Photovoltaic power station remote monitoring system based on Internet of Things

Abstract

The invention relates to the technical field of photovoltaic power stations, and discloses a photovoltaic power station remote monitoring system based on Internet of Things, a data acquisition module is used for deploying a plurality of remote monitoring points in a to-be-monitored area, and acquiring monitoring data of each remote monitoring point within a preset time; the data analysis module is used for analyzing the monitoring data and determining a target monitoring data chain, and the data calculation module is used for carrying out adjacent identification binding on every two adjacent monitoring data factors and calculating a data monitoring response value of the target monitoring data chain; and the abnormity alarm module is used for judging whether the to-be-monitored area is abnormal or not based on the data monitoring response value, if so, giving an alarm according to the data monitoring response value, and delimiting an abnormal time period based on the data monitoring response value, so that the running state of the photovoltaic power station can be comprehensively monitored, the abnormity can be accurately identified, the early warning can be effectively performed, and the abnormal time period can be quickly positioned. The method is of great significance in improving operation and maintenance efficiency and economic benefits of a photovoltaic power station and guaranteeing safe and stable operation of the power station.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power plants, and in particular to a photovoltaic power plant remote monitoring system based on the Internet of Things. Background Art

[0002] As the global demand for renewable energy continues to grow, photovoltaic power stations, as an important source of clean energy, have been rapidly developed and widely used. The stable operation of photovoltaic power stations is crucial to ensuring the reliability and economic benefits of power supply. However, due to the wide distribution of photovoltaic power stations, the complex and changeable environment in which they are located, and the possible defects of photovoltaic modules and related equipment themselves, photovoltaic power stations may experience various abnormal conditions, such as shadow obstruction, module aging, inverter failure, connection problems, etc. These abnormalities not only affect the power generation efficiency of the power station, but may also lead to serious consequences such as equipment damage and safety accidents.

[0003] In order to ensure the efficient and safe operation of photovoltaic power plants, it is crucial to detect and handle abnormalities in the power plants in a timely manner. At present, the monitoring of photovoltaic power plants usually relies on regular manual inspections and some automated monitoring systems. However, traditional manual inspections are labor-intensive, inefficient, and difficult to achieve real-time monitoring; while existing automated monitoring systems can often only monitor limited parameters and cannot fully evaluate the operating status of photovoltaic power plants. They have limited ability to identify complex abnormal situations, and the accuracy of early warning and fault location needs to be improved. Summary of the invention

[0004] The embodiment of the present invention provides a photovoltaic power station remote monitoring system based on the Internet of Things, which can comprehensively monitor the operating status of the photovoltaic power station, accurately identify abnormalities, effectively warn and quickly locate faults. It is of great significance to improve the operation and maintenance efficiency and economic benefits of the photovoltaic power station and ensure the safe and stable operation of the power station.

[0005] In order to achieve the above object, the present invention provides a photovoltaic power station remote monitoring system based on the Internet of Things, comprising:

[0006] A data acquisition module, used to determine the area to be monitored of the photovoltaic power station, deploy multiple remote monitoring points in the area to be monitored, and obtain monitoring data of each remote monitoring point within a preset time;

[0007] A data analysis module, used for analyzing the monitoring data of each remote monitoring point, and determining a target monitoring data chain based on the analysis result, wherein the target monitoring data chain is provided with a plurality of monitoring data factors and monitoring data factor connection nodes;

[0008] A data calculation module, used for performing adjacent identifier binding on every two adjacent monitoring data factors on the target monitoring data chain, and calculating the data monitoring response value of the target control data chain based on the binding result;

[0009] The abnormal alarm module is used to determine whether there is an abnormality in the monitored area of ​​the photovoltaic power station based on the data monitoring response value. If so, an alarm is issued according to the data monitoring response value, and an abnormal time period is defined based on the data monitoring response value.

[0010] Furthermore, the data acquisition module is used to:

[0011] The data acquisition module is used to determine the spatial scope and spatial layout of the monitoring area, and determine the minimum deployment number and distribution pattern of remote monitoring points;

[0012] The data acquisition module is used to evaluate the effectiveness of different distribution modes according to the expected monitoring target and the preset simulation algorithm, and determine the distribution mode with the highest coverage efficiency;

[0013] The data acquisition module is used to install a data acquisition device at a selected location and verify the validity and response time of the data acquisition device;

[0014] The data acquisition module is used to adjust the deployment of data collection points according to the test results until the predetermined monitoring effect and performance indicators are achieved.

