Data monitoring analysis method and system based on distributed power supply network connection

Abstract

The invention discloses a data monitoring analysis method and system based on distributed power supply networking, and relates to the technical field of power grid data online monitoring, and the method comprises the steps: recognizing the monitoring attribute of each data monitoring point in a monitoring network, and determining the configuration collection frequency of the data monitoring point according to the monitoring information; acquiring a pre-acquisition frequency sequence based on the configuration acquisition frequencies of all the data monitoring points; acquiring an actual acquisition frequency sequence of all data monitoring points; and comparing the acquisition frequency change parameter of each data monitoring point according to the pre-acquisition frequency sequence and the real acquisition frequency sequence, judging whether each acquisition frequency change parameter is abnormal or not by combining the change condition of the monitoring attribute of the corresponding data monitoring point, and if so, sending out alarm information. According to the method and the system, the originally configured acquisition frequencies and the real-time acquisition frequencies of different data monitoring points are compared and analyzed, and whether reasonable changes exist or not is judged according to the analysis result to give an alarm in time, so that the reliability of power grid data monitoring is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of on-line monitoring of power grid data, and more specifically, to a data monitoring and analysis method and system based on the connection of distributed power sources to the grid. Background Art

[0002] When connecting small-scale power generation equipment with a decentralized layout to the public power grid, technical means and management measures are required to ensure the stable operation of the power grid, the safe and reliable operation of the equipment, and to avoid harm to the power grid and users. The design of the technical means and management measures needs to achieve the goals of "observable, measurable, controllable, and adjustable". Among them, measurable means to carry out minute-level collection of low-voltage distributed photovoltaic user data to achieve real-time perception, operation monitoring, and abnormal analysis of low-voltage distributed photovoltaic power generation. However, the current data collection system often fails to achieve minute-level data collection, resulting in insufficient real-time perception ability of key data such as the operating status and power generation of distributed power sources. In the prior art, generally, two aspects are addressed: improving the data collection frequency (increasing the data collection frequency from once per hour or every 15 minutes to the minute level or even the second level to ensure that changes in the operating status of distributed power sources can be captured in real time) and matching and optimizing the data transmission path (reducing intermediate nodes in data transmission, using direct connection or efficient communication protocols to reduce data transmission delay). However, increasing the data collection frequency will significantly increase the amount of data, resulting in problems such as data storage pressure, consumption of computing resources, and risk of response delay. Therefore, it is necessary to consider how to balance the requirements for data processing and response on the basis of increasing the data collection frequency. The key lies in configuring different collection frequencies for different monitoring points and performing real-time monitoring and reasonable analysis based on whether the collection frequency changes to fill the technical gap. Summary of the Invention

[0003] The purpose of the present invention is to provide a data monitoring and analysis method and system based on the connection of distributed power sources to the grid. The method and system compare and analyze the originally configured collection frequency and the real-time collection frequency of different data monitoring points, and determine whether there is a reasonable change based on the analysis result to give a timely warning, thereby ensuring the reliability of power grid data monitoring.

[0004] The embodiments of the present invention are implemented as follows:

[0005] First aspect, a data monitoring and analysis method based on the connection of distributed power sources to the grid, includes the following steps: Locate all data monitoring points in the monitoring network, identify the monitoring attributes of each data monitoring point to obtain the monitoring information of this data monitoring point, and determine the configured acquisition frequency of this data monitoring point according to the monitoring information, where the monitoring attributes include monitoring scenarios, monitoring real-time requirements, and monitoring application types; Perform priority sorting based on the configured acquisition frequencies of all data monitoring points to obtain a pre-acquisition frequency sequence of all data monitoring points; Obtain the real-time acquisition frequencies of all data monitoring points and perform priority sorting to obtain a real-acquisition frequency sequence of all data monitoring points; Compare the acquisition frequency change parameters of each data monitoring point according to the pre-acquisition frequency sequence and the real-acquisition frequency sequence, and determine whether each acquisition frequency change parameter is abnormal. If it is abnormal, an alarm message is sent; where the acquisition frequency change parameters include acquisition frequency values, sequence position change values, and associated point distance change values; When determining whether the acquisition frequency change parameters are abnormal, the determination is made in combination with the change situation of the monitoring attributes of the corresponding data monitoring point.

[0006] In some optional implementation manners, when determining whether the acquisition frequency change parameter is abnormal, the determination in combination with the change situation of the monitoring attributes of the corresponding data monitoring point includes the following steps: Obtain the monitoring scenario change, monitoring real-time requirement change parameter, and monitoring application type change parameter of this data monitoring point between the previous monitoring period and the current monitoring period, and calculate the total change parameter according to the monitoring scenario change parameter, monitoring real-time requirement change parameter, and monitoring application type change parameter; Determine the monitoring attribute change type of this data monitoring point according to the total change parameter, and then determine whether it is abnormal after combining the monitoring attribute change type with the acquisition frequency change parameter; where the monitoring attribute change types include no change and change.

[0007] In some optional implementation manners, when the monitoring attribute change type is a change, further determine the rationality of the associated impact of the monitoring attribute change type on the acquisition frequency change parameter: Obtain the pre-acquisition frequency sequence after the monitoring attribute change of this data monitoring point in the current monitoring period, denoted as the changed pre-acquisition frequency sequence; Calculate the acquisition frequency change parameter of this data monitoring point between the changed pre-acquisition frequency sequence and the real-time frequency acquisition sequence; Calculate the first difference result according to the change situation of the acquisition frequency value, calculate the second difference result according to the change situation of the sequence position change value, and calculate the third difference result according to the change situation of the associated point distance change value; Calculate the theoretical second difference result and the theoretical third difference result according to the first difference result; Determine the rationality according to the second comparison value between the second difference result and the theoretical second difference result and the third comparison value between the third difference result and the theoretical third difference result.

[0008] In some alternative embodiments, the judgment of the rationality based on the second comparison value between the second difference result and the theoretical second difference result and the third comparison value between the third difference result and the theoretical third difference result specifically includes the following steps: determining a first rationality parameter according to the specific values of the second comparison value and the third comparison value; determining a second rationality parameter according to the difference between the second comparison value and the third comparison value; judging the rationality according to the first rationality parameter and the second rationality parameter; wherein, when the difference between the second comparison value and the third comparison value is not 0, a truth judgment step for the third comparison value is required: calculating the sequence bit change value of the associated point, comparing the change value with the difference between the second comparison value and the third comparison value, and judging the truth according to the comparison result.

[0009] In some alternative embodiments, the obtaining of the pre-acquisition frequency sequence of the data monitoring point after the monitoring attribute changes in this monitoring period includes the following steps: obtaining the change monitoring information of the data monitoring point after the monitoring attribute changes in this monitoring period; determining the pre-configured acquisition frequency of the data monitoring point according to the change monitoring information; performing a priority ranking on the pre-configured acquisition frequency and the configured acquisition frequencies of the remaining data monitoring points to obtain the pre-acquisition frequency sequence.

