Distributed photovoltaic power station intelligent monitoring system and method

Abstract

The invention provides a distributed photovoltaic power station intelligent monitoring system and method, and the system comprises a distributed photovoltaic point management module which is used for managing a plurality of accessed distributed photovoltaic points and obtaining the first monitoring data of each distributed photovoltaic point; the load point management module is used for managing a plurality of accessed load points and acquiring second monitoring data of each load point; and the scheduling module is used for comprehensively analyzing the first monitoring data and the second monitoring data, and determining and executing a scheduling scheme. According to the invention, supply and demand scheduling control among a plurality of distributed photovoltaic points and a plurality of load points is realized, and photovoltaic consumption is maximized on the premise of reducing influence on a power grid as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring management systems, and in particular to an intelligent monitoring system and method for a distributed photovoltaic power station. Background Art

[0002] Distributed photovoltaic power stations refer to power generation systems that use distributed resources, have a small installed capacity, and are located near users. They are generally connected to a power grid with a voltage level of less than 35 kV or lower. Distributed photovoltaic power stations specifically refer to distributed photovoltaic power station systems that use photovoltaic modules to directly convert solar energy into electrical energy.

[0003] How to realize the operation status monitoring and abnormality detection of distributed photovoltaic power stations is the basis for the stable and effective operation of distributed photovoltaic power stations. The existing monitoring and management systems only have the functions of data collection, transmission and viewing, and are relatively lacking in the coordinated control between multiple distributed photovoltaic points and multiple load points. Summary of the invention

[0004] One of the purposes of the present invention is to provide a distributed photovoltaic power station intelligent monitoring system and method to realize supply and demand scheduling control between multiple distributed photovoltaic points and multiple load points, and to achieve maximum photovoltaic consumption while minimizing the impact on the power grid.

[0005] An embodiment of the present invention provides a distributed photovoltaic power station intelligent monitoring system, comprising: A distributed photovoltaic point management module, used to manage multiple connected distributed photovoltaic points and obtain first monitoring data of each distributed photovoltaic point; A load point management module, used to manage multiple connected load points and obtain second monitoring data of each load point; The scheduling module is used to comprehensively analyze the first monitoring data and the second monitoring data, determine the scheduling plan and execute it.

[0006] Preferably, the distributed photovoltaic point management module includes: A photovoltaic point environment management unit, used to receive environmental monitoring data from an environmental monitoring device configured at the photovoltaic point; The photovoltaic equipment management unit is used to receive the electric energy monitoring data of the electric energy parameter monitoring device configured in each photovoltaic power generation equipment.

[0007] Preferably, the load point management module includes: A load end management unit, used to receive power monitoring data of power parameter monitoring devices of each load end configured at each load point; An image management unit, used for receiving images collected by each image collection module configured at each load point; The reservation management unit is used to receive and process reservation applications for the load ends of each load point.

[0008] Preferably, the scheduling module includes: A real-time power generation data analysis unit is used to analyze the power monitoring data of photovoltaic power generation equipment and obtain the real-time power generation data corresponding to each photovoltaic point; The power generation forecasting and analysis unit comprehensively analyzes real-time power generation data, environmental monitoring data and historical power generation data, makes short-term forecasts for each photovoltaic point, and obtains forecast power generation data; The load power consumption analysis unit is used to analyze the power parameter monitoring data of each load end of each load point to obtain the real-time power consumption data corresponding to each load point; A load forecasting and analysis unit is used to analyze the images, reservation applications and historical power consumption data corresponding to each load point to obtain the forecast power consumption data corresponding to each load point; The dispatching scheme analysis unit is used to conduct a comprehensive analysis of the real-time power generation data, the predicted power generation data, the real-time power consumption data and the predicted power consumption data to determine the dispatching scheme.

[0009] The execution unit is used to execute the scheduling plan.

