An intelligent monitoring system for virtual power plants

Abstract

The present invention relates to the field of intelligent monitoring technology, and discloses an intelligent monitoring system for a virtual power plant. The parsing module is used to obtain original data in the virtual power plant and filter the original data to obtain data to be monitored. The identification module is used to generate possible safety identification information or danger identification information based on feature data of the data to be monitored and feature data in a preset safety library. The monitoring module is used to obtain attribute information of the data to be monitored that carries possible safety identification information, and calculate abnormal values of the data to be monitored based on the attribute information values. The early warning module is used to generate different early warning conditions for the monitored data based on the abnormal values and issue different early warning signals. The present invention solves the technical problem of being unable to perform intelligent monitoring of data in a virtual power plant, improves the efficiency and reliability of data monitoring, timely grasps data quality and issues early warnings when abnormalities are monitored, and is of great significance to power storage and power dispatching.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring system for a virtual power plant. Background Art

[0002] Virtual power plant is one of the important technologies for realizing smart distribution network. Virtual power plant refers to the use of distributed energy management system to merge the clean energy, controllable load and energy storage system installed in the distribution network into a special power plant to participate in the operation of the power grid, so as to coordinate the contradiction between smart grid and distributed energy, fully tap the value and benefits brought by distributed energy to the power grid and users, and ultimately ensure the optimal allocation of distribution network, optimal control of production quality, and optimal realization of economic and social benefits. When shared energy storage or power dispatching is carried out based on virtual power plant, a large amount of electricity consumption data, energy storage data and charging and discharging data will be collected. Therefore, how to analyze and monitor the data generated by virtual power plant is a technical problem that needs to be solved urgently.

[0003] Currently, virtual power plant data is monitored by relevant staff. However, since virtual power plants operate 24 hours a day, once the power plant operators are away from the virtual power plant console for a long time, they will not be able to detect potential problems in the virtual power plant operation process in a timely manner, which can easily lead to data loss or intrusion of abnormal data. In addition, the complexity and diversity of the data increase the workload of operation and maintenance personnel, resulting in incomplete monitoring and low efficiency.

[0004] Therefore, how to provide a system that can intelligently monitor the data generated by virtual power plants is a technical problem that needs to be solved. Summary of the Invention

[0005] An embodiment of the present invention provides an intelligent monitoring system for a virtual power plant, which is used to solve the technical problems in the prior art that the data generated in the virtual power plant cannot be intelligently monitored and alarm signals cannot be issued in real time for abnormal data.

[0006] To achieve the above objectives, the present invention provides an intelligent monitoring system for a virtual power plant, comprising:

[0007] The parsing module is used to obtain the original data generated in the virtual power plant and filter the original data according to the preset filtering rules to obtain the data to be monitored;

[0008] an identification module, configured to obtain characteristic data of the data to be monitored, perform feature matching based on the characteristic data of the data to be monitored and characteristic data in a preset safety library, and generate possible safety identification information or danger identification information for the data to be monitored based on the matching result;

[0009] A monitoring module is used to obtain attribute information of the data to be monitored that may carry security identification information, collect attribute information values corresponding to each attribute information, and calculate an abnormal value of the data to be monitored based on the attribute information values corresponding to each attribute information;

[0010] The early warning module is used to generate different early warning conditions for the data to be monitored based on abnormal values of the data to be monitored, and to issue different early warning signals according to different early warning conditions.

[0011] In one embodiment, when the identification module generates possible safety identification information or danger identification information for the data to be monitored according to the matching result, it includes:

[0012] The identification module is used to obtain the source IP address of the data to be monitored and the security source IP address in the preset security library, and determine whether the data to be monitored meets the first security condition based on the relationship between the source IP address and the security source IP address;

[0013] The identification module is configured to determine that the data to be monitored does not meet the first security condition when the source IP address does not meet the security source IP address, and generate the danger identification information for the data to be monitored;

[0014] The identification module is configured to determine, when the source IP address matches the secure source IP address, that the data to be monitored meets a first security condition, obtain a destination IP address of the data to be monitored and a secure destination IP address in the preset security library, and determine, based on a relationship between the destination IP address and the secure destination IP address, whether the data to be monitored meets a second security condition;

[0015] The identification module is configured to determine that the data to be monitored does not meet the second security condition when the destination IP address does not meet the security destination IP address, and generate danger identification information for the data to be monitored;

[0016] The identification module is configured to determine that the data to be monitored meets a second security condition when the destination IP address matches the security destination IP address, and to generate the possible security identification information for the data to be monitored.

