Distributed photovoltaic power supply method and system based on big data interconnection

Abstract

The present application relates to the technical field of data processing, in particular to a distributed photovoltaic power supply method and system based on big data interconnection, comprising the following steps: collecting photovoltaic node time sequence data, identifying trend turning points and mutation positions, positioning trend synchronization points and time offset ranges, extracting resource repeated requests and sorting, identifying co-time mutation nodes, screening replacement channels, and outputting path change lists. In the present application, the behavior change sequence is constructed by the node change trend and mutation amplitude, the time sequence difference is calibrated by combining the direction consistency and the offset range, the resource concentration features are expressed by constructing the sorting structure with the request intensity and the time, the continuous mutation time points are extracted and the co-time node group is cross-positioned, the replacement channel is screened and the path order is adjusted by combining the path load, and the effect is achieved in realizing the node behavior identification precision, the resource request response ordering, the co-time event positioning efficiency and the path scheduling dynamic adaptation, and the response capability and power supply coordination in the multi-node change scene are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a distributed photovoltaic power supply method and system based on big data interconnection. BACKGROUND

[0002] The technical field of data processing mainly involves the collection, organization, analysis and conversion of raw data, and realizes efficient utilization and intelligent management of data through various calculation methods and logical judgments. Its core tasks include big data analysis, distributed computing, data communication, data fusion and information decision support, etc. This technology field is widely used in industrial control, energy management, smart city and smart grid, etc. It has important significance in processing large-scale heterogeneous data, real-time transmission control and optimization scheduling. Among them, the traditional distributed photovoltaic power supply method refers to the local energy distribution and adjustment of the photovoltaic system based on fixed control logic and on-site hardware parameters. It usually uses preset parameter matching and timing control strategy to solve the problems of poor power supply stability and low energy utilization efficiency in distributed photovoltaic system. Its common methods include realizing voltage and current matching and power regulation through PLC programming or realizing coordinated management of system operation through remote SCADA system issuing instruction control switch quantity.

[0003] The prior art relies on static parameter setting and preset adjustment strategy, and the response mechanism is insufficient when the node behavior exists trend inflection point or data mutation, which easily leads to lag in recognizing state changes between nodes, overlapping of resource requests in time but inability to establish priority ordering structure, causing resource competition processing deviation, difficulty for the system to capture and cooperatively process the sudden synchronous changes of multiple nodes in a short period, lack of dynamic sensing ability of load state of each channel in the communication path, and difficulty in realizing effective adjustment under the situation of path bottleneck or concentrated forwarding pressure, affecting the adjustment flexibility and power supply stability of the overall operation of the system. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art, and to provide a distributed photovoltaic power supply method and system based on big data interconnection.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: a distributed photovoltaic power supply method based on big data interconnection, comprising the following steps:

[0006] S1: Collect photovoltaic node time series data, compare the value change direction of consecutive time periods, record the trend turning point, construct a difference sequence, judge whether the jump meets the mutation standard, merge the mutation and turning point positions, and obtain the node behavior change position;

[0007] S2: Extract the power generation and load node change direction based on the node behavior change position, judge consistency, locate trend synchronization point, compare trend difference, mark change offset area, combine edge controller record, and output time synchronization offset range set;

[0008] S3: Read the time synchronization offset range set, filter repeated resource target request group, extract intensity and time value, execute product sorting, and output resource overlap request result;

[0009] S4: Call the continuous operation data in the resource overlap request result, identify the value mutation point, extract the mutation time set, and cross compare with the node time set, verify the time coincidence dense section, and get the common time mutation node identification group;

[0010] S5: According to the node path listed in the common time mutation node identification group, retrieve the current forwarding channel and load condition, combine the communication router record, filter the channel to be replaced, and output the distributed photovoltaic power supply path change list.

[0011] As a further scheme of the application, the node behavior change position includes trend turning time, difference mutation amplitude, trend change direction, the time synchronization offset range set includes synchronization offset start and end time, trend difference range, and direction consistency identification, the resource overlap request result includes repeated resource target, request intensity value, and recombination sorting index, the common time mutation node identification group includes mutation occurrence time, time coincidence interval, and mutation node number, and the distributed photovoltaic power supply path change list includes channel to be replaced number, adjusted path order, and channel current load value;

[0012] The definition of mutation standard is that the mutation amplitude of node behavior value reaches the preset change threshold, which marks the value jump;

[0013] The definition of change offset area is that the area marked when the node behavior change direction is inconsistent with the power generation and load node change direction.