[0010] In some alternative embodiments, the determining of the pre-configured acquisition frequency of the data monitoring point according to the change monitoring information includes the following steps: obtaining the configured acquisition frequencies of the data monitoring point in the previous monitoring period and this monitoring period, denoted as the first configured acquisition frequency and the second configured acquisition frequency, respectively determining a first merging weight and a second merging weight, and adding the product of the first merging weight and the first configured acquisition frequency and the product of the second merging weight and the second configured acquisition frequency to obtain the pre-configured acquisition frequency; wherein, the first merging weight is determined according to the historical change situation of the configured acquisition frequency of the data monitoring point, and the second merging weight is determined according to the difference between the value 1 and the first merging weight.

[0011] In some alternative embodiments, determining the first merging weight includes the following steps: determining the basic value of the first merging weight according to the historical change situation of the configured acquisition frequency of the data monitoring point; determining a first adjustment parameter according to the historical change situation of the monitoring scenario, determining a second adjustment parameter according to the historical change situation of the monitoring real-time requirement, and determining a third adjustment parameter according to the historical change situation of the monitoring application type; merging the basic value, the first adjustment parameter, the second adjustment parameter and the third adjustment parameter into the first merging weight.

[0012] In some alternative embodiments, the steps for identifying the monitoring attributes of each data monitoring point to obtain the monitoring information of the data monitoring point and determining the configured acquisition frequency of the data monitoring point according to the monitoring information are as follows: determining an acquisition frequency range according to the monitoring scenario of the data monitoring point; narrowing the acquisition frequency range according to the monitoring real-time requirement of the data monitoring point to obtain a quasi-acquisition frequency range; and determining a specific value within the quasi-acquisition frequency range according to the monitoring application type of the data monitoring point as the configured acquisition frequency.

[0013] In some alternative embodiments, after determining the acquisition frequency range according to the monitoring scenario of the data monitoring point, the following steps are further included: counting the historical monitoring scenario types of the data monitoring point in each monitoring period, and adjusting the acquisition frequency range according to the statistical situation of all historical monitoring scenario types, and using the adjusted acquisition frequency range to participate in the process of determining the configured acquisition frequency.

[0014] In a second aspect, a data monitoring and analysis system based on the connection of distributed power sources includes:

[0015] A first acquisition unit, which is used to locate all data monitoring points in the monitoring network, identify the monitoring attributes of each data monitoring point to obtain the monitoring information of the data monitoring point, and determine the configured acquisition frequency of the data monitoring point according to the monitoring information, where the monitoring attributes include monitoring scenario, monitoring real-time requirement, and monitoring application type;

[0016] A first processing unit, which is used to perform priority sorting based on the configured acquisition frequencies of all data monitoring points to obtain a pre-acquisition frequency sequence of all data monitoring points;

[0017] A second acquisition unit, which is used to obtain the real-time acquisition frequencies of all data monitoring points and perform priority sorting to obtain a real-acquisition frequency sequence of all data monitoring points;

[0018] A first calculation unit, which is used to compare the acquisition frequency change parameters of each data monitoring point according to the pre-acquisition frequency sequence and the real-acquisition frequency sequence, and determine whether each acquisition frequency change parameter is abnormal. If it is abnormal, an alarm message is sent; where the acquisition frequency change parameters include acquisition frequency value, sequence bit change value, and associated point distance change value; and when determining whether the acquisition frequency change parameter is abnormal, it is determined in combination with the change situation of the monitoring attributes of the corresponding data monitoring point.

[0019] The beneficial effects of the embodiments of the present invention are:

[0020] The data monitoring and analysis method and system based on the connection of distributed power sources provided by the embodiments of the present invention obtain in advance the monitoring attributes of all data monitoring points in the monitoring network, determine the pre-configured data collection frequency of each data monitoring point according to the monitoring information included in different monitoring attributes, then obtain the actual collection frequency of each data monitoring point, compare the configured collection frequency with the actual collection frequency, and judge whether there is an abnormal frequency collection through the change in the difference value of the collection frequency value, so as to issue an alarm. Moreover, on the basis of the difference comparison of the collection frequency values, the pre-collection frequency sequence and the actual collection frequency sequence are generated to judge the change value of the sequence position and the change value of the associated point distance, so as to judge whether the change in the collection frequency value is reliable and increase the accuracy of the abnormal analysis result. In addition, when performing abnormal analysis and judgment, in addition to directly comparing and referring to the change in the collection frequency value, it is also further determined whether the monitoring attribute has changed, so as to assist in judging whether the change in the collection frequency value is accurate and reliable, achieving the purpose of auxiliary comparison, so as to ensure the reliability of the final abnormal analysis result.

[0021] Generally speaking, the data monitoring and analysis method and system based on the connection of distributed power sources provided by the embodiments of the present invention analyze the configured and actual collection frequency values of the same data monitoring point, and combine sequence reference comparison and whether the configuration has changed for auxiliary comparison. Considering these two aspects, it can improve the rationality of the abnormal judgment of the collection frequency change of the data monitoring point and greatly enhance the alarm ability of the normal operation of the power grid data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a flowchart of the main steps of the analysis method provided by the embodiments of the present invention;

[0024] Figure 2 For Figure 1 It is a flowchart of one of the steps S400 of the shown main steps;

[0025] Figure 3 For Figure 1 It is a flowchart of one of the steps S100 of the shown main steps;

[0026] Figure 4 It is a modular schematic diagram of the analysis system provided by the embodiments of the present invention.

[0027] Icons: 500 - Analysis system; 510 - First acquisition unit; 520 - First processing unit; 530 - Second acquisition unit; 540 - First calculation unit. Detailed implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Generally, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0030] It should be understood that the "system", "device", and / or "module" used in the present invention is a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0031] As shown in the present invention and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0032] Flowcharts are used in the present invention to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in sequence. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0033] Embodiment

[0034] In the process of the safe connection of distributed power sources to the grid, the key design concept is to achieve the goals of being observable (deploying low-voltage distributed photovoltaic micro-applications to realize the panoramic visualization display of low-voltage distributed photovoltaic statistical data, operating status, regulation and control, and abnormal alarms), measurable (collecting low-voltage distributed photovoltaic user data at the minute level to realize the real-time perception, operation monitoring, and abnormal analysis of low-voltage distributed photovoltaic power generation), controllable (applying photovoltaic special circuit breakers to establish rigid control capabilities to realize the rigid controllability of all low-voltage distributed photovoltaic users), and adjustable (applying distributed power access units / smart IoT electricity meters to establish flexible adjustment capabilities to realize the flexible adjustment of low-voltage distributed photovoltaic users). Among them, for the measurable aspect, how to achieve minute-level data collection is a relatively crucial link. Currently, the prominent problems encountered in the power grid include low data collection frequency, large data transmission delay, limited data processing capacity, etc. These problems often prevent the data system from achieving minute-level data collection. To address this requirement, it can start from the main aspects such as increasing the data collection frequency and optimizing the data transmission path. However, it is found in the implementation process that increasing the data collection frequency will significantly increase the data volume, thus bringing problems such as data storage pressure, consumption of computing resources, and risk of response delay.