[0017] In one embodiment, when the monitoring module calculates the abnormal value of the monitored data according to the attribute information value corresponding to each attribute information, it includes:

[0018] The monitoring module is used to perform standardization processing on the attribute information value corresponding to each attribute information, and obtain the attribute standard value corresponding to each attribute information value;

[0019] The monitoring module is used to calculate the abnormal value of the data to be monitored according to the attribute standard value corresponding to each attribute information value.

[0020] In one embodiment, when the monitoring module performs standardization processing on the attribute information value corresponding to each attribute information, it includes:

[0021] The monitoring module is used to compare the attribute information values corresponding to each attribute information, and determine a maximum value max and a minimum value min;

[0022] The monitoring module is used to perform standardization processing on the attribute information value corresponding to each attribute information according to the maximum value max and the minimum value min.

[0023] In one embodiment, when the monitoring module performs normalization processing on the attribute information value corresponding to each attribute information according to the maximum value max and the minimum value min, the process includes:

[0024] Calculate the attribute information impact value (max-min) / 2 of the attribute information value according to the maximum value max and the minimum value min;

[0025] Setting a normalization conversion coefficient of the attribute information value based on the attribute information influence value (max-min) / 2, and normalizing the attribute information value corresponding to each attribute information according to the normalization conversion coefficient;

[0026] Preset an attribute information influence value matrix B, set B(B1, B2, B3, B4), where B1 is a first preset attribute information influence value, B2 is a second preset attribute information influence value, B3 is a third preset attribute information influence value, and B4 is a fourth preset attribute information influence value, and B1<B2<B3<B4;

[0027] A preset normalized conversion coefficient matrix C is set to C(C1, C2, C3, C4, C5), where C1 is a first preset normalized conversion coefficient, C2 is a second preset normalized conversion coefficient, C3 is a third preset normalized conversion coefficient, C4 is a fourth preset normalized conversion coefficient, and C5 is a fifth preset normalized conversion coefficient, and C1 < C2 < C3 < C4 < C5;

[0028] The normalized conversion coefficient of the attribute information value is set according to the relationship between the attribute information impact value (max-min) / 2 and each preset attribute information impact value:

[0029] When (max-min) / 2<B1, the first preset normalization conversion coefficient C1 is selected as the normalization conversion coefficient of the attribute information value;

[0030] When B1≤(max-min) / 2<B2, the second preset normalization conversion coefficient C2 is selected as the normalization conversion coefficient of the attribute information value;

[0031] When B2≤(max-min) / 2<B3, the third preset normalization conversion coefficient C3 is selected as the normalization conversion coefficient of the attribute information value;

[0032] When B3≤(max-min) / 2<B4, the fourth preset normalization conversion coefficient C4 is selected as the normalization conversion coefficient of the attribute information value;

[0033] When B4≤(max-min) / 2, the fifth preset normalization conversion coefficient C5 is selected as the normalization conversion coefficient of the attribute information value.

[0034] In one embodiment, when the early warning module generates different early warning conditions for the data to be monitored based on abnormal values of the data to be monitored, it includes:

[0035] The early warning module is used to generate different early warning conditions for the data to be monitored according to the relationship between the abnormal value, the first preset abnormal value and the second preset abnormal value;

[0036] The early warning module is configured to generate a first early warning condition for the data to be monitored when the abnormal value is smaller than the first preset abnormal value, or when the abnormal value is larger than the second preset abnormal value;

[0037] The early warning module is configured to generate a second early warning condition for the data to be monitored when the abnormal value is greater than or equal to the first preset abnormal value and the abnormal value is less than or equal to the second preset abnormal value.

[0038] In one embodiment, when the early warning module issues a corresponding early warning signal according to different early warning conditions, it includes:

[0039] When the early warning module generates the first early warning condition for the data to be monitored, the early warning module calculates the second abnormal value of the data to be monitored according to the attribute standard value corresponding to each attribute information value;

[0040] The early warning module is used to calculate the deviation value corresponding to each attribute information according to the attribute information value corresponding to each attribute information and the attribute standard value corresponding to each attribute information value;

[0041] The early warning module is used to calculate the evaluation value corresponding to each attribute information according to the deviation value corresponding to each attribute information and the second abnormal value;

[0042] The early warning module is used to compare the evaluation values corresponding to each attribute information and determine a maximum value Y;

[0043] The early warning module is used to issue an early warning signal according to the selected maximum value Y.