[0010] Preferably, the scheduling analysis unit performs the following operations: The real-time power generation data and predicted power generation data of each photovoltaic point are sampled in sections and a power generation sequence is constructed based on the results of the section sampling; The real-time power consumption data and predicted power consumption data of each load point are sampled in sections and a power consumption sequence is constructed based on the results of the section sampling; According to the electricity consumption sequence, the power generation sequence is screened, the paired power generation sequence is obtained and the corresponding analysis group is constructed; Analyze the analysis groups corresponding to each power usage sequence to determine the pairing mode; When the pairing mode is one-to-one pairing, the load points are sorted according to the number of pairable generation sequences in the analysis group; When the number in the first analysis group is one, pair the pairable power generation sequence with the power consumption sequence in the analysis group and delete the paired power consumption sequence and power generation sequence from the analysis group and the sorting; repeat the pairing process until the power consumption sequence and / or power generation sequence are paired; Construct a dispatch plan based on the paired power generation sequence and power consumption sequence.

[0011] Preferably, the scheduling analysis unit further performs the following operations: When the pairing mode is one-to-one pairing, when the number of the first analysis group is not one, the matching degree between the power consumption sequence and the power generation sequence is calculated, and the power consumption sequence and the power generation sequence with the greatest matching degree are paired.

[0012] Preferably, the scheduling analysis unit further performs the following operations: When the pairing mode is one-to-many, each photovoltaic point is combined to obtain multiple combinations and determine the power generation sequence corresponding to the combination; According to the power generation sequence corresponding to the combination, the analysis group corresponding to each power consumption sequence is updated; Sort the load points by the number of paired generation sequences in the analysis group; The first-ranked electricity consumption sequence and its corresponding analysis group are extracted in turn, the matching degree between the electricity consumption sequence and each power generation sequence in the analysis group is calculated, and the electricity consumption sequence and power generation sequence with the greatest matching degree are paired; and the paired electricity consumption sequence and power generation sequence are deleted from the analysis group and the ranking; the pairing process is repeated until the electricity consumption sequence and / or power generation sequence are paired.

[0013] Preferably, the scheduling analysis unit further performs the following operations: When the pairing mode is many-to-one, the analysis groups corresponding to each power usage sequence are updated according to the power generation sequence corresponding to the combination; Sort the load points by the number of paired generation sequences in the analysis group; The first-ranked electricity usage sequence and its corresponding analysis group are extracted in turn, the matching degree between the electricity usage sequence and each power generation sequence in the analysis group is calculated, and the electricity usage sequence and power generation sequence with the greatest matching degree are paired; the paired electricity usage sequence and power generation sequence are deleted from the analysis group and the ranking, and a new power generation sequence is generated based on the paired electricity usage sequence and power generation sequence. The new power generation sequence is screened based on the electricity usage sequence to determine whether it should be added to the corresponding analysis group; the pairing process is repeated until all electricity usage sequences are paired.

[0014] Preferably, the scheduling analysis unit screens the power generation sequence according to the power consumption sequence, obtains the pairable power generation sequence and constructs the corresponding analysis group, and performs the following operations: Obtain the loss coefficient between the load point corresponding to the electricity consumption sequence and the photovoltaic point corresponding to the power generation sequence; Correct each data in the power generation sequence according to the loss coefficient; Compare each data in the corrected power generation sequence with each corresponding data in the power consumption sequence. If both are greater than each other, the data passes the screening; otherwise, the data fails.

[0015] The present invention also provides a distributed photovoltaic power station intelligent monitoring method, which uses any of the above-mentioned distributed photovoltaic power station intelligent monitoring systems, including: Managing multiple connected distributed photovoltaic points and obtaining first monitoring data of each distributed photovoltaic point; Manage multiple connected load points and obtain the second monitoring data of each load point; Comprehensively analyze the first monitoring data and the second monitoring data, determine the scheduling plan and execute it.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0017] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of a distributed photovoltaic power station intelligent monitoring system according to an embodiment of the present invention; Figure 2 Schematic diagram of a distributed photovoltaic power station intelligent monitoring method in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0020] The embodiment of the present invention provides a distributed photovoltaic power station intelligent monitoring system. Figure 1 As shown, including: A distributed photovoltaic point management module 1 is used to manage multiple connected distributed photovoltaic points and obtain first monitoring data of each distributed photovoltaic point; The load point management module 2 is used to manage the connected multiple load points and obtain the second monitoring data of each load point; The scheduling module 3 is used to comprehensively analyze the first monitoring data and the second monitoring data, determine the scheduling plan and execute it.