[0035] To alleviate the pressure of significantly increasing the data volume caused by increasing the data collection frequency, it is necessary to configure different collection frequencies for different data monitoring points, so as to achieve the optimal planning of the data monitoring and collection strategy for the entire monitoring network. Therefore, how to configure the collection frequency for different data monitoring points is one of the key issues. Under this problem, even if different collection frequencies are configured for different data monitoring points, in the actual operation process, the same data monitoring point may also collect data according to a collection frequency different from the pre-configured one, thus changing the planning optimality of the data monitoring and collection strategy. The reason may be a temporary fault change or a human adjustment change. That is, on this basis, how to ensure the planning optimality of the data monitoring and collection strategy after the collection frequency of the data monitoring point changes is also a relatively crucial problem. Therefore, this embodiment provides a data monitoring and analysis method based on the connection of distributed power sources to the grid, which can perform real-time monitoring and analysis on the problems in the foregoing directions, so as to fill the technical blank in the aspect that it is impossible to further determine whether it is an abnormal change due to the change of the collection frequency. For details, please refer to Figure 1 , the data monitoring and analysis method based on the connection of distributed power sources to the grid provided by this embodiment includes the following steps:

[0036] S100: Locate all data monitoring points in the monitoring network, identify the monitoring attributes of each data monitoring point to obtain the monitoring information of this data monitoring point, and determine the configured acquisition frequency of this data monitoring point according to the said monitoring information, where the said monitoring attributes include monitoring scenario, monitoring real-time requirement, and monitoring application type; this step means identifying and obtaining the pre-configured parameter attributes of all data monitoring points (online monitoring units for monitoring photovoltaic user data) that make up the monitoring network (selecting different regional networks to execute according to the actual situation), that is, by identifying the monitoring attributes of each data monitoring point. The said monitoring attributes include three aspects: monitoring scenario (such as grid vulnerable fault points, equipment vulnerable overload points, short-circuit prone points, etc.; power generation power points and equipment balance control points, etc.; power generation amount monitoring points, equipment operation status monitoring points, etc.), monitoring real-time requirement (high, medium, low and other response requirements), and monitoring application type (emergency event monitoring scenario, dynamic regulation scenario, long-term trend analysis scenario, etc.). By using the monitoring information content included in the above three aspects of monitoring attributes, the acquisition frequency that should be adopted for the corresponding data monitoring point is determined accordingly (for example, for the equipment vulnerable overload point scenario belonging to the emergency event monitoring type and with an extremely high monitoring real-time requirement, a millisecond-level acquisition frequency configuration is considered), which is the configured acquisition frequency.

[0037] S200: Perform a priority ranking based on the configured acquisition frequencies of all data monitoring points to obtain a pre-acquisition frequency sequence of all data monitoring points; this step means using the method in step S100 to determine the configured acquisition frequencies of all data monitoring points, and then sorting all the configured acquisition frequencies according to the priority (in the order of millisecond, second, minute, hour, day), so as to obtain a pre-acquisition frequency sequence of the configured acquisition frequencies of all data monitoring points. This pre-acquisition frequency sequence reflects the address numbers of different data monitoring points (as the basis for calling monitoring attributes) and the numerical values of their configured acquisition frequencies. After obtaining the pre-planned pre-acquisition frequency sequence, the actual acquisition frequencies are then sorted.

[0038] S300: Obtain the real-time acquisition frequencies of all data monitoring points and perform a priority ranking to obtain a real-acquisition frequency sequence of all data monitoring points; this step means obtaining the acquisition frequency values in real time (for example, extracting the acquisition frequency from the current log of the sensor that uploads data as the data monitoring point itself) and performing a priority ranking (in the order of millisecond, second, minute, hour, day), that is, sorting the real-time acquisition frequencies of all data monitoring points in the same way to obtain a real-acquisition frequency sequence. This real-acquisition frequency sequence also reflects the address numbers of different data monitoring points (convenient for matching with the points in the pre-acquisition frequency sequence) and the numerical values of their configured acquisition frequencies.

[0039] S400: Compare the acquisition frequency change parameters of each data monitoring point according to the pre-acquired frequency sequence and the actual acquired frequency sequence, and determine whether each acquisition frequency change parameter is abnormal. If it is abnormal, send an alarm message; this step means using the sorted pre-acquired frequency sequence and the actual acquired frequency sequence for comparative analysis, and the analysis is carried out around the specific change situation of each data monitoring point, that is, by comparing the same data monitoring point in the two sequences to obtain the acquisition frequency change parameter of this data monitoring point, and using the obtained acquisition frequency change parameter to judge whether there is an abnormal acquisition frequency at this data monitoring point. If the acquisition frequency change parameter exceeds the expectation, an alarm message is sent, otherwise no warning is sent.

[0040] It should be noted that the acquisition frequency change parameters include three aspects: the acquisition frequency value, the sequence position change value, and the associated point distance change value. On the one hand, it is not simply to directly judge whether the change of the data monitoring point is abnormal based on the change of the acquisition frequency value, because it is common for the acquisition frequency value of a data monitoring point to change or even change slightly. However, if all data monitoring points change or even change in the same trend or by the same amplitude, it indicates the existence of an overall regulation behavior or phenomenon, such as reducing the acquisition load according to the corresponding needs of the power grid as a whole. At this time, the change of the data monitoring point may be a normal phenomenon. Therefore, it is also necessary to comprehensively judge by combining the sequence position change value and the associated point distance change value, that is, after comparing the generated pre-acquired frequency sequence and the actual acquired frequency sequence, judge the change situation of this data monitoring point in the sequence position to further assist in judging whether the acquisition frequency of the data monitoring point is abnormal. If the acquisition frequency value changes while the sequence positions in the two sequences do not change, it may be a normal situation. On this basis, considering that the sequence positions do not change, there may be an accidental phenomenon that only the sequence position of this data monitoring point does not change. It is also necessary to combine the judgment of the associated point distance change value, that is, whether the relative distance value (the interval value of the sequence position) of the data monitoring point(s) associated with this data monitoring point (such as device address number association) changes to further assist in the analysis and judgment.

[0041] Through the above sequence-based reference comparison method that combines the sequence bit change value and the associated point distance change value, the reliability of abnormal judgment when the acquisition frequency changes at the data monitoring point can be greatly improved. On this basis, it is also necessary to consider excluding the situation of false alarms caused by manual or automatic regulation of the configured acquisition frequency of the data monitoring point, that is, by combining and judging whether the configured acquisition frequency of the data monitoring point has been adjusted. Specifically, when judging whether the acquisition frequency change parameter is abnormal, it is judged in combination with the change situation of the monitoring attribute of the corresponding data monitoring point. That is, if the monitoring attribute of the data monitoring point changes, the configured acquisition frequency is re-determined according to the changed monitoring information, and then the re-determined configured acquisition frequency is compared and analyzed with the real-time acquisition frequency. The analysis method can be carried out in the way of the above steps, but it is necessary to re-generate the pre-acquisition frequency sequence.