[0044] In one embodiment, when the warning module issues a warning signal according to the selected maximum value, it includes:

[0045] When the maximum value Y is less than or equal to the first warning value, the warning module issues a third-level warning signal;

[0046] When the maximum value Y is greater than the first warning value and the maximum value Y is less than the second warning value, the warning module issues a secondary warning signal;

[0047] When the maximum value Y is greater than or equal to the second warning value, the warning module issues a first-level warning signal.

[0048] In one embodiment, when the warning module issues different warning signals according to different warning conditions, it further includes:

[0049] When the warning module generates a second warning condition for the data to be monitored, the warning module issues a warning signal according to the relationship between the maximum value max, the minimum value min, the first threshold value and the second threshold value;

[0050] When the maximum value max is less than or equal to the first threshold value, and the minimum value min is greater than or equal to the second threshold value, the warning module does not issue a warning signal;

[0051] When the maximum value max is less than or equal to the first threshold value, and the minimum value min is less than the second threshold value, the early warning module obtains the attribute information corresponding to the minimum value min, and uses the attribute information corresponding to the minimum value min as an early warning signal;

[0052] When the maximum value max is greater than the first threshold and the minimum value min is greater than or equal to the second threshold, the early warning module obtains attribute information corresponding to the maximum value max and uses the attribute information corresponding to the maximum value max as an early warning signal.

[0053] In one embodiment, it further includes:

[0054] The display module is used to receive the warning signal and display the warning signal to remind.

[0055] The present invention provides an intelligent monitoring system for a virtual power plant, which has the following advantages over the prior art:

[0056] The present invention discloses an intelligent monitoring system for a virtual power plant, comprising: a parsing module, an identification module, a monitoring module and an early warning module. The parsing module is used to obtain original data in the virtual power plant and filter the original data to obtain data to be monitored. The identification module is used to generate possible safety identification information or danger identification information based on characteristic data of the data to be monitored and characteristic data in a preset safety library. The monitoring module is used to obtain attribute information of the data to be monitored that carries possible safety identification information, and calculate abnormal values of the data to be monitored based on the attribute information values. The early warning module is used to generate different early warning conditions for the data to be monitored based on the abnormal values and issue different early warning signals. The present invention solves the technical problem of being unable to perform intelligent monitoring of data in a virtual power plant, improves the efficiency and reliability of data monitoring, timely grasps data quality and issues early warnings when abnormalities are monitored, and is of great significance to power storage and power dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A structural diagram of an intelligent monitoring system for a virtual power plant in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0058] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0059] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this 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 cannot be understood as a limitation on this application.

[0060] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0061] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0062] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0063] like Figure 1 As shown, an embodiment of the present invention discloses an intelligent monitoring system for a virtual power plant, including: an analysis module, an identification module, a monitoring module and an early warning module.

[0064] It should be understood that the parsing module is used to obtain the original data generated in the virtual power plant, and filter the original data according to preset filtering rules to obtain the data to be monitored; the identification module is used to obtain the characteristic data of the data to be monitored, perform feature matching based on the characteristic data of the data to be monitored and the characteristic data in the preset safety library, and generate possible safety identification information or danger identification information for the data to be monitored based on the matching results; the monitoring module is used to obtain the attribute information of the data to be monitored that carries possible safety identification information, collect the attribute information value corresponding to each attribute information, and calculate the abnormal value of the data to be monitored based on the attribute information value corresponding to each attribute information; the early warning module is used to generate different early warning conditions for the data to be monitored based on the abnormal value of the data to be monitored, and issue different early warning signals according to different early warning conditions.

[0065] It should be noted that the original data generated in the virtual power plant is obtained. The original data may include power generation data, wind power generation data, hydropower generation data, etc., and the original data is filtered according to preset filtering rules. The specific data filtering method is lengthy and mature, so it will not be introduced in detail here.

[0066] In this embodiment, the present invention solves the technical problem of being unable to intelligently monitor data in a virtual power plant, improves the efficiency and reliability of data monitoring, timely grasps data quality and issues early warnings when abnormalities are monitored, which is of great significance for power storage and power dispatching.

