are judged based on the comparison set data determined based on the monitoring data of the first monitoring indicator of the remaining n-1 monitoring nodes and the original data set (i.e., the original data set of the first monitoring indicator in the above-mentioned m original data sets).

[0073] After optimizing the monitoring data of n monitoring points, delete the identifier of the optimized monitoring point in the saving list, traverse the monitoring data of the remaining n-1 monitoring points, repeat the above steps, and obtain new optimized monitoring points; traverse the monitoring data of the remaining n-2 monitoring points, and repeat the above steps until the length of the saving list is 0, and the optimized monitoring point cannot be selected, and the monitoring point optimization process ends. At this time, the length of the optimized monitoring point list is the number of points that can be saved for such ecological environment monitoring instruments in the photovoltaic power station, and the point number is the number of points that can be saved.

[0074] Based on this scheme, for a monitoring indicator, we can gradually determine the nodes that can be optimized, gradually reduce the monitoring point data, and compare whether there is a significant gap between the reduced monitoring point data and the original number of monitoring point data. If there is no obvious gap, reducing the monitoring points will not affect the representativeness of monitoring, and the monitoring points can be optimized. Then, based on the same monitoring indicator, we can determine the next optimizable monitoring point until the first monitoring indicator can no longer be optimized, and all points that do not affect the representativeness of monitoring can be accurately determined.

[0075] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided in the embodiment of the present disclosure, one monitoring point includes only one monitoring device, and one monitoring device monitors m monitoring indicators, and then the above S204 can be specifically performed by the following S204a: S204a: If the target difference of the m monitoring indicators of the first monitoring point meets the preset difference, the identifier of the first monitoring point is added to the saving list.

[0076] It can be understood that in actual applications, each monitoring point of the ecological environment of a photovoltaic power station can monitor only one monitoring indicator, or multiple monitoring indicators can be monitored at one monitoring point. For monitoring points that can monitor multiple monitoring indicators, each monitoring indicator is processed in accordance with the above-mentioned processing method, and the target differences between the original data set corresponding to each monitoring indicator and each comparison data set are compared. If the target differences of all monitoring indicators at the monitoring point meet the preset differences, the monitoring point can be added to the saving list.

[0077] For example, monitoring points 1 to 99 can monitor both indicators 2 and 3 at the same time (and do not monitor other indicators). When optimizing monitoring points 1 to 99, if the target difference between the original data set and each comparison data set corresponding to indicator 2 in monitoring point 98 and the target difference between the original data set and each comparison data set corresponding to indicator 3 meet the preset difference, then monitoring point 98 can be added to the savings list.

[0078] Based on this scheme, for monitoring points that can monitor multiple indicators, monitoring point optimization processing can also be performed on such monitoring points. It is necessary to determine the target difference between the original data set and the comparison data set of each indicator in the multiple indicators. When the target difference of each indicator at the first monitoring point meets the preset difference, the first monitoring point can be added to the saving list to facilitate the subsequent selection of monitoring points for optimization of the multiple indicators.

[0079] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided by the embodiment of the present disclosure, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one monitoring indicator among m monitoring indicators, and the multiple monitoring devices monitor m monitoring indicators; furthermore, the above S204 can also be performed by the following S204b: S204b: If the target difference of all monitoring indicators of all monitoring devices at the first monitoring point is less than the preset difference, the point identifier of the first monitoring point is added to the saving list.

[0080] It can be understood that in actual applications, multiple monitoring devices can be deployed at the same monitoring point.

[0081] Based on this solution, for monitoring points of multiple types of monitoring equipment, the target differences corresponding to all indicators monitored by each monitoring equipment at the monitoring point need to meet the preset differences before the monitoring point can be determined as an alternative optimized monitoring point. This can avoid directly optimizing the entire monitoring point based on one device or one parameter, which affects the representativeness of other monitoring indicators.

[0082] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided by the embodiment of the present disclosure, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one monitoring indicator, multiple monitoring devices monitor m monitoring indicators, and the list to be optimized also includes a monitoring device identifier, and then the above S204 may include the following S204c: S204c: If the target difference of all monitoring indicators of only the first device at the first monitoring point is smaller than the preset difference, then the device identifier of the first device at the first monitoring point is added to the saving list.

[0083] Exemplarily, the photovoltaic power station ecological environment monitoring point optimization device can add the identifier of the first monitoring point and the identifier of the first device to the savings list.