[0015] Furthermore, the data analysis module is used to:

[0016] The data analysis module is used to extract all monitoring data of the same type and construct data series of the same type;

[0017] The data analysis module is used to attribute each monitoring data in the same type of data series to a fixed numerical interval based on a first preset calculation method, and obtain multiple attribute values;

[0018] The data analysis module is used to construct a first numerical analysis set based on all the attribute values;

[0019] The data analysis module is used to calculate the average value and standard deviation of the same type of data series, and calculate the difference between each monitoring data in the same type of data series and the average value;

[0020] The data analysis module is used to calculate the ratio of the difference value to the standard deviation, and construct a second numerical analysis set according to all the ratios;

[0021] The data analysis module is used to calculate the first data reference value of the first numerical analysis set according to the following formula:

[0022]

[0023] Among them, h1 is the first data reference value, n1 is the number of attribute values ​​in the first numerical analysis set, a i is the i-th attribute value in the first numerical analysis set, a 中 is the median of all attribute values ​​in the first numerical analysis set, e i is the weight corresponding to the i-th attribute value;

[0024] The data analysis module is used to calculate the second data reference value of the second numerical analysis set;

[0025] The data analysis module is used to calculate the monitoring data factor of the same type of data series based on the first data reference value and the second data reference value;

[0026] The data analysis module is used to determine the monitoring data factors of all remaining data series of the same type, sort them by size, introduce them into the connection nodes of the initialization data chain in turn, delete redundant connection nodes and connection nodes, and construct the target monitoring data chain, wherein the initialization data chain includes multiple connection nodes and connection nodes.

[0027] Furthermore, the data analysis module is used to:

[0028] The data analysis module is used to configure a first weight for the first data reference value and a second weight for the second data reference value;

[0029] The data analysis module is used to calculate the monitoring data factor of the same type of data series according to the following formula:

[0030] K=d1×(1+h1) h2 +d2×(1+h2) h1 ;

[0031] Among them, K is the monitoring data factor, d1 is the first weight, h2 is the second data reference value, and d2 is the second weight.

[0032] Furthermore, the data calculation module is used for:

[0033] The data calculation module is used to determine the difference amount of the monitoring data factors bound to the adjacent identifiers on the target control data link, wherein the difference amount is the difference between two monitoring data factors;

[0034] The data calculation module is used to calculate the numerical values ​​of all the differences, extract the maximum difference and the minimum difference, and calculate the difference between the maximum difference and the minimum difference;

[0035] The data calculation module is used to configure a standard score for each monitoring data factor on the target control data chain;

[0036] The data calculation module is used to calculate the data monitoring response value of the target control data link according to the following formula:

[0037]

[0038] Where W is the data monitoring response value, S1 is the difference value, Smin is the minimum difference, Smax is the maximum difference, m is the number of monitoring data factors on the target control data link, and F j is the standard score of the jth monitoring data factor.

[0039] Furthermore, the abnormal alarm module is used to:

[0040] The abnormal alarm module is used to obtain a preset preset data monitoring response value;

[0041] The abnormal alarm module is used to determine whether there is an abnormality in the monitored area of ​​the photovoltaic power station according to the relationship between the data monitoring response value and the preset data monitoring response value;

[0042] The abnormal alarm module is used to determine that there is no abnormality in the monitored area of ​​the photovoltaic power station when the data monitoring response value is less than the preset data monitoring response value;

[0043] The abnormal alarm module is used to determine that an abnormality exists in the monitored area of ​​the photovoltaic power station when the data monitoring response value is greater than or equal to the preset data monitoring response value.