[0067] In some embodiments of the present application, when the identification module generates possible safety identification information or danger identification information for the data to be monitored according to the matching result, it includes:

[0068] The identification module is used to obtain the source IP address of the data to be monitored and the security source IP address in the preset security library, and determine whether the data to be monitored meets the first security condition based on the relationship between the source IP address and the security source IP address;

[0069] The identification module is configured to determine that the data to be monitored does not meet the first security condition when the source IP address does not meet the security source IP address, and generate the danger identification information for the data to be monitored;

[0070] The identification module is configured to determine, when the source IP address matches the secure source IP address, that the data to be monitored meets a first security condition, obtain a destination IP address of the data to be monitored and a secure destination IP address in the preset security library, and determine, based on a relationship between the destination IP address and the secure destination IP address, whether the data to be monitored meets a second security condition;

[0071] The identification module is configured to determine that the data to be monitored does not meet the second security condition when the destination IP address does not meet the security destination IP address, and generate danger identification information for the data to be monitored;

[0072] The identification module is configured to determine that the data to be monitored meets a second security condition when the destination IP address matches the security destination IP address, and to generate the possible security identification information for the data to be monitored.

[0073] In this embodiment, the characteristic data of the data to be monitored include the source IP address and destination IP address of the data to be monitored. Whether the data to be monitored meets the first security condition is determined based on the relationship between the source IP address and the secure source IP address. If not, it means that the source of the data to be monitored is unknown and there is a security risk. Danger identification information is generated for the data to be monitored. When the source IP address meets the secure source IP address, whether the data to be monitored meets the second security condition is determined based on the destination IP address and the secure destination IP address in the preset security library. If so, it is determined that the data to be monitored may generate possible security identification information. At this time, the source IP address and destination IP address of the data to be monitored are both safe, and the data to be monitored can be received. When the present invention generates danger identification information for the data to be monitored, relevant staff need to determine whether the source IP address of the data to be monitored is safe. If so, it continues to determine whether the destination IP address of the data to be monitored is safe. The present invention can perform security judgment on the data to be monitored and avoid receiving data of unknown origin.

[0074] In some embodiments of the present application, when the monitoring module calculates the abnormal value of the data to be monitored according to the attribute information value corresponding to each attribute information, it includes:

[0075] The monitoring module is used to perform standardization processing on the attribute information value corresponding to each attribute information, and obtain the attribute standard value corresponding to each attribute information value;

[0076] The monitoring module is used to calculate the abnormal value of the data to be monitored according to the attribute standard value corresponding to each attribute information value.

[0077] In some embodiments of the present application, when the monitoring module performs standardization processing on the attribute information value corresponding to each attribute information, it includes:

[0078] The monitoring module is used to compare the attribute information values corresponding to each attribute information, and determine a maximum value max and a minimum value min;

[0079] The monitoring module is used to perform standardization processing on the attribute information value corresponding to each attribute information according to the maximum value max and the minimum value min.

[0080] In some embodiments of the present application, when the monitoring module performs normalization processing on the attribute information value corresponding to each attribute information according to the maximum value max and the minimum value min, the process includes:

[0081] Calculate the attribute information impact value (max-min) / 2 of the attribute information value according to the maximum value max and the minimum value min;

[0082] Setting a normalization conversion coefficient of the attribute information value based on the attribute information influence value (max-min) / 2, and normalizing the attribute information value corresponding to each attribute information according to the normalization conversion coefficient;

[0083] Preset an attribute information influence value matrix B, set B(B1, B2, B3, B4), where B1 is a first preset attribute information influence value, B2 is a second preset attribute information influence value, B3 is a third preset attribute information influence value, and B4 is a fourth preset attribute information influence value, and B1<B2<B3<B4;

[0084] A preset normalized conversion coefficient matrix C is set to C(C1, C2, C3, C4, C5), where C1 is a first preset normalized conversion coefficient, C2 is a second preset normalized conversion coefficient, C3 is a third preset normalized conversion coefficient, C4 is a fourth preset normalized conversion coefficient, and C5 is a fifth preset normalized conversion coefficient, and C1 < C2 < C3 < C4 < C5;

[0085] The normalized conversion coefficient of the attribute information value is set according to the relationship between the attribute information impact value (max-min) / 2 and each preset attribute information impact value:

[0086] When (max-min) / 2<B1, the first preset normalization conversion coefficient C1 is selected as the normalization conversion coefficient of the attribute information value;

[0087] When B1≤(max-min) / 2<B2, the second preset normalization conversion coefficient C2 is selected as the normalization conversion coefficient of the attribute information value;

[0088] When B2≤(max-min) / 2<B3, the third preset normalization conversion coefficient C3 is selected as the normalization conversion coefficient of the attribute information value;

[0089] When B3≤(max-min) / 2<B4, the fourth preset normalization conversion coefficient C4 is selected as the normalization conversion coefficient of the attribute information value;

[0090] When B4≤(max-min) / 2, the fifth preset normalization conversion coefficient C5 is selected as the normalization conversion coefficient of the attribute information value.