[0084] Based on this solution, when optimizing the monitoring points of multiple devices, if the target differences of all monitoring indicators of the first device meet the preset differences, only the first device in this type of monitoring point can be optimized. That is, the first device in the monitoring point can no longer be used for monitoring without affecting the representativeness of the monitoring data. The setting of monitoring equipment can be reduced, thereby saving monitoring resources and making the optimization method more flexible.

[0085] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided by the embodiment of the present disclosure, S205 may specifically include the following S205a1 or S205a2: S205a1. If the identifiers of some monitoring devices in the saving list are all monitoring points, then a monitoring device that meets a preset optimization condition is selected as the optimized monitoring device.

[0086] S205a2: If all the identifiers in the saveable list are monitoring point identifiers, a monitoring point that meets a preset optimization condition is selected as an optimized monitoring point.

[0087] Based on this scheme, if there are multiple monitoring devices in the monitoring point to monitor m monitoring indicators, there may be two situations in the above optimization steps. One situation is that the optimization of some monitoring devices at the monitoring point does not affect the representativeness of the monitoring data. The other situation is that the entire monitoring point is optimized without affecting the representativeness of the monitoring data. Therefore, the optimization object can be selected according to the actual situation, making the optimization more flexible.

[0088] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided by the embodiment of the present disclosure, the above S203 may specifically include the following S203a and S203b, or S203a and S203c: S203a, respectively determining the two-sample t-test p-value between the n comparison data sets of each monitoring indicator at each monitoring point and the corresponding original data set.

[0089] S203b: For the first monitoring indicator, if the two-sample t-test p-value of the first original data set and the first comparative data set is less than a preset threshold, it is determined that the difference between the first original data set and the first comparative data set is greater than a preset difference.

[0090] The first original data set is an original data set of the first monitoring indicator; and the first comparative data set is a comparative data set of the first monitoring indicator at the first monitoring point.

[0091] S203c: for the first monitoring indicator, if the two-sample t-test p-values ​​of the first original data set and the first comparative data set are greater than or equal to a preset threshold, it is determined that the difference between the first original data set and the first comparative data set is less than a preset difference.

[0092] For example, in the embodiment of the present disclosure, the two-sample t-test is used to determine whether there is a significant difference between the two groups of samples (the original data set and the comparison data set). Both the original data set and the comparison data set obey a normal distribution and have equal variances. First, the null hypothesis ( ): There is no difference in the means of the two populations, that is, ; Alternative hypothesis ( ): The means of the two populations are different, that is, Then, calculate the test t value as follows: .in, and is the mean of the two samples, and is the variance of the two samples, and is the size of the two samples. Finally, according to its degrees of freedom The p-value is obtained by looking up the table with the t-value. Usually p=0.05 is used as the critical point. When p<0.05, if the null hypothesis is true, the probability of observing the current sample data is less than 5%, so the null hypothesis is rejected. At this time, the two samples are significantly different. When p>0.05, if the null hypothesis is true, the probability of observing the current sample data is greater than 5%, and there is not enough evidence to reject the null hypothesis. At this time, there is no significant difference between the two samples. In some scenarios with more stringent requirements, the critical point can also be selected as p=0.01 or p=0.001.

[0093] Exemplarily, the p-value of the two-sample t-test of the m monitoring indicators of each of the n monitoring points is monitored. If there is a monitoring indicator with p<0.05 in a certain monitoring point, it means that if the monitoring point is removed, the monitoring values ​​of the n-1 monitoring points of the indicator will be significantly different from the monitoring values ​​of all n monitoring points, and the monitoring point cannot be selected in this round of monitoring point optimization; if the p>0.05 of the two-sample t-test of all m monitoring indicators of a certain monitoring point, then the monitoring point can be determined as a candidate for optimized monitoring point in this round and added to the list of savings.

[0094] Based on this scheme, a two-sample t-test method can be selected to determine whether the difference between the two data sets corresponding to the monitoring indicators of each monitoring point meets the preset difference by determining whether the two-sample t-test p value is less than the preset threshold. In this way, it can be determined whether removing the monitoring point affects the representativeness of the monitoring data.

[0095] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided by the embodiment of the present disclosure, the above S205 can be performed by S205b1 and S205b2: S205b1. After the traversal of m monitoring indicators of n monitoring points is completed, if the identifiers of some monitoring equipment in the saving list are all monitoring points, the average p-value of the double-sample t test of the monitoring indicators of all the monitoring equipment to be optimized in the saving list is calculated.