[0014] As a further scheme of the application, the specific steps of S1 are:

[0015] S101: Collect continuous time sequence data from photovoltaic nodes, compare the increase and decrease relationship of adjacent two time point values in turn, record the time point of value change direction inversion, and generate trend change time sequence;

[0016] S102: Calculate the difference value of adjacent values based on time sequence data, compare the change amplitude of difference value and previous and next difference value item by item, judge whether it exceeds the difference jump amplitude threshold, and generate mutation jump position sequence;

[0017] S103: Based on the sequence of trend change moments and the sequence of mutation jump positions, all time points in the merged sequence are extracted as behavior change points of nodes, and a node behavior change position is generated.

[0018] Definition of difference jump amplitude threshold: the difference amplitude limit for judging whether the node behavior numerical difference has a mutation.

[0019] As a further scheme of the present application, the specific steps of S2 are:

[0020] S201: According to the node behavior change position, the numerical change direction of the power generation node and the load node at the same time point is extracted, and the increasing and decreasing trend directions are compared one by one, the time point set with consistent change direction is screened, and the trend consistent moment sequence is generated.

[0021] S202: Based on the time points in the trend consistent moment sequence, the numerical difference of the power generation node and the load node at the time point and the time period before and after the time point is called, the upstream and downstream trend change values are calculated, and the coefficients are compared with the trend deviation threshold value, the positions exceeding the threshold value are extracted, and the trend deviation interval set is obtained.

[0022] S203: According to the time period position in the trend deviation interval set, the time period record data of the photovoltaic edge node controller is called, the start and end time of the controller response record in the interval is matched, the time span is extracted, and the time synchronization deviation range set is established.

[0023] Definition of increasing and decreasing trend direction: the increasing and decreasing trend of the node at the continuous moment compared with the previous moment;

[0024] Definition of trend deviation threshold: the change coefficient limit for judging whether the node power generation and load change trend deviates.

[0025] As a further scheme of the present application, the upstream and downstream trend change value calculation formula is specifically:

[0026] ;

[0027] Wherein, represents the upstream and downstream trend change value of the i-th time point, represents the power generation node power of the i-th time point, represents the load node power of the i-1-th time point, represents the power generation node power of the i+1-th time point, represents the load node power of the i-th time point, represents the power generation node value of the i-th time point, represents the load node value of the i-th time point.

[0028] As a further scheme of the present application, the specific step of S3 is:

[0029] S301: read the node request content in the time synchronization offset range set, extract the resource target identifier in each request, compare the repetition of the resource target identifier, filter multiple request records with the same resource target, and establish a resource target repeated request group list;

[0030] S302: according to the resource target repeated request group list, extract the intensity value and timestamp of the request in each group, calculate the product index of intensity and time value, sort the product value of the request, construct the request structure arranged by the product value, and generate the behavior request order sequence;

[0031] S303: call the resource target and time information of the request in the behavior request order sequence, screen the request group with overlapping time periods, locate the occupation of multiple requests to the same resource in the same time period, and establish a resource overlapping request result;

[0032] Definition of multiple request records with the same resource target: within the time synchronization offset range, multiple requests point to the same resource identifier request set.

[0033] As a further scheme of the present application, the product index calculation formula of intensity and time value is specifically:

[0034] ;

[0035] Wherein, represents the product index of intensity and time value of request q in resource group r, represents the intensity value of request q in resource group r, represents the intensity value of the kth request except request q in resource group r, represents the number of requests except request q in resource group r, represents the sum of intensity values of the remaining requests in resource group r, represents the timestamp of request q in resource group r, represents the average value of all request timestamps in resource group r, represents the absolute value of the difference between the timestamp of request q and the average value of the timestamp in resource group r.