[0042] Through the above technical solution, using the acquisition frequency of the configured data monitoring point (based on the monitoring scenario, monitoring real-time requirement, and monitoring application type), and combining with the predefined priority to generate a pre-acquisition sequence, and then generating a real-acquisition sequence after real-time acquisition for comparison and analysis. Using multi-dimensional parameters such as the acquisition frequency value, sequence bit change value, and associated point distance change value to comprehensively judge abnormalities, effectively distinguish normal regulation from abnormal fluctuations, and at the same time adaptively analyze and judge the change situation of the configured acquisition frequency brought about by the change of the monitoring attribute, so as to improve the accuracy of abnormal alarms and avoid operation and maintenance interference caused by misjudgment of frequency changes.

[0043] On the basis of the above technical solution, when the monitoring attribute of the corresponding data monitoring point changes, it will affect the re-determination of the configured acquisition frequency, thus changing the acquisition frequency change parameter obtained by comparing the pre-acquisition frequency sequence and the real-acquisition frequency sequence, and ultimately affecting the judgment result of whether it is abnormal. Therefore, the judgment of whether the monitoring attribute has changed is a key pre-judgment step, which can be further judged by the change situation of the monitoring scenario, monitoring real-time requirement, and monitoring application type. For details, please refer to Figure 2 When judging whether the acquisition frequency change parameter is abnormal, judging in combination with the change situation of the monitoring attribute of the corresponding data monitoring point includes the following steps:

[0044] S410: Obtain the monitoring scenario change, the change parameter of the monitoring real-time requirement, and the change parameter of the monitoring application type between the previous monitoring period and the current monitoring period for this data monitoring point; This step represents that when judging whether there is a change in the monitoring attributes of a certain data monitoring point, the degree of change is calculated based on whether there are changes in the monitoring scenario, the monitoring real-time requirement, and the monitoring application type between the previous monitoring period (the monitoring time period for the previous round of data collection and upload) and the current monitoring period (the monitoring time period for the current round of data collection and upload). Specifically, determine whether the monitoring scenario changes in the previous monitoring period and the current monitoring period respectively. If there is a change, determine a change parameter as the monitoring scenario change parameter according to the change situation (for example, if it changes from the easily faulty point of the monitored power grid to the easily overloaded point of the device, which is a non-correlated change, the change parameter is relatively close to 1; if it changes from the easily overloaded point of the device to the easily short-circuited point, which is a correlated conversion, the change parameter is relatively close to 0); Similarly, determine a change parameter as the change parameter of the monitoring real-time requirement according to the change situation of the monitoring real-time requirement (for example, determined according to the conversion ratio of the real-time requirement from high to medium); Similarly, determine a change parameter as the change parameter of the monitoring application type according to the change situation of the monitoring application type (calculated with reference to the monitoring scenario change. For example, if it changes from the emergency event monitoring scenario to the dynamic regulation scenario, the change parameter is relatively close to 1; if it is a conversion within the emergency event monitoring scenario type, the change parameter is relatively close to 0).

[0045] S420: Calculate the total change parameter based on the monitoring scenario change parameter, the change parameter of the monitoring real-time requirement, and the change parameter of the monitoring application type; This step represents combining the three values of the monitoring scenario change parameter, the change parameter of the monitoring real-time requirement, and the change parameter of the monitoring application type determined in step S410 to calculate a total change parameter. Among them, the combination method can be direct summation or weighted summation, so as to obtain the total change parameter to characterize the degree of change in the monitoring attributes.

[0046] S430: Determine the change type of the monitoring attribute of the data monitoring point according to the total change parameter, where the change type of the monitoring attribute includes no change and change; after combining the change type of the monitoring attribute with the acquisition frequency change parameter, determine whether it is abnormal. This step indicates using the numerical value of the total change parameter to represent the change type of the monitoring attribute (no change and change) of the corresponding data monitoring point. If the numerical value of the total change parameter is 0, it is no change; if the numerical value of the total change parameter is not 0, it is change. In this case, it can also be further divided into slight change, moderate change or severe change, etc. according to the numerical value of the total change parameter. Then, make a combined judgment according to the change type of the monitoring attribute and the acquisition frequency change parameter. If the change type of the monitoring attribute is no change and the acquisition frequency change parameter is not zero, it can be determined that the acquisition frequency of the corresponding data monitoring point has changed abnormally (if the acquisition frequency change parameter is zero, it is normal). If the change type of the monitoring attribute is change and the acquisition frequency change parameter is zero, it can also be judged that the acquisition frequency of the corresponding data monitoring point has changed abnormally; if the change type of the monitoring attribute is change and the acquisition frequency change parameter is not zero, further analysis is required to exclude the normal situation of manually synchronously adjusting the acquisition frequency to obtain the judgment result of whether there is an abnormal change.

[0047] Through the above technical solution, calculate the total change parameter using the changes in the monitoring scenario, real-time requirement, and application type to determine whether the monitoring attribute has changed, and make a comprehensive abnormal judgment in combination with the acquisition frequency change parameter, which can further effectively distinguish between normal adjustment and abnormal fluctuation, thereby avoiding false alarms and missed reports and improving the accuracy and reliability of abnormal detection. On this basis, for the situation where the change type of the monitoring attribute is change and the acquisition frequency change parameter is not zero, further targeted analysis is required to obtain a more reliable judgment result. Further, when the change type of the monitoring attribute is change, judge the rationality of the associated impact of the change type of the monitoring attribute on the acquisition frequency change parameter, that is, whether the change in the monitoring attribute brings an expected associated impact on the acquisition frequency change parameter after the change, so as to further analyze whether the real-time acquisition frequency has changed abnormally unexpectedly.

[0048] Specifically, it can be achieved through the following steps: Obtain the pre-acquisition frequency sequence after the monitoring attribute of the data monitoring point changes in this monitoring period, denoted as the changed pre-acquisition frequency sequence; that is, it means that after the monitoring attribute changes, the configured acquisition frequency of the data monitoring point changes, and then a new pre-acquisition frequency sequence is generated as the changed pre-acquisition frequency sequence. It should be noted that when the configured acquisition frequency of a single data monitoring point changes, record the changed configured acquisition frequency (the configured acquisition frequencies of the other data monitoring points do not change) to generate the changed pre-acquisition frequency sequence.