[0096] S205b2. Determine the monitoring equipment at the monitoring point with the largest average p-value of the two-sample t-test as the monitoring equipment for this round of optimization.

[0097] Specifically, if the length of the list that can be saved is 0, it means that no monitoring point meets the optimization requirements, and all traversal algorithms are skipped, and the monitoring point optimization process ends; if its length is not 0, the average value of the double sample t test p value of all monitoring indicators of these monitoring points is calculated (it can also be a weighted average, with the indicator importance as the weight). Finally, the monitoring point with the largest average value of the double sample t test p value in the list of candidate optimized monitoring points is selected as the monitoring point for this round of optimization. The largest average value of p value means that the difference between the monitoring value of the remaining monitoring points after optimizing the monitoring point and the monitoring value of the monitoring point before optimization is the smallest, and the monitoring point number is deleted from the list of monitoring points to be optimized, and the monitoring point number is added to the list of optimized monitoring points.

[0098] Based on this scheme, if the objects to be optimized screened out during this round of traversal are all monitoring points of multiple devices, and the differences in all indicator data of some devices meet the optimization conditions, then the average p-value of the two-sample t-test of all monitoring indicators can be determined for these devices, thereby screening out the equipment that can be optimized, and optimizing the points more flexibly.

[0099] Optionally, in the photovoltaic power station ecological environment monitoring point optimization method provided by the embodiment of the present disclosure, S205 can be performed by S205c1 and S205c2: S205c1. After the traversal of the m monitoring indicators of the n monitoring points is completed, if the identifiers of the monitoring points are all in the saving list, the average p-value of the double-sample t test of the monitoring indicators of all the monitoring points to be optimized in the saving list is calculated.

[0100] S205c2. The monitoring point with the largest average p-value of the two-sample t-test is determined as the monitoring point for this round of optimization.

[0101] It should be noted that in the embodiment of the present disclosure, one monitoring point is selected in each round of optimization.

[0102] Based on this scheme, if all the monitoring points in the saving list are monitoring points, the monitoring point with the largest average p value can be selected as the monitoring point for the final optimization in this round.

[0103] Figure 3 A schematic diagram of an optimization process for ecological environment monitoring points with multiple monitoring points and multiple factors provided in an embodiment of the present disclosure.

[0104] like Figure 3 As shown in , the processing flow is as follows: obtain multi-point (n) multi-indicator monitoring data, synthesize the original n point data into data set 1, traverse sequentially in n points, and form data set 2 with the remaining n-1 point data except the current point, calculate the double sample t test of each indicator in the m monitoring indicators of data set 1 and the corresponding indicator of data set 2, and determine whether the t test p value of all indicators is greater than 0.05. If so, add the point to the list of points that can be saved, if not, return to traverse the next point again; if n points have not been traversed, return to traverse the next point again. If n points have been traversed, select the point with the largest t test p value of all elements in the list of points that can be saved as the optimized point. Continue traversing in the remaining n-1 points.

[0105] Example: The observation data of the soil temperature and humidity monitoring network were obtained from a photovoltaic power station in Gonghe County, Qinghai Province. The monitoring indicators include soil temperature and soil moisture content. The monitoring network includes 30 sets of soil temperature and humidity instruments, which are evenly distributed in the photovoltaic power station.

[0106] Monitoring point optimization was carried out for 30 soil temperature and humidity monitoring points. First, the soil temperature and moisture content data of the 30 monitoring points were preprocessed to remove records with non-numerical values ​​or abnormal values. If the non-numerical value and abnormal value records at a certain time of a certain monitoring point were deleted, the records of the corresponding time of all other monitoring points would be deleted to ensure data comparability.

[0107] The monitoring data sets are then combined into hourly averages.

[0108] Finally, the monitoring data of the same indicator at the same monitoring point are put into one data set, forming 30×2=60 data sets. The data of all 30 monitoring points are combined into data set 1, which remains unchanged in subsequent steps.

[0109] Next, the data of the 30 monitoring points are traversed, and when a certain monitoring point is traversed, the data sets of the remaining 29 monitoring points are removed to form the second data set.

[0110] Calculate the p-value of the t-test of soil temperature and water content between data set 1 and data set 2. If the p-values ​​of the two indicators of the monitoring point are both greater than 0.05, then add the monitoring point to the list of optimized monitoring points.