[0036] As a further scheme of the present application, the specific step of S4 is:

[0037] S401: call the continuous running data in the resource overlapping request result, extract the numerical difference value of the node at adjacent time points, compare the difference value domain change amplitude item by item, and judge with the set numerical jump threshold value, filter the time points with jump amplitude exceeding the threshold value, and generate the mutation time set;

[0038] S402: According to the mutation time set, the time record information of the corresponding node is called, each mutation time point is matched with the time value in the node running time sequence, a section with multiple node mutation time overlaps is identified, and a time overlap dense section interval is obtained;

[0039] S403: For each time section in the time overlap dense section interval, the node identifier of the simultaneous mutation behavior is extracted, the repeated nodes are removed and sorted by time grouping, the node set with synchronous mutation characteristics is refined, and a co-time mutation node identifier group is established;

[0040] Definition of difference value range change amplitude: the fluctuation interval of the numerical difference value of the adjacent time points of the node in a short time;

[0041] Definition of numerical jump threshold: the numerical change amplitude limit for judging numerical mutation;

[0042] Definition of node set with synchronous mutation characteristics: the node identifier group in which the numerical values of multiple nodes are mutated in the same time section.

[0043] As a further scheme of the present application, the specific steps of S5 are:

[0044] S501: According to the node path information listed in the co-time mutation node identifier group, the forwarding channel identifier and the corresponding load value currently used by the node in the path are extracted, the channel and load combination is retrieved in turn according to the node path order, and a channel load mapping table is established;

[0045] S502: According to the channel load value in the channel load mapping table, the channel state record of the photovoltaic communication router is called, the channel enable state identifier in the same time section is extracted, and the channel load value and the enable state are compared to determine whether they are simultaneously in the abnormal range. Filter the channel numbers that meet the replacement condition to generate a replaceable channel set;

[0046] S503: Call the node path information corresponding to the replaceable channel set, compare the channel number order of the path with the replacement number position, rearrange the channel sequence, extract the changed path number structure, and establish a distributed photovoltaic power supply path change list;

[0047] Definition of abnormal range: the numerical interval in which the channel load value and the enable state value exceed the normal working threshold.

[0048] The distributed photovoltaic power supply system based on big data interconnection comprises:

[0049] The node trend extraction module obtains photovoltaic node power and load time sequence values, compares power change directions of adjacent time periods and records reversal times, calculates power difference values and compares them with fluctuation identification reference values, extracts time points with change amplitudes exceeding limits, integrates reversal and mutation times, and generates node behavior change positions;

[0050] The behavior offset comparison module extracts power directions of power generation and load nodes according to the node behavior change positions, judges direction consistency, screens trend synchronization points, calculates power difference values, extracts time periods with difference values exceeding limits and performs time comparison with edge records, and generates a time synchronization offset range set;

[0051] The repeated request screening module reads node requests in the time synchronization offset range set, screens resource name repeated records and groups them, extracts request intensity and time of each group, performs product sorting and reconstructs request order, and generates a resource overlapping request result;

[0052] The mutation co-time refining module calls node power data in the resource overlapping request result, compares adjacent power changes and mutation identification values, extracts mutation time points and performs cross comparison with node behavior change positions, screens repeated time mutation nodes, and generates a co-time mutation node identification group;

[0053] The path line updating module queries power supply path and channel time consumption data according to the co-time mutation node identification group, judges whether the time consumption exceeds a limit value, screens replacement channels and updates path order, and generates a distributed photovoltaic power supply path change list.

[0054] Compared with the prior art, the advantages and positive effects of the present application are that:

[0055] In the present application, behavior change sequences are constructed by node change trends and mutation amplitudes, time sequence differences are calibrated by direction consistency and offset range, resource concentration features are expressed by request intensity and time, co-time node groups are located by continuous mutation time points, replacement channels are screened and path order is adjusted by path load, and the effects are that node behavior recognition is accurate, resource request response is ordered, co-time event positioning is efficient, and path scheduling is dynamically adaptive, and the response capability and power supply coordination in a multi-node change scenario are improved. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0057] Figure 1 It is a step flowchart of the present application.

[0058] Figure 2 System module diagram of the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the present application will be described below with reference to the drawings.

[0060] In the embodiments of the present application, the words such as “example”, “for example” are used to represent an example, illustration or description. Any embodiment or design scheme described as “example” in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word “example” is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by “and / or” can be both, or can be one of the two.

[0061] In the embodiments of the present application, “image” and “picture” can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. “Of”, “corresponding” and “corresponding” can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0062] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0063] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail with reference to the drawings and specific embodiments.