[0021] Wherein, the distributed photovoltaic point management module 1 includes: A photovoltaic point environment management unit, used to receive environmental monitoring data from an environmental monitoring device configured at the photovoltaic point; The photovoltaic equipment management unit is used to receive the electric energy monitoring data of the electric energy parameter monitoring device configured in each photovoltaic power generation equipment.

[0022] That is, the first monitoring data includes: environmental monitoring data and power monitoring data of the photovoltaic point; the environmental monitoring data includes: light intensity, light duration, temperature, humidity, etc.; the power monitoring data includes voltage, current, power, etc.; Wherein, the load point management module 2 includes: The load end management unit is used to receive the electric energy monitoring data of the electric energy parameter monitoring device configured at each load end of each load point; the electric energy monitoring data includes: voltage, current, electric quantity, etc.; An image management unit is used to receive images collected by various image acquisition modules configured at various load points; it is mainly used to collect images of waiting vehicles in the waiting areas of various load points; The reservation management unit is used to receive and process reservation applications for the load ends of each load point. Each load end of the load point (for example, charging pile) can be reserved online, that is, users can log in to the monitoring system through mobile terminals such as mobile phones, view the usage of each charging pile, and make reservations.

[0023] Among them, the scheduling module 3 includes: The real-time power generation data analysis unit is used to analyze the power monitoring data of the photovoltaic power generation equipment to obtain the real-time power generation data corresponding to each photovoltaic point; the real-time power generation data is the amount of electricity generated per unit time; The power generation prediction and analysis unit comprehensively analyzes the real-time power generation data, environmental monitoring data and historical power generation data, performs short-term predictions on each photovoltaic point, and obtains predicted power generation data; the predicted power generation data includes: the amount of electricity that may be generated per unit time at each time after the current moment; the prediction is specifically: the real-time power generation data, environmental monitoring data and historical power generation data are screened and feature extracted, and the extracted features are used as search items to retrieve the predicted power generation data from the pre-configured prediction library; the prediction library is constructed by prior analysis, and the predicted power generation data in the library is uniformly corresponding to each extracted feature; the screening is to screen out the historical power generation data under the same environment based on the environmental monitoring data, and the extracted features include: parameters representing the current power generation and parameters representing the power generation in each time period in the historical power generation data; in addition, in order to improve accuracy, environmental prediction data can be added for accurate prediction, that is, the search items of the prediction library need to add the parameters corresponding to each monitoring item of the environmental prediction data; The load power consumption analysis unit is used to analyze the power parameter monitoring data of each load end of each load point to obtain the real-time power consumption data corresponding to each load point; the real-time power consumption data is the current power consumption per unit time; The load prediction and analysis unit is used to analyze the images, reservation and use applications and historical power consumption data corresponding to each load point to obtain the predicted power consumption data corresponding to each load point; the predicted power consumption data includes: the amount of power that may be used per unit time at each time after the current moment; The dispatching scheme analysis unit is used to conduct a comprehensive analysis of the real-time power generation data, the predicted power generation data, the real-time power consumption data and the predicted power consumption data to determine the dispatching scheme.