[0091] In this embodiment, attribute information of the data to be monitored that carries possible safety identification information is obtained, wherein the attribute information may include electric power generation, wind power generation, hydropower generation and thermal power generation, and the attribute information value corresponding to each attribute information is the data corresponding to the attribute information, such as electric power generation generates 40,000 degrees in one day, wind power generation generates 38,000 degrees in one day, hydropower generation generates 42,000 degrees in one day, and thermal power generation generates 36,000 degrees in one day. The attribute information value corresponding to each attribute information is standardized, and the attribute standard value corresponding to each attribute information value is obtained. The attribute information value corresponding to each attribute information is numerically compared, and a maximum value max and a minimum value min are determined, the maximum value is 42,000 degrees, and the minimum value is 36,000 degrees.

[0092] In this embodiment, if electric power generation is standardized, there are 40000*C i, i=1, 2, 3, 4, 5. Other attribute information can be calculated according to the same method and are not shown one by one here. The abnormal value of the data to be monitored is calculated according to the attribute standard value corresponding to each attribute information value. For example, the attribute standard value of electric power generation is X1, wind power generation is X2, hydropower generation is X3, and thermal power generation is X4. The abnormal value of the data to be monitored is (X1+X2+X3+X4) / 4. By performing standardized processing, the present invention can simplify the calculation process, reduce data monitoring errors, and improve data monitoring accuracy. By calculating the abnormal values of the data to be monitored, reliable data support can be provided for intelligent data monitoring.

[0093] In some embodiments of the present application, when the early warning module generates different early warning conditions for the data to be monitored based on abnormal values of the data to be monitored, it includes:

[0094] The early warning module is used to generate different early warning conditions for the data to be monitored according to the relationship between the abnormal value, the first preset abnormal value and the second preset abnormal value;

[0095] The early warning module is configured to generate a first early warning condition for the data to be monitored when the abnormal value is smaller than the first preset abnormal value, or when the abnormal value is larger than the second preset abnormal value;

[0096] The early warning module is configured to generate a second early warning condition for the data to be monitored when the abnormal value is greater than or equal to the first preset abnormal value and the abnormal value is less than or equal to the second preset abnormal value.

[0097] In this embodiment, different warning conditions are generated for the monitored data based on the relationship between the abnormal value, the first preset abnormal value and the second preset abnormal value, wherein the first preset abnormal value and the second preset abnormal value can be set according to actual conditions. It should be noted that the second preset abnormal value is greater than the first preset abnormal value. When the abnormal value is less than the first preset abnormal value, or the abnormal value is greater than the second preset abnormal value, the first warning condition is generated for the monitored data. When the abnormal value is greater than or equal to the first preset abnormal value and the abnormal value is less than or equal to the second preset abnormal value, the second warning condition is generated for the monitored data. By generating different warning conditions according to different situations, the present invention can issue a warning in real time when the data is abnormal, thereby improving the warning efficiency.

[0098] In some embodiments of the present application, when the early warning module issues a corresponding early warning signal according to different early warning conditions, it includes:

[0099] When the early warning module generates the first early warning condition for the data to be monitored, the early warning module calculates the second abnormal value of the data to be monitored according to the attribute standard value corresponding to each attribute information value;

[0100] The early warning module is used to calculate the deviation value corresponding to each attribute information according to the attribute information value corresponding to each attribute information and the attribute standard value corresponding to each attribute information value;

[0101] The early warning module is used to calculate the evaluation value corresponding to each attribute information according to the deviation value corresponding to each attribute information and the second abnormal value;

[0102] The early warning module is used to compare the evaluation values corresponding to each attribute information and determine a maximum value Y;

[0103] The early warning module is used to issue an early warning signal according to the selected maximum value Y.