[0049] Then, calculate the acquisition frequency change parameter of the data monitoring point between the pre-acquisition frequency sequence of the change and the real-time frequency acquisition sequence; that is, it means to further judge by recalculating an acquisition frequency change parameter through the obtained pre-acquisition frequency sequence of the change and the real-time frequency acquisition sequence. Specifically, the acquisition frequency change parameter is obtained by calculating three aspects: the acquisition frequency value, the sequence bit change value, and the associated point distance change value, that is, the rationality judgment of the change can be associated through the change situations of the acquisition frequency value, the sequence bit change value, and the associated point distance change value. Generally speaking, after the monitoring attribute changes, it is necessary to compare the original acquisition frequency change parameter (the comparative analysis result of the pre-acquisition frequency sequence and the real acquisition frequency sequence) with the acquisition frequency change parameter after the monitoring attribute change (the comparative analysis result of the pre-acquisition frequency sequence of the change and the real-time frequency acquisition sequence) to judge whether the associated impact brought by this change before and after is as expected. The comparison between the acquisition frequency change parameter and the acquisition frequency change parameter after the monitoring attribute change can be further analyzed through the acquisition frequency value, the sequence bit change value, and the associated point distance change value. After the acquisition frequency value changes, the sequence bit change value and the associated point distance change value should also change as expected.

[0050] Specifically, calculate the first difference result according to the (before and after) change situation of the acquisition frequency value, calculate the second difference result according to the (before and after) change situation of the sequence bit change value, and calculate the third difference result according to the (before and after) change situation of the associated point distance change value; thus, the change results of the acquisition frequency value, the sequence bit change value, and the associated point distance change value are obtained. At this time, verify how the sequence bit change value and the associated point distance change value should change in the sequence ranking after the acquisition frequency value changes, that is, calculate the theoretical second difference result (the result of the sequence bit change value in theory) and the theoretical third difference result (the result of the associated point distance change value in theory) according to the first difference result; finally, further judge through the differences between the theoretical second difference result and the theoretical third difference result and the actual second difference result and the third difference result, that is, judge the rationality according to the second comparison value between the second difference result and the theoretical second difference result and the third comparison value between the third difference result and the theoretical third difference result. If the theoretical second comparison value is zero and the third comparison value is zero, it proves that the first difference result brings the second difference result and the third difference result that change as expected, which means that the acquisition frequency change parameter after the monitoring attribute change also changes as expected, which means that the configured acquisition frequency change after the monitoring attribute change belongs to the controllable normal situation, otherwise it belongs to the abnormal change situation.

[0051] After monitoring the change of attributes, regenerate the pre - acquisition frequency sequence of the change, and calculate the new acquisition frequency change parameter by combining with the real - time acquisition sequence. By comparing the differences in the acquisition frequency values before and after, the sequence bit change values, and the associated point distance change values, verify whether the change of the monitored attribute has an expected associated impact on the acquisition frequency change parameter, so as to further distinguish normal adjustment from abnormal fluctuation. On the basis of this technical solution, considering that the first difference result will affect the specific values of the second difference result and the third difference result, and the first difference result will have the same degree of influence on the specific values of the second difference result and the third difference result. Therefore, it is necessary to further judge the rationality of the associated impact according to the magnitude of the degree of influence and whether this degree of influence is the same. Specifically, the judgment of the rationality according to the second comparison value between the second difference result and the theoretical second difference result and the third comparison value between the third difference result and the theoretical third difference result includes the following steps:

[0052] Determine the first rationality parameter according to the specific values of the second comparison value and the third comparison value; this step means calculating a first rationality parameter through the specific values of the second comparison value and the third comparison value as the basis for the first - layer judgment. The specific value should be obtained by combining the situation of the first difference result with the difference between its expected change in the sequence and the actual change. The larger the specific values of the second comparison value and the third comparison value, the lower the rationality of the associated impact; conversely, the smaller the values, the higher the rationality of the associated impact. Then determine the second rationality parameter according to the difference between the second comparison value and the third comparison value; that is, it means calculating a second rationality parameter as the basis for the second - layer judgment from whether the change situation of the above - mentioned sequence bit change value is the same as the change situation of the associated point distance change value. If they are the same, it indicates a higher rationality of the associated impact; conversely, if they are different, it indicates a lower rationality of the associated impact. Finally, judge the rationality by combining the first rationality parameter and the second rationality parameter. The combination judgment method can be a (weighted) average calculation method.

[0053] In addition, since after the acquisition frequency value changes, the sequence bit change value and the associated point distance change value should also change as expected and in the same way. Then, when calculating the difference between the second comparison value and the third comparison value, if the difference is zero, it indicates a higher reliability of the change process; if it is not zero, it indicates a lower reliability of the change process. And in this case, there may be a situation where the theoretical and actual change situations of the associated point (the data monitoring point associated with the data monitoring point participating in the calculation) are inconsistent. For example, the associated point has a sequence bit change or different associated points are located (one of the multiple associated points is mis - located during the calculation). At this time, it is necessary to further judge whether the change of the associated point is reliable.

[0054] Specifically, when the difference between the second comparison value and the third comparison value is not 0, a truth judgment step for the third comparison value is required: calculate the sequence bit change value of the associated point, compare this change value with the difference between the second comparison value and the third comparison value, and judge the truth according to the comparison result, that is, it means obtaining the sequence bit change value situation of the associated point, comparing this sequence bit change value with the difference (between the second comparison value and the third comparison value), if the comparison result of the two is 0 (the sequence bit change value is 0 and the difference is 0, which means the selection of the associated point is reliable; the sequence bit change value is non-0 and the difference is non-0, which means there is an accidental error in the calculation of the associated point and does not affect the reliability of the associated point selection), it indicates that the truth is relatively high, if the comparison result of the two is non-0, it means the truth is relatively poor, and at this time, the determination method of the second rationality parameter needs to be reconsidered.

[0055] Through the above technical solution, calculate the specific values and the difference between the second comparison value and the third comparison value, generate the first rationality parameter and the second rationality parameter respectively, and comprehensively judge the rationality of the influence of the change of the monitoring attribute on the associated change parameter of the acquisition frequency in combination with the two. At the same time, for the case where the difference is not zero, further verify the truth of the sequence bit change value of the associated point, so as to eliminate the interference of wrong selection or accidental error of the associated point, and improve the accuracy and reliability of the abnormal judgment.

[0056] In some embodiments, the generation method of the pre-acquisition frequency sequence after the change of the monitoring attribute can be the same as the generation method of the pre-acquisition frequency sequence before the change, but it needs to be updated separately after the acquisition frequency of a certain data monitoring point changes, and the acquisition frequencies configured for the rest of the data monitoring points are not updated to ensure the rationality of the single control variable for comparative analysis. Specifically, the steps for obtaining the pre-acquisition frequency sequence of this data monitoring point after the change of the monitoring attribute in this monitoring period are as follows:

[0057] Obtain the change monitoring information of this data monitoring point after the change of the monitoring attribute in this monitoring period, and determine the pre-configured acquisition frequency of this data monitoring point according to this change monitoring information; this step means using the change monitoring information obtained by the corresponding data monitoring point after the change of the monitoring attribute to re-determine the configured acquisition frequency as the pre-configured acquisition frequency, and the determination method can refer to the description of S100. Then, perform a priority ranking on the pre-configured acquisition frequency of this data monitoring point and the configured acquisition frequencies of the rest of the data monitoring points to obtain the pre-acquisition frequency sequence, that is, only use the pre-configured acquisition frequency of this data monitoring point as a variable to perform a priority ranking with the configured acquisition frequencies already obtained by other data monitoring points, and the ranking method can refer to the description of step S200, so as to generate the pre-acquisition frequency sequence after the change.