[0024] The execution unit is used to execute the dispatching plan. It comprehensively considers the real-time power generation data, predicted power generation data, real-time power consumption data and predicted power consumption data to ensure the effectiveness of this dispatching for the current and future power generation and power consumption, without the need for frequent dispatching, to ensure the effectiveness and accuracy of the dispatching; In order to generate the best scheduling solution, the scheduling analysis unit performs the following operations: The real-time power generation data and predicted power generation data of each photovoltaic point are sampled in sections and a power generation sequence is constructed based on the results of the section sampling; the section sampling is performed at a preset time interval (any time between 2 minutes and 30 minutes), and then the section sampled data are arranged in sequence to form a power generation sequence; The real-time power consumption data and predicted power consumption data of each load point are sampled in sections and a power consumption sequence is constructed based on the results of the section sampling. The sampling of the power consumption sequence is consistent with the power generation sequence, so it will not be explained in detail here. According to the electricity consumption sequence, the power generation sequence is screened to obtain the paired power generation sequence and construct the corresponding analysis group; each power generation sequence in the screened analysis group can meet the needs of the power consumption sequence; Analyze the analysis groups corresponding to each power consumption sequence to determine the pairing mode; when the total number of power consumption sequences is the same as the total number of power generation sequences in the analysis group and the total number of power generation sequences in the largest analysis group is the same as the total number of power generation sequences, enter the one-to-one pairing mode; when the total number of power consumption sequences is less than the total number of power generation sequences in the analysis group and there is an empty set in the analysis group corresponding to the power consumption sequence, enter the one-to-many pairing mode; when the total number of power consumption sequences is more than the total number of power generation sequences in the analysis group and the number of power generation sequences in the analysis group is greater than or equal to the preset number threshold, enter the many-to-one pairing mode; When the pairing mode is one-to-one pairing, the load points are sorted according to the number of pairable generation sequences in the analysis group; When the number in the first analysis group is one, pair the pairable power generation sequence with the power consumption sequence in the analysis group and delete the paired power consumption sequence and power generation sequence from the analysis group and the sorting; repeat the pairing process until the power consumption sequence and / or power generation sequence are paired; Construct a dispatch plan based on the paired power generation sequence and power consumption sequence.

[0025] When the pairing mode is one-to-one pairing, when the number of the first analysis group is not one, the matching degree between the power consumption sequence and the power generation sequence is calculated, and the power consumption sequence and the power generation sequence with the greatest matching degree are paired.

[0026] When the pairing mode is one-to-many, each photovoltaic point is combined to obtain multiple combinations and determine the power generation sequence corresponding to the combination; According to the power generation sequence corresponding to the combination, the analysis group corresponding to each power consumption sequence is updated; Sort the load points by the number of paired generation sequences in the analysis group; The first-ranked electricity consumption sequence and its corresponding analysis group are extracted in turn, the matching degree between the electricity consumption sequence and each power generation sequence in the analysis group is calculated, and the electricity consumption sequence and power generation sequence with the greatest matching degree are paired; and the paired electricity consumption sequence and power generation sequence are deleted from the analysis group and the ranking; the pairing process is repeated until the electricity consumption sequence and / or power generation sequence are paired.

[0027] When the pairing mode is many-to-one, the analysis groups corresponding to each power usage sequence are updated according to the power generation sequence corresponding to the combination; Sort the load points by the number of paired generation sequences in the analysis group; The first-ranked electricity usage sequence and its corresponding analysis group are extracted in turn, the matching degree between the electricity usage sequence and each power generation sequence in the analysis group is calculated, and the electricity usage sequence and power generation sequence with the greatest matching degree are paired; the paired electricity usage sequence and power generation sequence are deleted from the analysis group and the ranking, and a new power generation sequence is generated based on the paired electricity usage sequence and power generation sequence. The new power generation sequence is screened based on the electricity usage sequence to determine whether it should be added to the corresponding analysis group; the pairing process is repeated until all electricity usage sequences are paired.

[0028] In order to ensure the accuracy of screening and pairing, in one embodiment, the scheduling analysis unit screens the power generation sequence according to the power consumption sequence, obtains the pairable power generation sequence and constructs the corresponding analysis group, and performs the following operations: Obtain the loss coefficient between the load point corresponding to the electricity consumption sequence and the photovoltaic point corresponding to the power generation sequence; Correct each data in the power generation sequence according to the loss coefficient; Compare each data in the corrected power generation sequence with each corresponding data in the power consumption sequence. If both are greater than each other, the data passes the screening; otherwise, the data fails.