[0104] In this embodiment, when the first warning condition is generated for the monitored data, the second abnormal value of the monitored data is calculated according to the attribute standard value. The second abnormal value can be calculated according to the following formula: s = ((X1-X) 2 +(X2-X) 2 +(X3-X) 2 +(X4-X) 2 ) / 4, calculate the deviation value corresponding to each attribute information according to the attribute information value and the attribute standard value, that is, calculate the absolute value of the difference between the attribute information value and the attribute standard value, calculate the evaluation value corresponding to each attribute information according to the deviation value corresponding to each attribute information and the second abnormal value, and calculate the evaluation value corresponding to each attribute information according to the following formula: W=r / s, wherein W is the evaluation value corresponding to each attribute information, r is the deviation value corresponding to each attribute information, and s is the second abnormal value. The evaluation values corresponding to each attribute information are numerically compared, and a maximum value Y is determined. An early warning signal is issued according to the maximum value Y. The present invention can generate different early warning signals for the monitored data, thereby improving the processing efficiency of the staff.

[0105] In some embodiments of the present application, when the warning module issues a warning signal according to the selected maximum value, it includes:

[0106] When the maximum value Y is less than or equal to the first warning value, the warning module issues a third-level warning signal;

[0107] When the maximum value Y is greater than the first warning value and the maximum value Y is less than the second warning value, the warning module issues a secondary warning signal;

[0108] When the maximum value Y is greater than or equal to the second warning value, the warning module issues a first-level warning signal.

[0109] In this embodiment, the first warning value and the second warning value can be set according to actual conditions and are not specifically limited here. It should be noted that the second warning value is greater than the first warning value. When the maximum value Y is larger, the higher the degree of abnormality of the monitored data, the higher the corresponding warning signal level, thereby shortening the processing time.

[0110] In some embodiments of the present application, when the warning module issues different warning signals according to different warning conditions, it further includes:

[0111] When the warning module generates a second warning condition for the data to be monitored, the warning module issues a warning signal according to the relationship between the maximum value max, the minimum value min, the first threshold value and the second threshold value;

[0112] When the maximum value max is less than or equal to the first threshold value, and the minimum value min is greater than or equal to the second threshold value, the warning module does not issue a warning signal;

[0113] When the maximum value max is less than or equal to the first threshold value, and the minimum value min is less than the second threshold value, the early warning module obtains the attribute information corresponding to the minimum value min, and uses the attribute information corresponding to the minimum value min as an early warning signal;

[0114] When the maximum value max is greater than the first threshold and the minimum value min is greater than or equal to the second threshold, the early warning module obtains attribute information corresponding to the maximum value max and uses the attribute information corresponding to the maximum value max as an early warning signal.

[0115] In this embodiment, when the second warning condition is generated for the monitored data, a warning signal is issued based on the relationship between the maximum value max, the minimum value min, the first threshold value and the second threshold value, wherein the first threshold value and the second threshold value can be specifically set according to the maximum value max and the minimum value min. It should be noted that the first threshold value is greater than the second threshold value. When the maximum value max is less than or equal to the first threshold value and the minimum value min is greater than or equal to the second threshold value, all data are in a normal range and no warning signal is issued. When the maximum value max is less than or equal to the first threshold value and the minimum value min is less than the second threshold value, the attribute information corresponding to the minimum value min (such as wind power generation, thermal power generation, etc.) is obtained, and the attribute information corresponding to the minimum value min is used as a warning signal. For example, if the minimum value min is 26,000 kWh, the second threshold value is 36,000 kWh, and the minimum value min is 0. It does not belong to the normal power generation range. If the minimum value min corresponds to wind power generation, wind power generation is used as an early warning signal for early warning. The staff will detect the wind power generation equipment. When the maximum value max is greater than the first threshold and the minimum value min is greater than or equal to the second threshold, the attribute information corresponding to the maximum value max is obtained, and the attribute information corresponding to the maximum value max is used as an early warning signal. For example, if the maximum value max is 50,000 kWh, the first threshold is 45,000 kWh. At this time, the maximum value max does not belong to the normal power generation range. If the maximum value max corresponds to wind power generation, wind power generation is used as an early warning signal for early warning. It is determined whether there is a data recording error. The present invention can not only ensure the normal transmission of virtual power plant data, but also prevent the occurrence of data anomalies, timely grasp the data quality and issue an early warning when monitoring anomalies, which is of great significance to power storage and power dispatching.

[0116] In some embodiments of the present application, it also includes: a display module for receiving the warning signal and displaying the warning signal as a reminder, which can remind the staff in real time to avoid omissions.