[0064] Please refer to Figure 1 , the distributed photovoltaic power supply method based on big data interconnection includes the following steps:

[0065] S1: Collecting time series data from photovoltaic nodes, comparing the value change direction of consecutive time periods, recording the trend turning point, constructing the difference sequence, judging whether the difference jump degree meets the mutation standard, merging the positions meeting the conditions with the trend turning points, and obtaining the node behavior change position;

[0066] S2: According to the node behavior change position, extracting the change direction of the power generation and load nodes, positioning the trend synchronization point after judging the consistency of the direction, comparing the trend difference of the synchronization point, marking the change offset area, combining the time period record of the photovoltaic edge node controller to establish the time offset range, and outputting the time synchronization offset range set;

[0067] S3: Read the node request content in the time synchronization offset range set, filter the resource target duplicate request group, extract the request intensity and time value of each group, perform product sorting, reorganize the behavior request sequence in order, and output the resource overlap request result;

[0068] S4: Call the continuous running data of the resource overlap request result, identify the value mutation point, extract the mutation time set, cross compare with the node time set, verify the time coincidence intensive section, and get the co-time mutation node identification group;

[0069] S5: According to the node path listed in the co-time mutation node identification group, search the current forwarding channel and load condition, combine the photovoltaic communication router record to filter the channel to be replaced, adjust the path order, and output the distributed photovoltaic power supply path change list.

[0070] The node behavior change position includes trend turning time, difference mutation amplitude, trend change direction, the time synchronization offset range set includes synchronization offset start and end time, trend difference range, and direction consistency identification, the resource overlap request result includes duplicate resource target, request intensity value, and reorganization sorting index, the co-time mutation node identification group includes mutation occurrence time, time coincidence interval, and mutation node number, and the distributed photovoltaic power supply path change list includes the channel to be replaced number, the adjusted path order, and the channel current load value.

[0071] The specific steps of S1 are:

[0072] S101: Collect continuous time sequence data from photovoltaic nodes, compare the increase and decrease relationship of adjacent two time points in turn, record the time point when the value change direction is reversed, and generate the trend change time sequence;

[0073] The continuous time series data is collected from the photovoltaic node, and the specific operation is to collect the current output power data of the node every 10 seconds through the communication module, for example, 60 data points are collected continuously from 10:00 to 10:10, each data point includes a time stamp and a corresponding power value, and the data storage structure is in the form of an array list indexed by time, for example, the first item is [10:00:00, 98W], the second item is [10:00:10, 102W], and when comparing in sequence, from the second time point, the numerical comparison is performed with the previous time point, for example, the second item 102W is compared with the first item 98W, it is judged that it is in the rising direction, the third item 106W is compared with the second item 102W, it is still rising, until it is found that the power value of a time point starts to decrease, for example, the fifth item 107W is smaller than the fourth item 109W, which is in the falling direction, at this time, the direction is reversed from continuous rising to falling, and the time of the fourth item is recorded as the trend reversal point, and the comparison is continued backward, if the sixth item 104W and the seventh item 100W continue to fall, but the eighth item 96W rises to 101W, the time corresponding to the eighth item is recorded as a new reversal point, all judgments are based on the numerical relationship between the adjacent two points, and the traversal operation is performed in sequence until the last data point, every time the trend direction changes from rising to falling or from falling to rising, it is judged that the trend reverses and the corresponding time point is recorded, through the above execution process, the trend change time in the whole time sequence can be extracted and recorded one by one, and a complete trend change time sequence is formed.

[0074] S102: Calculate the difference value of adjacent values based on the time series data, compare the difference value with the variation amplitude of the difference value before and after, judge whether it exceeds the difference value jump amplitude threshold, and generate a sudden jump position sequence;

[0075] After acquiring the photovoltaic node continuous time sequence power data, the difference between the values of the adjacent two time points is compared item by item, that is, the first item is subtracted from the second item, and so on to obtain the difference sequence of each item. For example, the input data is [98, 102, 106, 109, 107, 104, 100, 96, 101, 106] W, and the difference sequence is [4, 4, 3, -2, -3, -4, -4, 5, 5]. The amplitude difference of each difference value and the previous and next difference values is compared, for example, the fourth difference value -2 is subtracted from the previous difference value 3 and the next difference value -3 to obtain the difference value change amplitude 5 and 1, respectively. It is judged whether there is a mutation, and the difference jump amplitude threshold Tdiff needs to be set. The threshold is set according to the output fluctuation range of the photovoltaic equipment and the on-site interference condition. Generally, the photovoltaic output fluctuation amplitude is small in sunny days, within ±3 W, and is large in cloudy days, up to ±7 W. In order to avoid misjudgment, Tdiff can be set as 6 W as the jump judgment standard. For the point whose difference value change amplitude is greater than or equal to the threshold, it is judged to be a jump point. For example, the eighth item value in the above difference value sequence is 5, which is different from the previous difference value -4 by 9 and from the next difference value 5 by 0. Only one direction difference value exceeds the threshold, which does not meet the jump condition. If the node power data is [150, 155, 161, 145], the difference value sequence is [5, 6, -16], and the third item difference value -16 is different from the previous difference value 6 by 22 and from the next value by 0. Since the current point is located at the boundary, but the difference amplitude 22 is much larger than Tdiff=6, it is judged that the time point is a jump point. The process continues to be executed for the whole data in turn to form the mutation jump position sequence composed of all jump points.