[0044] Furthermore, the abnormal alarm module is used to:

[0045] The abnormal alarm module is used to configure a first calculation coefficient y1 for the preset data monitoring response value to obtain y1×g, where y1=1.2, and g is the preset data monitoring response value;

[0046] The abnormal alarm module is used to configure a second calculation coefficient y2 for the preset data monitoring response value to obtain y2×g, where y2=1.4;

[0047] The abnormal alarm module is used to generate a first processing sequence according to the corresponding preset data monitoring response value when g<preset data monitoring response value≤y1×g;

[0048] The abnormal alarm module is used to generate a second processing sequence according to the corresponding preset data monitoring response value when y1×g<preset data monitoring response value≤y2×g;

[0049] The abnormal alarm module is used to generate a third processing sequence according to the corresponding preset data monitoring response value when y2×g<preset data monitoring response value;

[0050] The abnormal alarm module is used to calculate the alarm level coefficient of the area to be monitored based on the first processing sequence, the second processing sequence and the third processing sequence.

[0051] Furthermore, the abnormal alarm module is used to:

[0052] The abnormal alarm module is used to count a first number of the first processing sequence, a second number of the second processing sequence, and a third number of the third processing sequence;

[0053] The abnormal alarm module is used to calculate the alarm level coefficient of the monitored area according to the following formula:

[0054]

[0055] Wherein, L is the alarm level coefficient, R1 is the first quantity, R2 is the second quantity, R3 is the third quantity, and ln is the logarithmic function sign.

[0056] Furthermore, the abnormal alarm module is used to:

[0057] The abnormal alarm module is used to obtain the data monitoring response value of each preset time period of the day, and obtain the data monitoring response value of the same time period of the previous day;

[0058] The abnormal alarm module is used to obtain the minimum data monitoring response value time period and the minimum data monitoring response value of the current day and the previous day respectively;

[0059] The abnormal alarm module is used to determine whether the lowest data monitoring response value of the day is greater than the lowest data monitoring response value of the previous day. If so, it is determined that the abnormality occurred after the period of the lowest data monitoring response value of the previous day; if not, it is determined that the abnormality occurred before the period of the lowest data monitoring response value of the previous day.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The data acquisition module is used to deploy multiple remote monitoring points in the monitored area and obtain the monitoring data of each remote monitoring point within a preset time; the data analysis module is used to analyze the monitoring data and determine the target monitoring data chain; the data calculation module is used to bind adjacent identifiers of every two adjacent monitoring data factors and calculate the data monitoring response value of the target control data chain; the abnormal alarm module is used to determine whether there is an abnormality in the monitored area based on the data monitoring response value. If so, an alarm is issued according to the data monitoring response value, and the abnormal time period is defined based on the data monitoring response value. It can comprehensively monitor the operating status of the photovoltaic power station, accurately identify abnormalities, effectively warn and quickly locate the abnormal time period, which is of great significance to improving the operation and maintenance efficiency and economic benefits of the photovoltaic power station and ensuring the safe and stable operation of the power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0063] Figure 1 The schematic diagram of the structure of the remote monitoring system of a photovoltaic power station based on the Internet of Things in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0064] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0065] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0066] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0067] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0068] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0069] like Figure 1 As shown, an embodiment of the present invention discloses a photovoltaic power station remote monitoring system based on the Internet of Things, including:

[0070] A data acquisition module, used to determine the area to be monitored of the photovoltaic power station, deploy multiple remote monitoring points in the area to be monitored, and obtain monitoring data of each remote monitoring point within a preset time;

[0071] In this embodiment, the monitoring data includes temperature, humidity, voltage, current, air pressure, etc. The monitoring data refers to data that affects photovoltaic power generation.

[0072] A data analysis module, used for analyzing the monitoring data of each remote monitoring point, and determining a target monitoring data chain based on the analysis result, wherein the target monitoring data chain is provided with a plurality of monitoring data factors and monitoring data factor connection nodes;

[0073] A data calculation module, used for performing adjacent identifier binding on every two adjacent monitoring data factors on the target monitoring data chain, and calculating the data monitoring response value of the target control data chain based on the binding result;

[0074] The abnormal alarm module is used to determine whether there is an abnormality in the monitored area of ​​the photovoltaic power station based on the data monitoring response value. If so, an alarm is issued according to the data monitoring response value, and an abnormal time period is defined based on the data monitoring response value.