[0111] After the traversal, there is a monitoring point in the list of monitoring points that can be optimized. The p-value and the p-value of the t-test of soil temperature and water content of the monitoring point with the largest p-value are 0.94 and 0.06 respectively. Optimizing this monitoring point has the smallest impact on the entire monitoring network, so this monitoring point is optimized first and added to the list of optimized monitoring points.

[0112] Continue to traverse the remaining 29 monitoring points, select the second optimized monitoring point, and then traverse until no monitoring points can be selected whose p-values ​​of the t-test for soil temperature and water content between data set 1 and data set 2 are both greater than 0.05. At this time, deleting any monitoring point will cause the monitoring values ​​of the remaining monitoring points to be significantly different from the monitoring values ​​before optimization, and the optimization process ends.

[0113] The soil temperature and water content data after each monitoring point optimization are compared with the original data. Figure 4 ,and Figure 5 express. Figure 4 A schematic diagram of a soil temperature index optimization process for a soil temperature and humidity monitoring network point provided in an embodiment of the present disclosure. Figure 4 The figure shows the optimization process of soil temperature indicators at soil temperature and humidity monitoring points of a photovoltaic power station in Qinghai Province and its double-sample t-test p-value compared with all original points. The p-value of each step of the optimization process is marked on the bar chart. Figure 5A schematic diagram of the soil moisture index optimization process for a soil temperature and humidity monitoring network point provided in an embodiment of the present disclosure. The soil moisture index optimization process for a certain photovoltaic power station soil temperature and humidity monitoring network point in Qinghai Province and its double-sample t-test p-value with all original points, and the p-value of each step of the optimization process are marked on the bar chart. Among them, based on the historical data of 30 soil temperature and humidity monitoring networks in a certain photovoltaic power station in Qinghai Province, this method can be used to optimize 5 monitoring points, saving 16.7% of monitoring points. Spatially drawing the monitoring points in the list of optimized monitoring points and the monitoring points that need to be retained can guide the layout of monitoring points for subsequent batch monitoring, such as Figure 6 shown. Figure 6 A schematic diagram of monitoring points after optimization is provided in an embodiment of the present disclosure. These optimized monitoring points reduce the cost of purchasing, deploying and later maintenance of power station instruments while ensuring the representativeness of monitoring, thereby improving their benefits.

[0114] Corresponding to the embodiments of the aforementioned methods, the present disclosure also provides embodiments of a photovoltaic power station ecological environment monitoring point optimization device and a computer device used therein.

[0115] The embodiments of the device disclosed herein can be applied to computer devices, such as servers or terminal devices. The device embodiments can be implemented through software, hardware, or a combination of software and hardware. Taking software implementation as an example, a photovoltaic power station ecological environment monitoring point optimization device in a logical sense is determined by the processor of the photovoltaic power station ecological environment monitoring point optimization in which it is located reading the corresponding computer program instructions in the non-volatile memory into the memory and running them. From the hardware level, if Figure 7 As shown, Figure 7 This is a hardware structure diagram of the computer equipment where the photovoltaic power station ecological environment monitoring point optimization device is located in the embodiment of the disclosure, except Figure 7 In addition to the processor 710, memory 730, network interface 720, and non-volatile memory 740 shown, the server or electronic device where the photovoltaic power station ecological environment monitoring point optimization method is located in the embodiment may also include other hardware, usually according to the actual function of the computer device, which will not be described in detail.

[0116] like Figure 8 As shown, Figure 8A schematic diagram of the structure of a photovoltaic power station ecological environment monitoring point optimization device provided by an embodiment of the present disclosure, the photovoltaic power station ecological environment monitoring point optimization device 800 includes: a generation module 801, a determination module 802, a difference comparison module 803, an identification adding module 804 and a selection module 805; the generation module 801 is used to generate m original data sets based on the monitoring data of n monitoring points that monitor m monitoring indicators, and generate a list to be optimized according to the identifications of the n monitoring points, one monitoring indicator corresponds to one original data set, m is a positive integer, and n is an integer greater than 1; the determination module 802 is used to start traversing the monitoring points according to the list to be optimized, and when traversing to the first monitoring point, start traversing the monitoring indicators according to the m monitoring indicators, and when traversing to the first monitoring point, start traversing the monitoring indicators according to the m monitoring indicators, When there is one monitoring indicator, the monitoring data of the first monitoring indicator of the other n-1 monitoring points are determined as the comparison data set, each monitoring indicator corresponds to n comparison data sets, and one comparison data set corresponds to one monitoring point; the first monitoring point is any one of the n monitoring points, and the first monitoring indicator is any one of the m monitoring indicators; the difference comparison module 803 is used to determine the target difference between the n comparison data sets and the corresponding original data sets of each monitoring indicator after traversing the m monitoring indicators of the n monitoring points; the identification adding module 804 is used to add the identification of the target object that meets the preset difference condition to the savable list; the selection module 805 is used to select the target object that meets the preset optimization condition from the savable list as the optimization object of this round.