[0117] In summary, the embodiments of the present invention include: a parsing module, an identification module, a monitoring module and an early warning module. The parsing module is used to obtain the original data in the virtual power plant and filter the original data to obtain the data to be monitored. The identification module is used to generate possible safety identification information or danger identification information based on the characteristic data of the data to be monitored and the characteristic data in the preset safety library. The monitoring module is used to obtain the attribute information of the data to be monitored that carries possible safety identification information, and calculate the abnormal value of the data to be monitored according to the attribute information value. The early warning module is used to generate different early warning conditions for the data to be monitored according to the abnormal value and issue different early warning signals. The present invention solves the technical problem that it is impossible to perform intelligent monitoring of data in the virtual power plant, improves the efficiency and reliability of data monitoring, timely grasps the data quality and issues early warnings when monitoring abnormalities, which is of great significance to power storage and power dispatching.

[0118] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0119] Although the present invention has been described above with reference to exemplary embodiments, various modifications may be made and equivalent components may be substituted without departing from the scope of the present invention. In particular, the various features of the disclosed embodiments may be combined with one another in any manner, provided no structural conflicts exist. These combinations are not fully described in this specification solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but encompasses all technical solutions within the scope of the claims.

[0120] Those skilled in the art will understand that the above are merely 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 will still be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An intelligent monitoring system for a virtual power plant, characterized in that: include: The parsing module is used to obtain the original data generated in the virtual power plant and filter the original data according to the preset filtering rules to obtain the data to be monitored; an identification module, configured to obtain characteristic data of the data to be monitored, perform feature matching based on the characteristic data of the data to be monitored and characteristic data in a preset safety library, and generate possible safety identification information or danger identification information for the data to be monitored based on the matching result; A monitoring module is used to obtain attribute information of the data to be monitored that may carry security identification information, collect attribute information values corresponding to each attribute information, and calculate an abnormal value of the data to be monitored based on the attribute information values corresponding to each attribute information; An early warning module, configured to generate different early warning conditions for the data to be monitored based on abnormal values of the data to be monitored, and to issue different early warning signals according to different early warning conditions; When the monitoring module calculates the abnormal value of the data to be monitored according to the attribute information value corresponding to each attribute information, it includes: The monitoring module is used to perform standardization processing on the attribute information value corresponding to each attribute information, and obtain the attribute standard value corresponding to each attribute information value; The monitoring module is used to calculate the abnormal value of the data to be monitored according to the attribute standard value corresponding to each attribute information value; When the monitoring module performs standardization processing on the attribute information value corresponding to each attribute information, it includes: The monitoring module is used to compare the attribute information values corresponding to each attribute information, and determine a maximum value max and a minimum value min; The monitoring module is used to perform normalization processing on the attribute information value corresponding to each attribute information according to the maximum value max and the minimum value min; When the monitoring module performs normalization processing on the attribute information value corresponding to each attribute information according to the maximum value max and the minimum value min, the process includes: Calculate the attribute information impact value (max-min) / 2 of the attribute information value according to the maximum value max and the minimum value min; Setting a normalization conversion coefficient of the attribute information value based on the attribute information influence value (max-min) / 2, and normalizing the attribute information value corresponding to each attribute information according to the normalization conversion coefficient; Preset an attribute information influence value matrix B, set B(B1, B2, B3, B4), where B1 is a first preset attribute information influence value, B2 is a second preset attribute information influence value, B3 is a third preset attribute information influence value, and B4 is a fourth preset attribute information influence value, and B1<B2<B3<B4; A preset normalized conversion coefficient matrix C is set to C(C1, C2, C3, C4, C5), where C1 is a first preset normalized conversion coefficient, C2 is a second preset normalized conversion coefficient, C3 is a third preset normalized conversion coefficient, C4 is a fourth preset normalized conversion coefficient, and C5 is a fifth preset normalized conversion coefficient, and C1 < C2 < C3 < C4 < C5; The normalized conversion coefficient of the attribute information value is set according to the relationship between the attribute information impact value (max-min) / 2 and each preset attribute information impact value: When (max-min) / 2<B1, the first preset normalization conversion coefficient C1 is selected as the normalization conversion coefficient of the attribute information value; When B1≤(max-min) / 2<B2, the second preset normalization conversion coefficient C2 is selected as the normalization conversion coefficient of the attribute information value; When B2≤(max-min) / 2<B3, the third preset normalization conversion coefficient C3 is selected as the normalization conversion coefficient of the attribute information value; When B3≤(max-min) / 2<B4, the fourth preset normalization conversion coefficient C4 is selected as the normalization conversion coefficient of the attribute information value; When B4≤(max-min) / 2, the fifth preset normalization conversion coefficient C5 is selected as the normalization conversion coefficient of the attribute information value.