[0075] The beneficial effect of the above technical solution is that the present invention can comprehensively monitor the operating status of photovoltaic power stations, accurately identify anomalies, effectively warn and quickly locate abnormal time periods, which is of great significance for improving the operation and maintenance efficiency and economic benefits of photovoltaic power stations and ensuring the safe and stable operation of power stations.

[0076] In some embodiments of the present application, the data acquisition module is used to:

[0077] The data acquisition module is used to determine the spatial scope and spatial layout of the monitoring area, and determine the minimum deployment number and distribution pattern of remote monitoring points;

[0078] The data acquisition module is used to evaluate the effectiveness of different distribution modes according to the expected monitoring target and the preset simulation algorithm, and determine the distribution mode with the highest coverage efficiency;

[0079] The data acquisition module is used to install a data acquisition device at a selected location and verify the validity and response time of the data acquisition device;

[0080] The data acquisition module is used to adjust the deployment of data collection points according to the test results until the predetermined monitoring effect and performance indicators are achieved.

[0081] In this embodiment, the minimum deployment quantity and distribution mode can be retrieved from the data history records.

[0082] In this embodiment, the preset simulation algorithm includes a genetic algorithm, a particle swarm optimization or an ant colony algorithm, etc., which are used to optimize the layout of data collection points.

[0083] In this embodiment, the field test includes the evaluation of the field of view, resolution, data transmission rate and system stability of the data acquisition device.

[0084] The beneficial effect of the above technical solution is that the present invention can ensure the accuracy of data collection and avoid errors that affect abnormal judgment by reasonably deploying data monitoring points.

[0085] In some embodiments of the present application, the data analysis module is used to:

[0086] The data analysis module is used to extract all monitoring data of the same type and construct data series of the same type;

[0087] The data analysis module is used to attribute each monitoring data in the same type of data series to a fixed numerical interval based on a first preset calculation method, and obtain multiple attribute values;

[0088] The data analysis module is used to construct a first numerical analysis set based on all the attribute values;

[0089] The data analysis module is used to calculate the average value and standard deviation of the same type of data series, and calculate the difference between each monitoring data in the same type of data series and the average value;

[0090] The data analysis module is used to calculate the ratio of the difference value to the standard deviation, and construct a second numerical analysis set according to all the ratios;

[0091] The data analysis module is used to calculate the first data reference value of the first numerical analysis set according to the following formula:

[0092]

[0093] Among them, h1 is the first data reference value, n1 is the number of attribute values ​​in the first numerical analysis set, a i is the i-th attribute value in the first numerical analysis set, a 中 is the median of all attribute values ​​in the first numerical analysis set, e i is the weight corresponding to the i-th attribute value;

[0094] The data analysis module is used to calculate the second data reference value of the second numerical analysis set;

[0095] The data analysis module is used to calculate the monitoring data factor of the same type of data series based on the first data reference value and the second data reference value;

[0096] The data analysis module is used to determine the monitoring data factors of all remaining data series of the same type, sort them by size, introduce them into the connection nodes of the initialization data chain in turn, delete redundant connection nodes and connection nodes, and construct the target monitoring data chain, wherein the initialization data chain includes multiple connection nodes and connection nodes.

[0097] In this embodiment, for the same type of monitoring data, such as temperature, the temperature of each monitoring data point is extracted to construct a data series of the same type.

[0098] In this embodiment, the first preset calculation method is a data normalization method. The specific normalization process is complicated and mature, and will not be introduced in detail here.

[0099] In this embodiment, the fixed numerical interval is [0, 1].

[0100] In this embodiment, the calculation method of the second data reference value is consistent with the calculation method of the first data reference value, and in order to save space, it will not be repeated here.

[0101] In this embodiment, the initialized data chain includes a plurality of blank connection nodes and connection sections, the sorted monitoring data factors are introduced into the blank connection nodes in sequence, and the remaining blank connection nodes and connection sections are deleted to obtain the target monitoring data chain.

[0102] The beneficial effect of the above technical solution is: the present invention calculates the monitoring data factor of the same type of data series based on the first data reference value and the second data reference value, which can avoid the errors caused by the different temperatures collected by multiple data monitoring points, and thus generate a unified monitoring data factor, which can effectively eliminate the errors, provide abnormal judgment data, and construct the target monitoring data chain, which can further lay the foundation for abnormal judgment of the monitored area.