[0117] Optionally, the identification adding module 804 is used to delete the identification of the target object to be optimized in the list to be optimized after the current round of optimization is completed; the determination module 802 is also used to restart traversing each monitoring indicator of each monitoring point according to the list to be optimized after deleting the identification of the target object to be optimized, and determine the comparison data set of each monitoring indicator of each monitoring point.

[0118] Optionally, a monitoring point includes only one monitoring device, and one monitoring device monitors m monitoring indicators; the identification adding module 804 is specifically used to add the identification of the first monitoring point to the saving list if the target difference of the m monitoring indicators of the first monitoring point meets the preset difference condition.

[0119] Optionally, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one of m monitoring indicators, and multiple monitoring devices monitor m monitoring indicators; the identification adding module 804 is specifically used to add the point identification of the first monitoring point to the saving list if the target difference of all monitoring indicators of all monitoring devices at the first monitoring point is less than the preset difference.

[0120] Optionally, a monitoring point includes multiple monitoring devices, each monitoring device monitors at least one monitoring indicator, multiple monitoring devices monitor m monitoring indicators, and the list to be optimized also includes monitoring device identification; the identification adding module 804 is specifically used to add the device identification of the first device at the first monitoring point to the saving list if the target difference of all monitoring indicators of only the first device at the first monitoring point meets the preset difference condition.

[0121] Optionally, selection module 805 is specifically used to select monitoring equipment that meets preset optimization conditions as optimized monitoring equipment if all the identifications of some monitoring equipment in the saving list are monitoring points; if all the identifications of some monitoring equipment in the saving list are monitoring points, then select monitoring points that meet preset optimization conditions as optimized monitoring points.

[0122] Optionally, the difference comparison module 803 is specifically used to determine the two-sample t-test p-value of n comparison data sets and the corresponding original data sets for each monitoring indicator of each monitoring point; for the first monitoring indicator, if the two-sample t-test p-value of the first original data set and the first comparison data set is less than a preset threshold, it is determined that the difference between the first original data set and the first comparison data set is greater than the preset difference; if the two-sample t-test p-value of the first original data set and the first comparison data set is greater than or equal to the preset threshold, it is determined that the difference between the first original data set and the first comparison data set is less than or equal to the preset difference; wherein, the first original data set is the original data set of the first monitoring indicator; and the first comparison data set is the comparison data set of the first monitoring indicator of the first monitoring point.

[0123] Optionally, module 805 is selected to be specifically used for calculating the average double-sample t-test p-value of the monitoring indicators of all monitoring equipment to be optimized in the saving list after traversal of m monitoring indicators of n monitoring points is completed, if the identifiers of some monitoring equipment in the saving list are all monitoring points; and determining the monitoring equipment of the monitoring point with the largest average double-sample t-test p-value as the monitoring equipment for this round of optimization.

[0124] Optionally, module 805 is selected to be specifically used for calculating the average double-sample t-test p-value of the monitoring indicators of all monitoring points to be optimized in the saving list after traversal of the m monitoring indicators of n monitoring points is completed, if the identifiers of all monitoring points are in the saving list; and determining the monitoring point with the largest average double-sample t-test p-value as the monitoring point to be optimized in this round.