[0029] In addition, when pairing, that is, when calculating the matching degree, the power generation sequence needs to be corrected to ensure that it conforms to the actual situation, thereby improving the pairing accuracy.

[0030] In one embodiment, the load prediction analysis unit analyzes the image, reservation application, and historical power consumption data corresponding to each load point to obtain the predicted power consumption data corresponding to each load point, including: Determine whether there is an application for reservation. If not, segment the image corresponding to each load point to obtain the regional image in the preset waiting area; the waiting area includes: the area divided for waiting vehicles, the area on the road from the waiting area to the load point, and the extension area of ​​the entrance and exit, etc.; Perform object recognition on the regional image to identify the vehicle and its model and license plate number; Sort and number the identified vehicles; According to the maximum allowed charging number of the load point (the number of load terminals), a corresponding number of prediction sorting axes are constructed and pre-processed according to the current monitoring data of each load terminal; The predicted sorting axis is sorted before and after the idle time point after the pre-processing to build a list of tasks to be scheduled; Sequentially extract the vehicle with the first sorting number and arrange it to the first predicted sorting axis in the list of tasks to be arranged; when arranging, retrieve the historical charging record of the vehicle according to the license plate number, average the charging time of the historical charging record, adjust the idle time of the predicted sorting axis according to the average time, update the list of tasks to be arranged, and configure it to the corresponding time on the predicted sorting axis according to the power curve in the charging record corresponding to the average time in the historical charging record; when the historical charging record is not retrieved according to the license plate number, determine the estimated charging time according to the vehicle model and the pre-configured vehicle model charging estimation table; adjust the idle time of the predicted sorting axis according to the estimated charging time, update the list of tasks to be arranged, and configure it to the corresponding time on the predicted sorting axis according to the power curve corresponding to the estimated charging time; The statistical prediction sorting axis corresponds to the sum of power at each future time, on this basis, the predicted power consumption data is obtained; The pre-processing includes: obtaining the remaining charging time corresponding to each load end, adjusting the idle time of the prediction sorting axis with the remaining charging time, retrieving the total curve from the pre-configured curve generation library based on the generated power curve, determining the power curve corresponding to the remaining charging time based on the total curve and the generated power curve, and configuring it to the corresponding time on the prediction sorting axis; When there is an application for reservation, in the pre-processing, the corresponding prediction sorting axis is subjected to time masking processing according to the reservation time period of the reservation application and the number of the reserved load end. The task arrangement of the vehicle that does not perform image recognition in the processed time period is performed. At this time, when sorting, it is necessary to determine whether the time difference between the starting point of the idle time of the prediction sorting axis and the starting point of the masking time meets the needs of the extracted vehicle. If it does not meet the needs, the idle time point of the prediction sorting axis is moved to the end point of the masking time to re-sort the list of tasks to be arranged and re-extract the prediction sorting axis; When sorting, it is necessary to verify whether there is an appointment application based on the license plate number of the sorted vehicle. If so, determine whether the starting point of the idle time on the predicted sorting axis is later than the starting point of the mask time corresponding to the appointment application. If it is later, skip the vehicle and mark it. Send the appointment time, load end and license plate number to the mobile terminal of the load point staff to facilitate the staff to schedule; When it is predicted whether the starting point of the idle time on the sorting axis is earlier than the starting point of the masking time corresponding to the corresponding reservation application, the sorting is normal and the masking time corresponding to the corresponding reservation application in the pre-processing is cancelled; In addition, when recognizing images, if more than the preset number of vehicles are detected at the entrance and exit, the reservation application is closed; and the reservation application is open based on the idle time on the predicted sorting axis; through the above measures, the effective coordination between the reservation application and the on-site waiting is ensured, and the orderly and safe operation of the load point is ensured. The vehicles with sorting numbers are tracked, and when the sorting changes, the prediction process is re-performed.