[0103] In some embodiments of the present application, the data analysis module is used to:

[0104] The data analysis module is used to configure a first weight for the first data reference value and a second weight for the second data reference value;

[0105] The data analysis module is used to calculate the monitoring data factor of the same type of data series according to the following formula:

[0106] K=d1×(1+h1) h2 +d2×(1+h2) h1 ;

[0107] Among them, K is the monitoring data factor, d1 is the first weight, h2 is the second data reference value, and d2 is the second weight.

[0108] In this embodiment, the first weight is 0.6 and the second weight is 0.4.

[0109] In some embodiments of the present application, the data calculation module is used to:

[0110] The data calculation module is used to determine the difference amount of the monitoring data factors bound to the adjacent identifiers on the target control data link, wherein the difference amount is the difference between two monitoring data factors;

[0111] The data calculation module is used to calculate the numerical values ​​of all the differences, extract the maximum difference and the minimum difference, and calculate the difference between the maximum difference and the minimum difference;

[0112] The data calculation module is used to configure a standard score for each monitoring data factor on the target control data chain;

[0113] The data calculation module is used to calculate the data monitoring response value of the target control data link according to the following formula:

[0114]

[0115] Where W is the data monitoring response value, S1 is the difference value, Smin is the minimum difference, Smax is the maximum difference, m is the number of monitoring data factors on the target control data link, and F j is the standard score of the jth monitoring data factor.

[0116] In this embodiment, the method for calculating the standard score is a well-known technique and will not be described in detail here.

[0117] The beneficial effect of the above technical solution is that the present invention can accurately reflect whether there is an abnormality in the monitored area by calculating the data monitoring response value of the target control data link, provide reliable data support for abnormality judgment, avoid errors in manual judgment, and make a more comprehensive judgment, avoiding the singleness of the judgment basis.

[0118] In some embodiments of the present application, the abnormality alarm module is used to:

[0119] The abnormal alarm module is used to obtain a preset preset data monitoring response value;

[0120] The abnormal alarm module is used to determine whether there is an abnormality in the monitored area of ​​the photovoltaic power station according to the relationship between the data monitoring response value and the preset data monitoring response value;

[0121] The abnormal alarm module is used to determine that there is no abnormality in the monitored area of ​​the photovoltaic power station when the data monitoring response value is less than the preset data monitoring response value;

[0122] The abnormal alarm module is used to determine that an abnormality exists in the monitored area of ​​the photovoltaic power station when the data monitoring response value is greater than or equal to the preset data monitoring response value.

[0123] The beneficial effect of the above technical solution is that the present invention determines whether there is an abnormality in the monitored area of ​​the photovoltaic power station according to the relationship between the data monitoring response value and the preset data monitoring response value. The judgment process is simple and direct, and the judgment efficiency is improved.

[0124] In some embodiments of the present application, the abnormality alarm module is used to:

[0125] The abnormal alarm module is used to configure a first calculation coefficient y1 for the preset data monitoring response value to obtain y1×g, where y1=1.2, and g is the preset data monitoring response value;

[0126] The abnormal alarm module is used to configure a second calculation coefficient y2 for the preset data monitoring response value to obtain y2×g, where y2=1.4;

[0127] The abnormal alarm module is used to generate a first processing sequence according to the corresponding preset data monitoring response value when g<preset data monitoring response value≤y1×g;

[0128] The abnormal alarm module is used to generate a second processing sequence according to the corresponding preset data monitoring response value when y1×g<preset data monitoring response value≤y2×g;

[0129] The abnormal alarm module is used to generate a third processing sequence according to the corresponding preset data monitoring response value when y2×g<preset data monitoring response value;

[0130] The abnormal alarm module is used to calculate the alarm level coefficient of the area to be monitored based on the first processing sequence, the second processing sequence and the third processing sequence.

[0131] The beneficial effect of the above technical solution is that the present invention calculates the alarm level coefficient of the monitored area based on the first processing sequence, the second processing sequence and the third processing sequence. When the alarm level coefficient is higher, it means that the operating state of the monitored area is worse and the existing problems are more serious, which provides reliable data support for maintenance of the staff and ensures the accurate investment of manpower and material resources.