[0058] Based on the above solution, considering that the pre-configured acquisition frequency needs to be re-determined as the pre-configured acquisition frequency after monitoring attribute changes, the pre-configured acquisition frequency can be re-determined according to the changed monitoring scenario, monitoring real-time requirement, and monitoring application type. The situation of temporary changes in these three aspects may belong to accidental changes in long-term operation or may also belong to temporary human plan adjustments. For the former case of accidental changes, since the acquisition frequency is configured manually according to the changes in the monitoring scenario, monitoring real-time requirement, and monitoring application type, a trend analysis needs to be made in combination with historical changes, especially when the changes are small and not easy to be defined and analyzed manually. Specifically, determining the pre-configured acquisition frequency of the data monitoring point according to the changed monitoring information includes the following steps:

[0059] Obtain the configured acquisition frequencies of the data monitoring point in the previous monitoring period and the current monitoring period, denoted as the first configured acquisition frequency and the second configured acquisition frequency, respectively determine the first merging weight and the second merging weight, and add the product of the first merging weight and the first configured acquisition frequency to the product of the second merging weight and the second configured acquisition frequency to obtain the pre-configured acquisition frequency; this step means using the method of combining weights to retain the reliability of historical data and the target after changes, so that the combined result of the weighted products is more accurate and true. It should be noted that the first merging weight is determined according to the change situation of the historical configured acquisition frequency of the data monitoring point, that is, the greater the change rate of the historical configured acquisition frequency (indicating that the reliability of the reference of the previous historical data becomes lower), the lower the first merging weight, and vice versa; the second merging weight is determined according to the difference between the value 1 and the first merging weight, that is, it means that the sum of the first merging weight and the second merging weight is 1.

[0060] Through the above technical solution, by combining the historical configured acquisition frequency with the currently changed configured acquisition frequency and generating the pre-configured acquisition frequency by means of weight allocation, the reference value of historical data is retained, and the actual situation of current monitoring attribute changes is also considered, thus improving the accuracy and adaptability of the pre-configured acquisition frequency. Based on the above solution, the first merging weight can be determined according to the change situation of the historical configured acquisition frequency of the data monitoring point. In order to ensure that the first merging weight can refer to all dimensions of historical monitoring attributes, the historical scenario change situation, the historical monitoring real-time requirement change situation, and the historical monitoring application type change situation can also be further considered. Specifically, determining the first merging weight includes the following steps:

[0061] Determine the basic value of the first combined weight according to the change situation of the historical configuration acquisition frequency of the data monitoring point, that is, it represents determining the basic value of the first combined weight by using the change trend of the historical configuration acquisition frequency. On this basis, determine the first adjustment parameter according to the change situation of the historical monitoring scenario, determine the second adjustment parameter according to the change situation of the historical monitoring real-time requirement, and determine the third adjustment parameter according to the change situation of the historical monitoring application type. The determination method is, for example, to determine different adjustment parameter ranges according to the amplitudes of the change trend lines of the historical monitoring scenario, historical monitoring real-time requirements, and historical monitoring application types in multiple monitoring periods. The larger the amplitude, the larger the adjustment parameter, and the more obvious the effect of changing the basic value. Finally, combine (such as sum) the basic value, the first adjustment parameter, the second adjustment parameter, and the third adjustment parameter into the first combined weight. Especially when the adjustment parameter is larger, the resulting value after combining with the first combined weight is smaller, thereby reducing the proportion of the pre-configured acquisition frequency calculated in the previous monitoring period. By comprehensively considering the change situation of the historical configuration acquisition frequency and the change trends of the historical monitoring scenario, real-time requirements, and application type, and using the combined calculation method of the basic value and the adjustment parameter to determine the first combined weight, it not only retains the multi-dimensional reference value of the historical data but also dynamically adjusts the weight distribution of the historical data, thereby further improving the accuracy and adaptability of the pre-configured acquisition frequency.

[0062] In some embodiments, the configured acquisition frequency can also refer to the determination method of the pre-configured acquisition frequency, that is, it is jointly determined by using the three dimensions of the monitoring scenario, monitoring real-time requirement, and monitoring application type of the monitoring attribute, so as to improve the rationality of the configured acquisition frequency calculation. For details, please refer to Figure 3 , identifying the monitoring attributes of each data monitoring point to obtain the monitoring information of the data monitoring point, and determining the configured acquisition frequency of the data monitoring point according to the monitoring information includes the following steps:

[0063] S110: Determine the acquisition frequency range according to the monitoring scenario of the data monitoring point; this step means to determine the range where the acquisition frequency to be configured is located through the specific category of the monitoring scenario. For example, for emergency event monitoring, the acquisition frequency is in the high-frequency range. Then, proceed to step S130: Narrow down the acquisition frequency range according to the real-time monitoring requirement of the data monitoring point to obtain the quasi-acquisition frequency range; this step means to limit the range of the acquisition frequency range through real-time monitoring. For example, if the response requirement is extremely high, select the range of higher-frequency values in the high-frequency range as the quasi-acquisition frequency range. Then, proceed to step S140: Determine a specific value within the quasi-acquisition frequency range according to the monitoring application type of the data monitoring point as the configured acquisition frequency. This step means to specifically select the value of the acquisition frequency within the quasi-acquisition frequency range for different monitoring application types. For example, if the monitoring application type is equipment overload, determine the acquisition frequency in seconds; if it is power generation power regulation, determine the acquisition frequency in minutes; if it is power generation quantity statistics, determine the acquisition frequency in hours.

[0064] Through the above technical solution, the determination range of the acquisition frequency is gradually narrowed down using three dimensions: monitoring scenario, real-time monitoring requirement, and monitoring application type. Finally, a specific value is selected within the quasi-acquisition frequency range as the configured acquisition frequency, which can not only improve the rationality and accuracy of the configured acquisition frequency calculation but also ensure that the data acquisition strategy can be dynamically adapted under different monitoring scenarios and application requirements. On the basis of the above technical solution, considering that the monitoring scenarios in different monitoring periods will change (the dynamic switching process of monitoring tasks), that is, it means that the focus of the monitoring task of this data monitoring point will show historical or long-term fluctuating adjustments. Therefore, the acquisition frequency range will also change dynamically and needs to be adjusted compatibly. Therefore, after determining the acquisition frequency range according to the monitoring scenario of the data monitoring point, the following steps are also included:

[0065] S120: Count the historical monitoring scenario types of the data monitoring point in each monitoring period, and adjust the acquisition frequency range according to the statistical situation of all historical monitoring scenario types, and participate in the process of determining the configured acquisition frequency with the adjusted acquisition frequency range; this step means to separately count the monitoring scenario types of all monitoring periods, and determine and adjust the acquisition frequency range based on the mainly concentrated monitoring scenario types, rather than determining the acquisition frequency range based on the monitoring scenario of a certain monitoring period alone, ensuring that the acquisition frequency range has strong compatibility with the dynamic changes of monitoring tasks. That is, by counting the distribution of historical monitoring scenario types, the acquisition frequency range is dynamically adjusted to make it compatible with the dynamic changes and long-term fluctuations of monitoring tasks, thereby improving the adaptability and stability of the acquisition frequency range and ensuring that the data acquisition strategy can still maintain rationality and accuracy under different monitoring periods and changes in the task focus.