[0031] Since scheduling involves calculations and other processing and the probability of scheduling anomalies will increase with frequent scheduling, in one embodiment, the distributed photovoltaic power station intelligent monitoring system further includes: a scheduling trigger module; The scheduling trigger module performs the following operations: Determine the usage of the electric energy storage modules associated with each load end; When the power level of the energy storage module is lower than the preset first warning power level (10%), the dispatch is triggered; When the power level of the energy storage module is higher than or equal to the first warning power level but less than or equal to the preset second warning power level (70%), and the power level reduced within the preset time period (any from 1 minute to 30 minutes) of the unit is greater than or equal to the preset power threshold (any from 1% to 5%), scheduling is triggered; after the scheduling is triggered, the first monitoring data and the second monitoring data are comprehensively analyzed to determine the scheduling plan and execute it.

[0032] The present invention also provides a distributed photovoltaic power station intelligent monitoring method, using any of the above distributed photovoltaic power station intelligent monitoring systems, such as Figure 2 As shown, including: Step 1: managing multiple connected distributed photovoltaic points and obtaining first monitoring data of each distributed photovoltaic point; Step 2: managing the connected multiple load points and obtaining the second monitoring data of each load point; Step 3: Comprehensively analyze the first monitoring data and the second monitoring data, determine the scheduling plan and execute it.

[0033] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A distributed photovoltaic power station intelligent monitoring system, characterized in that: include: A distributed photovoltaic point management module, used to manage multiple connected distributed photovoltaic points and obtain first monitoring data of each distributed photovoltaic point; A load point management module, used to manage multiple connected load points and obtain second monitoring data of each load point; The scheduling module is used to comprehensively analyze the first monitoring data and the second monitoring data, determine the scheduling plan and execute it.

2. The distributed photovoltaic power station intelligent monitoring system according to claim 1, characterized in that: Distributed photovoltaic point management module includes: A photovoltaic point environment management unit, used to receive environmental monitoring data from an environmental monitoring device configured at the photovoltaic point; The photovoltaic equipment management unit is used to receive the electric energy monitoring data of the electric energy parameter monitoring device configured in each photovoltaic power generation equipment.

3. The distributed photovoltaic power station intelligent monitoring system according to claim 1, characterized in that: The load point management module includes: A load end management unit, used to receive power monitoring data of power parameter monitoring devices of each load end configured at each load point; An image management unit, used for receiving images collected by each image collection module configured at each load point; The reservation management unit is used to receive and process reservation applications for the load ends of each load point.

4. The distributed photovoltaic power station intelligent monitoring system according to claim 1, characterized in that: The scheduling module includes: A real-time power generation data analysis unit is used to analyze the power monitoring data of photovoltaic power generation equipment and obtain the real-time power generation data corresponding to each photovoltaic point; The power generation forecasting and analysis unit comprehensively analyzes real-time power generation data, environmental monitoring data and historical power generation data, makes short-term forecasts for each photovoltaic point, and obtains forecast power generation data; The load power consumption analysis unit is used to analyze the power parameter monitoring data of each load end of each load point to obtain the real-time power consumption data corresponding to each load point; A load forecasting and analysis unit is used to analyze the images, reservation applications and historical power consumption data corresponding to each load point to obtain the forecast power consumption data corresponding to each load point; A dispatching scheme analysis unit is used to comprehensively analyze the real-time power generation data, the predicted power generation data, the real-time power consumption data and the predicted power consumption data to determine the dispatching scheme; The execution unit is used to execute the scheduling plan.