[0125] The photovoltaic power station ecological environment monitoring point optimization device provided by the embodiment of the present disclosure, for n monitoring points that monitor m monitoring indicators, can be based on the historical monitoring data of the m monitoring indicators of the n monitoring points as the original data set of each indicator, and then each monitoring indicator of the n monitoring points can be traversed, for each monitoring point, the monitoring data of the monitoring point in the original data set is removed to obtain a comparison data set, and the difference between each comparison data set and the original data set is compared. If the difference is not significant, it means that the data removal of the point has little effect on the monitoring representativeness of the data, so that the monitoring point can be optimized, and a list of savings can be obtained based on the difference determined for each monitoring point, and then the monitoring point or monitoring equipment with the smallest monitoring representativeness effect is selected from the list as the object of this optimization. When the monitoring instrument needs to be updated, its site selection is optimized, and the representativeness of its monitoring can be guaranteed while reducing the site layout, reducing the procurement, construction and maintenance costs of the ecological environment monitoring equipment, and improving the comprehensive benefits of the photovoltaic power station. By gradually selecting the optimized monitoring nodes, the number of ecological environment monitoring points can be gradually reduced during its operation without losing the representativeness of the monitoring, thereby reducing the cost of monitoring its ecological environment impact.

[0126] Correspondingly, the present disclosure also provides a photovoltaic power station ecological environment monitoring point optimization device, the device includes a processor; a memory for storing processor executable instructions; wherein the processor is configured to: generate a first original data set based on the monitoring data of the first monitoring indicator of n monitoring points, generate a list to be optimized according to the identifiers of the n monitoring points, the monitoring indicators of the n monitoring points are the same, and n is an integer greater than 1; start traversal according to the list to be optimized, and when traversing to the first monitoring point, determine the monitoring data of the first monitoring indicators of the remaining other n-1 monitoring points as the i-th comparison data set, i is a positive integer less than or equal to n; after traversing the first monitoring indicators of the n monitoring points, respectively determine the target difference between the first original data set and each comparison data set; after determining the target difference between the original data set of each monitoring indicator of the n monitoring points and the corresponding each comparison data set, add the identifier of the monitoring point that meets the preset difference condition to the saving list; select the monitoring point that meets the preset optimization condition from the saving list to determine it as the optimized monitoring point.

[0127] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the various steps in the above-mentioned photovoltaic power station ecological environment monitoring point optimization method embodiment are implemented.

[0128] The present disclosure also provides a computer device, which includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the computer-readable instructions, when executed by the processor, implement the various steps in the above-mentioned photovoltaic power station ecological environment monitoring point optimization method embodiment.

[0129] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0130] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the modules determined as separated components may or may not be physically separated, and the components determined as module displays may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed scheme. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0131] The above describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0132] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the inventions claimed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not claimed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0133] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0134] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A method for optimizing the monitoring points of the ecological environment of a photovoltaic power station, characterized in that: The method comprises: Based on the monitoring data of n monitoring points that monitor m monitoring indicators, m original data sets are generated, and a list to be optimized is generated according to the identifiers of the n monitoring points, where one monitoring indicator corresponds to one original data set, m is a positive integer, and n is an integer greater than 1; Start traversing the monitoring points according to the list to be optimized, and when traversing to the first monitoring point, start traversing the monitoring indicators according to the m monitoring indicators, and when traversing to the first monitoring indicator, determine the monitoring data of the first monitoring indicator of other n-1 monitoring points as a comparison data set, each monitoring indicator corresponds to n comparison data sets, and one comparison data set corresponds to one monitoring point; the first monitoring point is any one of the n monitoring points, and the first monitoring indicator is any one of the m monitoring indicators; After traversing the m monitoring indicators of the n monitoring points, respectively determining the target difference between the n comparison data sets of each monitoring indicator and the corresponding original data set; Adding the identification of the target object that meets the preset difference condition to the saving list, wherein the target object is a monitoring point or a monitoring device in the monitoring point; A target object that meets a preset optimization condition is selected from the savable list to be determined as an optimization object in this round.

2. The method according to claim 1, characterized in that The method further comprises: After the current round of optimization is completed, the identifier of the target object to be optimized in the list to be optimized is deleted; According to the list to be optimized after deleting the identifier of the optimized target object, each monitoring indicator of each monitoring point is traversed again to determine a comparison data set of each monitoring indicator of each monitoring point.

3. The method according to claim 1, characterized in that A monitoring point includes only one monitoring device, and the one monitoring device monitors the m monitoring indicators; and adding the identifier of the target object that meets the preset difference condition to the saving list includes: If the target difference of the m monitoring indicators of the first monitoring point meets the preset difference condition, the identifier of the first monitoring point is added to the saving list.

4. The method according to claim 1, characterized in that A monitoring point includes multiple monitoring devices, each monitoring device monitors at least one of the m monitoring indicators, and the multiple monitoring devices monitor the m monitoring indicators; adding the identifier of the target object that meets the preset difference condition to the saving list includes: If the target difference of all monitoring indicators of all monitoring devices at the first monitoring point is less than the preset difference, the point identifier of the first monitoring point is added to the saving list.