[0066] In this embodiment, a data monitoring and analysis system 500 based on the connection of distributed power sources to the grid is also provided. Please refer to Figure 4 the modular schematic diagram of the data monitoring and analysis system 500 based on the connection of distributed power sources to the grid in Figure 4 . It is mainly used to divide the functional modules of the data monitoring and analysis system 500 based on the connection of distributed power sources to the grid according to the embodiments of the above method. For example, each functional module can be divided, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the present invention is schematic, only a logical function division, and there can be other division methods in actual implementation. For example, in the case of dividing each functional module corresponding to each function, Figure 4 only a system / device schematic diagram is shown. Among them, the data monitoring and analysis system 500 based on the connection of distributed power sources to the grid may include a first acquisition unit 510, a first processing unit 520, a second acquisition unit 530, and a first calculation unit 540. The functions of each unit module will be described below.

[0067] The first acquisition unit 510 is used to locate all data monitoring points in the monitoring network, identify the monitoring attributes of each data monitoring point, obtain the monitoring information of the data monitoring point, and determine the configured acquisition frequency of the data monitoring point according to the monitoring information. Among them, the monitoring attributes include monitoring scenarios, monitoring real-time requirements, and monitoring application types. In some embodiments, the first acquisition unit 510 is further used to determine the acquisition frequency range according to the monitoring scenario of the data monitoring point; narrow the acquisition frequency range according to the monitoring real-time requirements of the data monitoring point to obtain a quasi-acquisition frequency range; determine a specific value in the quasi-acquisition frequency range according to the monitoring application type of the data monitoring point as the configured acquisition frequency. And it is used to: count the historical monitoring scenario types of the data monitoring point in each monitoring period, and adjust the acquisition frequency range according to the statistical situation of all historical monitoring scenario types, and participate in the process of determining the configured acquisition frequency with the adjusted acquisition frequency range.

[0068] The first processing unit 520 is used to perform priority sorting based on the configured acquisition frequencies of all data monitoring points to obtain a pre-acquisition frequency sequence of all data monitoring points;

[0069] The second acquisition unit 530 is used to obtain the real-time acquisition frequencies of all data monitoring points and perform priority sorting to obtain a real-acquisition frequency sequence of all data monitoring points;

[0070] A first calculation unit 540, which is used to compare the acquisition frequency change parameters of each data monitoring point according to the pre-acquired frequency sequence and the actual acquired frequency sequence, and determine whether each acquisition frequency change parameter is abnormal. If it is abnormal, an alarm message is sent; wherein, the acquisition frequency change parameters include the acquisition frequency value, the sequence bit change value, and the associated point distance change value; wherein, when determining whether the acquisition frequency change parameter is abnormal, it is determined in combination with the change of the monitoring attribute of the corresponding data monitoring point. In some embodiments, the first calculation unit 540 is further configured to obtain the monitoring scenario change, the monitoring real-time requirement change parameter, and the monitoring application type change parameter of the data monitoring point between the previous monitoring period and the current monitoring period, and calculate the total change parameter according to the monitoring scenario change parameter, the monitoring real-time requirement change parameter, and the monitoring application type change parameter; determine the monitoring attribute change type of the data monitoring point according to the total change parameter, and then determine whether it is abnormal after combining the monitoring attribute change type with the acquisition frequency change parameter; wherein, the monitoring attribute change type includes no change and change. If the monitoring attribute change type is change, then determine the rationality of the associated impact of the monitoring attribute change type on the acquisition frequency change parameter: obtain the pre-acquired frequency sequence after the monitoring attribute change of the data monitoring point in the current monitoring period, denoted as the changed pre-acquired frequency sequence; calculate the acquisition frequency change parameter between the changed pre-acquired frequency sequence and the real-time frequency acquisition sequence of the data monitoring point; calculate the first difference result according to the change of the acquisition frequency value, calculate the second difference result according to the change of the sequence bit change value, and calculate the third difference result according to the change of the associated point distance change value; calculate the theoretical second difference result and the theoretical third difference result according to the first difference result; determine the rationality according to the second comparison value between the second difference result and the theoretical second difference result and the third comparison value between the third difference result and the theoretical third difference result. It is also used to determine the first rationality parameter according to the specific values of the second comparison value and the third comparison value; determine the second rationality parameter according to the difference between the second comparison value and the third comparison value; determine the rationality according to the first rationality parameter and the second rationality parameter; wherein, if the difference between the second comparison value and the third comparison value is not 0, a authenticity judgment step for the third comparison value is required: calculate the sequence bit change value of the associated point, compare the change value with the difference between the second comparison value and the third comparison value, and determine the authenticity according to the comparison result. And it is used to obtain the changed monitoring information of the data monitoring point after the monitoring attribute change in the current monitoring period; determine the pre-configured acquisition frequency of the data monitoring point according to the changed monitoring information; perform priority sorting on the pre-configured acquisition frequency and the configured acquisition frequencies of the remaining data monitoring points to obtain the pre-acquired frequency sequence.It is also used to obtain the configured acquisition frequencies of the data monitoring point in the previous monitoring period and the current monitoring period, denoted as the first configured acquisition frequency and the second configured acquisition frequency, respectively determine the first merging weight and the second merging weight, and add the product of the first merging weight and the first configured acquisition frequency to the product of the second merging weight and the second configured acquisition frequency to obtain the pre-configured acquisition frequency; wherein, the first merging weight is determined according to the change situation of the historical configured acquisition frequency of the data monitoring point, and the second merging weight is determined according to the difference between the value 1 and the first merging weight. And it is used to determine the base value of the first merging weight according to the change situation of the historical configured acquisition frequency of the data monitoring point; determine the first adjustment parameter according to the change situation of the historical monitoring scenario, determine the second adjustment parameter according to the change situation of the historical monitoring real-time requirement, and determine the third adjustment parameter according to the historical monitoring application type; merge the base value, the first adjustment parameter, the second adjustment parameter and the third adjustment parameter into the first merging weight.