5. The distributed photovoltaic power station intelligent monitoring system according to claim 4, characterized in that: The scheduling analysis unit performs the following operations: The real-time power generation data and predicted power generation data of each photovoltaic point are sampled in sections and a power generation sequence is constructed based on the results of the section sampling; The real-time power consumption data and predicted power consumption data of each load point are sampled in sections and a power consumption sequence is constructed based on the results of the section sampling; According to the electricity consumption sequence, the power generation sequence is screened, the paired power generation sequence is obtained and the corresponding analysis group is constructed; Analyze the analysis groups corresponding to each power usage sequence to determine the pairing mode; When the pairing mode is one-to-one pairing, the load points are sorted according to the number of pairable generation sequences in the analysis group; When the number in the first analysis group is one, pair the pairable power generation sequence with the power consumption sequence in the analysis group and delete the paired power consumption sequence and power generation sequence from the analysis group and the sorting; repeat the pairing process until the power consumption sequence and / or power generation sequence are paired; Construct a dispatch plan based on the paired power generation sequence and power consumption sequence.

6. The distributed photovoltaic power station intelligent monitoring system according to claim 5, characterized in that: The scheduling analysis unit also performs the following operations: When the pairing mode is one-to-one pairing, when the number of the first analysis group is not one, the matching degree between the power consumption sequence and the power generation sequence is calculated, and the power consumption sequence and the power generation sequence with the greatest matching degree are paired.

7. The distributed photovoltaic power station intelligent monitoring system according to claim 5, characterized in that: The scheduling analysis unit also performs the following operations: When the pairing mode is one-to-many, each photovoltaic point is combined to obtain multiple combinations and determine the power generation sequence corresponding to the combination; According to the power generation sequence corresponding to the combination, the analysis group corresponding to each power consumption sequence is updated; Sort the load points by the number of paired generation sequences in the analysis group; The first-ranked electricity consumption sequence and its corresponding analysis group are extracted in turn, the matching degree between the electricity consumption sequence and each power generation sequence in the analysis group is calculated, and the electricity consumption sequence and power generation sequence with the greatest matching degree are paired; and the paired electricity consumption sequence and power generation sequence are deleted from the analysis group and the ranking; the pairing process is repeated until the electricity consumption sequence and / or power generation sequence are paired.

8. The distributed photovoltaic power station intelligent monitoring system according to claim 5, characterized in that: The scheduling analysis unit also performs the following operations: When the pairing mode is many-to-one, the analysis groups corresponding to each power usage sequence are updated according to the power generation sequence corresponding to the combination; Sort the load points by the number of paired generation sequences in the analysis group; Sequentially extract the first-ranked electricity consumption sequence and its corresponding analysis group, calculate the matching degree between the electricity consumption sequence and each power generation sequence in the analysis group, and pair the electricity consumption sequence with the power generation sequence with the greatest matching degree; and delete the paired electricity consumption sequence and power generation sequence from the analysis group and ranking, generate a new power generation sequence based on the paired electricity consumption sequence and power generation sequence, and screen the new power generation sequence based on the electricity consumption sequence to determine whether to add it to the corresponding analysis group; Repeat the pairing process until the power sequence is complete.

9. The distributed photovoltaic power station intelligent monitoring system according to claim 5, characterized in that: The scheduling analysis unit screens the power generation sequence according to the power consumption sequence, obtains the paired power generation sequence and constructs the corresponding analysis group, and performs the following operations: Obtain the loss coefficient between the load point corresponding to the electricity consumption sequence and the photovoltaic point corresponding to the power generation sequence; Correct each data in the power generation sequence according to the loss coefficient; Compare each data in the corrected power generation sequence with each corresponding data in the power consumption sequence. If both are greater than each other, the data passes the screening; otherwise, the data fails.

10. A distributed photovoltaic power station intelligent monitoring method, characterized in that: The distributed photovoltaic power station intelligent monitoring system according to any one of claims 1 to 9 is applied, comprising: Managing multiple connected distributed photovoltaic points and obtaining first monitoring data of each distributed photovoltaic point; Manage multiple connected load points and obtain the second monitoring data of each load point; Comprehensively analyze the first monitoring data and the second monitoring data, determine the scheduling plan and execute it.

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