[0132] In some embodiments of the present application, the abnormality alarm module is used to:

[0133] The abnormal alarm module is used to count a first number of the first processing sequence, a second number of the second processing sequence, and a third number of the third processing sequence;

[0134] The abnormal alarm module is used to calculate the alarm level coefficient of the monitored area according to the following formula:

[0135]

[0136] Wherein, L is the alarm level coefficient, R1 is the first quantity, R2 is the second quantity, R3 is the third quantity, and ln is the logarithmic function sign.

[0137] In some embodiments of the present application, the abnormality alarm module is used to:

[0138] The abnormal alarm module is used to obtain the data monitoring response value of each preset time period of the day, and obtain the data monitoring response value of the same time period of the previous day;

[0139] The abnormal alarm module is used to obtain the minimum data monitoring response value time period and the minimum data monitoring response value of the current day and the previous day respectively;

[0140] The abnormal alarm module is used to determine whether the lowest data monitoring response value of the day is greater than the lowest data monitoring response value of the previous day. If so, it is determined that the abnormality occurred after the period of the lowest data monitoring response value of the previous day; if not, it is determined that the abnormality occurred before the period of the lowest data monitoring response value of the previous day.

[0141] The beneficial effects of the above technical solution are: the present invention can comprehensively monitor the operating status of the photovoltaic power station, accurately identify anomalies, effectively warn, and quickly locate abnormal time periods, providing convenience for the maintenance work of the staff, improving work efficiency and reducing workload.

[0142] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0143] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0144] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A photovoltaic power station remote monitoring system based on the Internet of Things, characterized in that: include: A data acquisition module, used to determine the area to be monitored of the photovoltaic power station, deploy multiple remote monitoring points in the area to be monitored, and obtain monitoring data of each remote monitoring point within a preset time; A data analysis module, used for analyzing the monitoring data of each remote monitoring point, and determining a target monitoring data chain based on the analysis result, wherein the target monitoring data chain is provided with a plurality of monitoring data factors and monitoring data factor connection nodes; A data calculation module, used for performing adjacent identifier binding on every two adjacent monitoring data factors on the target monitoring data chain, and calculating the data monitoring response value of the target control data chain based on the binding result; The abnormal alarm module is used to determine whether there is an abnormality in the monitored area of ​​the photovoltaic power station based on the data monitoring response value. If so, an alarm is issued according to the data monitoring response value, and an abnormal time period is defined based on the data monitoring response value.

2. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module is used for: The data acquisition module is used to determine the spatial scope and spatial layout of the monitoring area, and determine the minimum deployment number and distribution pattern of remote monitoring points; The data acquisition module is used to evaluate the effectiveness of different distribution modes according to the expected monitoring target and the preset simulation algorithm, and determine the distribution mode with the highest coverage efficiency; The data acquisition module is used to install a data acquisition device at a selected location and verify the validity and response time of the data acquisition device; The data acquisition module is used to adjust the deployment of data collection points according to the test results until the predetermined monitoring effect and performance indicators are achieved.

3. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The data analysis module is used for: The data analysis module is used to extract all monitoring data of the same type and construct data series of the same type; The data analysis module is used to attribute each monitoring data in the same type of data series to a fixed numerical interval based on a first preset calculation method, and obtain multiple attribute values; The data analysis module is used to construct a first numerical analysis set based on all the attribute values; The data analysis module is used to calculate the average value and standard deviation of the same type of data series, and calculate the difference between each monitoring data in the same type of data series and the average value; The data analysis module is used to calculate the ratio of the difference value to the standard deviation, and construct a second numerical analysis set according to all the ratios; The data analysis module is used to calculate the first data reference value of the first numerical analysis set according to the following formula: Among them, h1 is the first data reference value, n1 is the number of attribute values ​​in the first numerical analysis set, a i is the i-th attribute value in the first numerical analysis set, a 中 is the median of all attribute values ​​in the first numerical analysis set, e i is the weight corresponding to the i-th attribute value; The data analysis module is used to calculate the second data reference value of the second numerical analysis set; The data analysis module is used to calculate the monitoring data factor of the same type of data series based on the first data reference value and the second data reference value; The data analysis module is used to determine the monitoring data factors of all remaining data series of the same type, sort them by size, introduce them into the connection nodes of the initialization data chain in turn, delete redundant connection nodes and connection nodes, and construct the target monitoring data chain, wherein the initialization data chain includes multiple connection nodes and connection nodes.

4. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 3 is characterized in that: The data analysis module is used for: The data analysis module is used to configure a first weight for the first data reference value and a second weight for the second data reference value; The data analysis module is used to calculate the monitoring data factor of the same type of data series according to the following formula: K=d1×(1+h1) h2 +d2×(1+h2) h1 ; Among them, K is the monitoring data factor, d1 is the first weight, h2 is the second data reference value, and d2 is the second weight.

5. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The data calculation module is used for: The data calculation module is used to determine the difference amount of the monitoring data factors bound to the adjacent identifiers on the target control data link, wherein the difference amount is the difference between two monitoring data factors; The data calculation module is used to calculate the numerical values ​​of all the differences, extract the maximum difference and the minimum difference, and calculate the difference between the maximum difference and the minimum difference; The data calculation module is used to configure a standard score for each monitoring data factor on the target control data chain; The data calculation module is used to calculate the data monitoring response value of the target control data link according to the following formula: Where W is the data monitoring response value, S1 is the difference value, Smin is the minimum difference, Smax is the maximum difference, m is the number of monitoring data factors on the target control data link, and F j is the standard score of the jth monitoring data factor.

6. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The abnormal alarm module is used for: The abnormal alarm module is used to obtain a preset preset data monitoring response value; The abnormal alarm module is used to determine whether there is an abnormality in the monitored area of ​​the photovoltaic power station according to the relationship between the data monitoring response value and the preset data monitoring response value; The abnormal alarm module is used to determine that there is no abnormality in the monitored area of ​​the photovoltaic power station when the data monitoring response value is less than the preset data monitoring response value; The abnormal alarm module is used to determine that an abnormality exists in the monitored area of ​​the photovoltaic power station when the data monitoring response value is greater than or equal to the preset data monitoring response value.

7. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 6 is characterized in that: The abnormal alarm module is used for: The abnormal alarm module is used to configure a first calculation coefficient y1 for the preset data monitoring response value to obtain y1×g, where y1=1.2, and g is the preset data monitoring response value; The abnormal alarm module is used to configure a second calculation coefficient y2 for the preset data monitoring response value to obtain y2×g, where y2=1.4; The abnormal alarm module is used to generate a first processing sequence according to the corresponding preset data monitoring response value when g<preset data monitoring response value≤y1×g; The abnormal alarm module is used to generate a second processing sequence according to the corresponding preset data monitoring response value when y1×g<preset data monitoring response value≤y2×g; The abnormal alarm module is used to generate a third processing sequence according to the corresponding preset data monitoring response value when y2×g<preset data monitoring response value; The abnormal alarm module is used to calculate the alarm level coefficient of the area to be monitored based on the first processing sequence, the second processing sequence and the third processing sequence.

8. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 7 is characterized in that: The abnormal alarm module is used for: The abnormal alarm module is used to count a first number of the first processing sequence, a second number of the second processing sequence, and a third number of the third processing sequence; The abnormal alarm module is used to calculate the alarm level coefficient of the monitored area according to the following formula: Wherein, L is the alarm level coefficient, R1 is the first quantity, R2 is the second quantity, R3 is the third quantity, and ln is the logarithmic function sign.

9. The photovoltaic power station remote monitoring system based on the Internet of Things according to claim 5 is characterized in that: The abnormal alarm module is used for: The abnormal alarm module is used to obtain the data monitoring response value of each preset time period of the day, and obtain the data monitoring response value of the same time period of the previous day; The abnormal alarm module is used to obtain the minimum data monitoring response value time period and the minimum data monitoring response value of the current day and the previous day respectively; The abnormal alarm module is used to determine whether the lowest data monitoring response value of the day is greater than the lowest data monitoring response value of the previous day. If so, it is determined that the abnormality occurred after the period of the lowest data monitoring response value of the previous day; if not, it is determined that the abnormality occurred before the period of the lowest data monitoring response value of the previous day.

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