5. The method according to claim 1, characterized in that A monitoring point includes multiple monitoring devices, each monitoring device monitors at least one monitoring indicator, the multiple monitoring devices monitor the m monitoring indicators, and the list to be optimized also includes monitoring device identifiers; adding the identifiers of target objects that meet the preset difference conditions to the saving list includes: If only the target difference of all monitoring indicators of the first device at the first monitoring point is smaller than the preset difference, the device identifier of the first device at the first monitoring point is added to the saving list.

6. The method according to any one of claims 3 to 5, characterized in that The step of selecting a target object that meets a preset optimization condition from the thrifty list and determining it as an optimized monitoring point includes: If the list of possible savings contains identifiers of some monitoring equipment at the monitoring points, then a monitoring equipment that meets the preset optimization conditions is selected as the optimized monitoring equipment; If all the identifiers in the savable list are monitoring point identifiers, a monitoring point that meets the preset optimization conditions is selected as the optimized monitoring point.

7. The method according to claim 6, characterized in that After traversing the m monitoring indicators of the n monitoring points, respectively determining the target difference between the n comparison data sets of each monitoring indicator and the corresponding original data set includes: Determine the two-sample t-test p value of n comparison data sets and the corresponding original data sets for each monitoring indicator at each monitoring point; For the first monitoring indicator, if the two-sample t-test p-value of the first original data set and the first comparative data set is less than a preset threshold, it is determined that the difference between the first original data set and the first comparative data set is greater than a preset difference; if the two-sample t-test p-value of the first original data set and the first comparative data set is greater than or equal to the preset threshold, it is determined that the difference between the first original data set and the first comparative data set is less than or equal to the preset difference; The first original data set is an original data set of the first monitoring indicator; and the first comparative data set is a comparative data set of the first monitoring indicator of the first monitoring point.

8. The method according to claim 7, characterized in that If all the identifiers of some monitoring equipment at monitoring points are in the saveable list, then target objects meeting preset optimization conditions are selected as optimized monitoring equipment, including: After the traversal of the m monitoring indicators of the n monitoring points is completed, if the identifiers of some monitoring equipment in the saving list are all monitoring points, then the average p-value of the double-sample t test of the monitoring indicators of all the monitoring equipment to be optimized in the saving list is calculated; The monitoring equipment at the monitoring point with the largest average p-value of the two-sample t-test is determined as the monitoring equipment for this round of optimization.

9. The method according to claim 7, characterized in that: If all the identifiers in the savable list are monitoring point identifiers, then selecting a monitoring point that meets a preset optimization condition as an optimized monitoring point includes: After the traversal of the m monitoring indicators of the n monitoring points is completed, if the identifiers of the monitoring points are all in the saving list, then the average p-value of the double-sample t test of the monitoring indicators of all the monitoring points to be optimized in the saving list is calculated; The monitoring point with the largest average p-value of the two-sample t test is determined as the monitoring point for this round of optimization.

10. A photovoltaic power station ecological environment monitoring point optimization device, characterized in that: The photovoltaic power station ecological environment monitoring point optimization device comprises: a generation module, a determination module, a difference comparison module, a mark adding module and a selection module; The generating module is used to generate m original data sets based on the monitoring data of n monitoring points that monitor m monitoring indicators, and generate a list to be optimized according to the identifiers of the n monitoring points, one monitoring indicator corresponds to one original data set, m is a positive integer, and n is an integer greater than 1; The determination module is used to start traversing the monitoring points according to the list to be optimized, and when traversing to the first monitoring point, start traversing the monitoring indicators according to the m monitoring indicators, and when traversing to the first monitoring indicator, determine the monitoring data of the first monitoring indicator of other n-1 monitoring points as a comparison data set, each monitoring indicator corresponds to n comparison data sets, and one comparison data set corresponds to one monitoring point; the first monitoring point is any one of the n monitoring points, and the first monitoring indicator is any one of the m monitoring indicators; The difference comparison module is used to determine the target difference between the n comparison data sets and the corresponding original data sets of each monitoring indicator after traversing the m monitoring indicators of the n monitoring points; The identification adding module is used to add the identification of the target object that meets the preset difference condition to the saving list; The selection module is used to select a target object that meets a preset optimization condition from the savable list and determine it as an optimization object in this round.

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