[0071] In the above embodiments, the more specific working processes of the functional units can refer to the corresponding content disclosed in the foregoing method embodiments. In addition, the functional units can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0072] Embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0073] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0075] Obviously, those skilled in the art can make various modifications and variations to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A data monitoring and analysis method based on the connection of distributed power sources to the grid, characterized in that, The steps are as follows: Locate all data monitoring points in the monitoring network, identify the monitoring attributes of each data monitoring point, obtain the monitoring information of the data monitoring point, and determine the configured acquisition frequency of the data monitoring point according to the monitoring information, where the monitoring attributes include monitoring scenarios, monitoring real-time requirements, and monitoring application types; Perform priority sorting based on the configured acquisition frequencies of all data monitoring points to obtain a pre-acquisition frequency sequence of all data monitoring points; Obtain the real-time acquisition frequencies of all data monitoring points and perform priority sorting to obtain a real-acquisition frequency sequence of all data monitoring points; Compare the acquisition frequency change parameters of each data monitoring point according to the pre-acquisition frequency sequence and the real-acquisition frequency sequence, and determine whether each acquisition frequency change parameter is abnormal. If it is abnormal, an alarm message is sent; where the acquisition frequency change parameters include acquisition frequency values, sequence position change values, and associated point distance change values; Among them, when determining whether the acquisition frequency change parameter is abnormal, it is determined in combination with the change situation of the monitoring attributes of the corresponding data monitoring point.

2. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 1, characterized in that When determining whether the acquisition frequency change parameter is abnormal, determining in combination with the change situation of the monitoring attributes of the corresponding data monitoring point includes the following steps: Obtain the monitoring scenario change, monitoring real-time requirement change parameter, and monitoring application type change parameter of the data monitoring point between the previous monitoring period and the current monitoring period, calculate the total change parameter according to the monitoring scenario change parameter, monitoring real-time requirement change parameter, and monitoring application type change parameter; determine the monitoring attribute change type of the data monitoring point according to the total change parameter, and then determine whether it is abnormal after combining the monitoring attribute change type with the acquisition frequency change parameter; where the monitoring attribute change type includes no change and change.

3. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 2, wherein If the monitoring attribute change type is a change, then determine the rationality of the associated impact of the monitoring attribute change type on the acquisition frequency change parameter: Obtain the pre-acquisition frequency sequence after the monitoring attribute change of the data monitoring point in the current monitoring period, denoted as the changed pre-acquisition frequency sequence; calculate the acquisition frequency change parameter of the data monitoring point between the changed pre-acquisition frequency sequence and the real-time frequency acquisition sequence; calculate the first difference result according to the change situation of the acquisition frequency value, calculate the second difference result according to the change situation of the sequence position change value, and calculate the third difference result according to the change situation of the associated point distance change value; calculate the theoretical second difference result and the theoretical third difference result according to the first difference result; determine the rationality according to the second comparison value between the second difference result and the theoretical second difference result and the third comparison value between the third difference result and the theoretical third difference result.

4. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 3, characterized in that, The specific steps of determining the rationality according to the second comparison value between the second difference result and the theoretical second difference result and the third comparison value between the third difference result and the theoretical third difference result include the following steps: Determine a first rationality parameter according to the specific values of the second comparison value and the third comparison value; determine a second rationality parameter according to the difference between the second comparison value and the third comparison value; judge the rationality according to the first rationality parameter and the second rationality parameter; Among them, when the difference between the second comparison value and the third comparison value is not 0, it is necessary to perform a authenticity judgment step on the third comparison value: calculate the sequence bit change value of the associated point, compare the change value with the difference between the second comparison value and the third comparison value, and judge the authenticity according to the comparison result.

5. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 3, characterized in that, The steps for obtaining the pre-acquisition frequency sequence of this data monitoring point after the monitoring attribute changes in this monitoring period are as follows: Obtain the change monitoring information of this data monitoring point after the monitoring attribute changes in this monitoring period; determine the pre-configured acquisition frequency of this data monitoring point according to the change monitoring information; perform priority sorting on the pre-configured acquisition frequency and the configured acquisition frequencies of the remaining data monitoring points to obtain the pre-acquisition frequency sequence.

6. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 5, wherein The steps for determining the pre-configured acquisition frequency of this data monitoring point according to the change monitoring information are as follows: Obtain the configured acquisition frequencies of this data monitoring point in the previous monitoring period and this monitoring period, denoted as the first configured acquisition frequency and the second configured acquisition frequency, respectively determine the first merging weight and the second merging weight, and add the product of the first merging weight and the first configured acquisition frequency and the product of the second merging weight and the second configured acquisition frequency to obtain the pre-configured acquisition frequency; among them, the first merging weight is determined according to the historical configured acquisition frequency change situation of this data monitoring point, and the second merging weight is determined according to the difference between the value 1 and the first merging weight.

7. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 6, characterized in that, The steps for determining the first merging weight include the following: Determine the basic value of the first merging weight according to the historical configured acquisition frequency change situation of this data monitoring point; determine the first adjustment parameter according to the historical monitoring scenario change situation, determine the second adjustment parameter according to the historical monitoring real-time requirement change situation, and determine the third adjustment parameter according to the historical monitoring application type change situation; merge the basic value, the first adjustment parameter, the second adjustment parameter and the third adjustment parameter into the first merging weight.

8. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 1, characterized in that, The steps for identifying the monitoring attribute of each data monitoring point, obtaining the monitoring information of this data monitoring point, and determining the configured acquisition frequency of this data monitoring point according to the monitoring information are as follows: Determine the acquisition frequency interval according to the monitoring scenario of this data monitoring point; narrow the acquisition frequency interval according to the monitoring real-time requirement of this data monitoring point to obtain the quasi-acquisition frequency interval; determine the specific value in the quasi-acquisition frequency interval according to the monitoring application type of this data monitoring point as the configured acquisition frequency.

9. The data monitoring and analysis method based on the connection of distributed power sources to the grid according to claim 8, characterized in that After determining the acquisition frequency interval according to the monitoring scenario of this data monitoring point, the following steps are also included: count the historical monitoring scenario types of this data monitoring point in each monitoring period, and adjust the acquisition frequency interval according to the statistical situation of all historical monitoring scenario types, and use the adjusted acquisition frequency interval to participate in the process of determining the configured acquisition frequency.

10. A data monitoring and analysis system based on the connection of distributed power sources, characterized in that, Include: A first acquisition unit, which is configured to locate all data monitoring points in the monitoring network, identify the monitoring attributes of each data monitoring point, obtain the monitoring information of the data monitoring point, and determine the configured acquisition frequency of the data monitoring point according to the monitoring information, where the monitoring attributes include a monitoring scenario, a monitoring real-time requirement, and a monitoring application type; A first processing unit, which is configured to perform priority sorting based on the configured acquisition frequencies of all data monitoring points to obtain a pre-acquisition frequency sequence of all data monitoring points; A second acquisition unit, which is configured to obtain the real-time acquisition frequencies of all data monitoring points and perform priority sorting to obtain a real-acquisition frequency sequence of all data monitoring points; A first calculation unit, which is configured to compare the acquisition frequency change parameters of each data monitoring point according to the pre-acquisition frequency sequence and the real-acquisition frequency sequence, and determine whether each acquisition frequency change parameter is abnormal. If it is abnormal, an alarm message is sent; where the acquisition frequency change parameters include an acquisition frequency value, a sequence bit change value, and an associated point distance change value; where, when determining whether the acquisition frequency change parameter is abnormal, the change situation of the monitoring attributes of the corresponding data monitoring point is combined for judgment.

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