2. The intelligent monitoring system for a virtual power plant according to claim 1, characterized in that: When the identification module generates possible safety identification information or danger identification information for the data to be monitored according to the matching result, it includes: The identification module is used to obtain the source IP address of the data to be monitored and the security source IP address in the preset security library, and determine whether the data to be monitored meets the first security condition based on the relationship between the source IP address and the security source IP address; The identification module is configured to determine that the data to be monitored does not meet the first security condition when the source IP address does not meet the security source IP address, and generate the danger identification information for the data to be monitored; The identification module is configured to determine, when the source IP address matches the secure source IP address, that the data to be monitored meets a first security condition, obtain a destination IP address of the data to be monitored and a secure destination IP address in the preset security library, and determine, based on a relationship between the destination IP address and the secure destination IP address, whether the data to be monitored meets a second security condition; The identification module is configured to determine that the data to be monitored does not meet the second security condition when the destination IP address does not meet the security destination IP address, and generate danger identification information for the data to be monitored; The identification module is configured to determine that the data to be monitored meets a second security condition when the destination IP address matches the security destination IP address, and to generate the possible security identification information for the data to be monitored.

3. The intelligent monitoring system for a virtual power plant according to claim 1, characterized in that: When the early warning module generates different early warning conditions for the data to be monitored based on the abnormal value of the data to be monitored, it includes: The early warning module is used to generate different early warning conditions for the data to be monitored according to the relationship between the abnormal value, the first preset abnormal value and the second preset abnormal value; The early warning module is configured to generate a first early warning condition for the data to be monitored when the abnormal value is less than the first preset abnormal value, or when the abnormal value is greater than the second preset abnormal value; The early warning module is configured to generate a second early warning condition for the data to be monitored when the abnormal value is greater than or equal to the first preset abnormal value and the abnormal value is less than or equal to the second preset abnormal value.

4. The intelligent monitoring system for a virtual power plant according to claim 3, characterized in that: When the early warning module issues corresponding early warning signals according to different early warning conditions, it includes: When the early warning module generates the first early warning condition for the data to be monitored, the early warning module calculates the second abnormal value of the data to be monitored according to the attribute standard value corresponding to each attribute information value; The early warning module is used to calculate the deviation value corresponding to each attribute information according to the attribute information value corresponding to each attribute information and the attribute standard value corresponding to each attribute information value; The early warning module is used to calculate the evaluation value corresponding to each attribute information according to the deviation value corresponding to each attribute information and the second abnormal value; The early warning module is used to compare the evaluation values corresponding to each attribute information and determine a maximum value Y; The early warning module is used to issue an early warning signal according to the selected maximum value Y.

5. The intelligent monitoring system for virtual power plants according to claim 4 is characterized in that: When the warning module issues a warning signal according to the selected maximum value, it includes: When the maximum value Y is less than or equal to the first warning value, the warning module issues a third-level warning signal; When the maximum value Y is greater than the first warning value and the maximum value Y is less than the second warning value, the warning module issues a secondary warning signal; When the maximum value Y is greater than or equal to the second warning value, the warning module issues a first-level warning signal.

6. The intelligent monitoring system for virtual power plants according to claim 3, characterized in that: When the early warning module issues different early warning signals according to different early warning conditions, it also includes: When the warning module generates a second warning condition for the data to be monitored, the warning module issues a warning signal according to the relationship between the maximum value max, the minimum value min, the first threshold value and the second threshold value; When the maximum value max is less than or equal to the first threshold value, and the minimum value min is greater than or equal to the second threshold value, the warning module does not issue a warning signal; When the maximum value max is less than or equal to the first threshold value, and the minimum value min is less than the second threshold value, the early warning module obtains the attribute information corresponding to the minimum value min, and uses the attribute information corresponding to the minimum value min as an early warning signal; When the maximum value max is greater than the first threshold and the minimum value min is greater than or equal to the second threshold, the early warning module obtains attribute information corresponding to the maximum value max and uses the attribute information corresponding to the maximum value max as an early warning signal.

7. The intelligent monitoring system for a virtual power plant according to claim 1, characterized in that: Also includes: The display module is used to receive the warning signal and display the warning signal to remind.

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