[0076] S103: Based on the trend change time sequence and the mutation jump position sequence, all time points in the merged sequence are extracted as the behavior change points of the node, and the node behavior change position is generated.

[0077] After obtaining the trend change time sequence and the mutation jump position sequence, first, the two sequences are loaded into the memory to form two list structures. In the example, the trend change time sequence is [10:03, 10:07, 10:12], and the mutation jump position sequence is [10:05, 10:07, 10:15]. All elements in the two lists are read one by one, and all time points are collected into a new list. During the execution process, for repeated time points such as 10:07, only one is recorded. By programming traversal, the elements in the two lists are compared one by one, and the non-repeated elements are appended to the merged list to obtain the preliminary set [10:03, 10:07, 10:12, 10:05, 10:15]. Then, the list is sorted in time sequence. The elements are sorted in ascending order by comparing hours, minutes, and seconds. Finally, the behavior change point set arranged in time sequence is obtained as [10:03, 10:05, 10:07, 10:12, 10:15]. This process does not need to call a complex model. Only three steps of list traversal, de-duplication, and sorting can complete the final formation of the node behavior change position sequence.

[0078] The specific steps of S2 are:

[0079] S201: According to the node behavior change position, the value change direction of the power generation node and the load node at the same time point is extracted, the increase and decrease trend directions are compared one by one, the time point set with consistent change direction is screened, and the trend consistent time sequence is generated.

[0080] First, each time point in the previously generated node behavior change position sequence is called, and the power data of the power generation node and the power or current data of the load node corresponding to the time point are called one by one. The current value and the previous value of the power generation node and the load node at the time point are called respectively, and the comparison between the two values is performed. If the current value is greater than the previous value, it is judged as an upward trend, otherwise as a downward trend. For a specific example, if the power generation node is 160W at 10:05 and 155W at 10:04, it is considered as an upward trend at the power generation end. If the corresponding time of the load node is 170W and 175W respectively, the load is a downward trend. The two trend directions are represented by positive and negative signs, for example, “+” for upward and “−” for downward. Then, the directions are compared. If the directions are consistent (i.e., both are “+” or both are “−”), the time point is marked as a direction consistent point. If they are not consistent, the time point is skipped and not recorded. The above operation is repeated for all node behavior change points in the traversal process to form a preliminary screening list. In this process, attention should be paid to processing boundary data, such as the first or last time point in the time sequence. It is necessary to judge whether it has the data of the previous or next time point to support the direction judgment. For data that does not meet the integrity, the time point is not judged and removed. Finally, all time points that meet the conditions and have consistent directions are collected to form the trend consistent time sequence.

[0081] S202: Based on the time points in the trend consistent moment sequence, the numerical difference values of the power generation node and the load node at the time point and the time period before and after the time point are called, the upstream and downstream trend change values are calculated, the coefficients are compared with the trend deviation threshold value, the positions exceeding the threshold value are extracted, and the trend deviation interval set is obtained;

[0082] The upstream and downstream trend change value calculation formula is specifically:

[0083]

[0084] Among them, represents the upstream and downstream trend change value of the i-th time point, represents the power generation node power of the i-th time point, represents the load node power of the i-1-th time point, represents the power generation node power of the i+1-th time point, represents the load node power of the i-th time point, represents the power generation node value of the i-th time point, represents the load node value of the i-th time point.

[0085] The definition of the upstream and downstream trend change value is explained as follows:

[0086] The upstream and downstream trend change value is a kind of index for measuring the power change characteristics between the power generation node and the load node at a certain time point and its adjacent time period. The value comprehensively considers the power value difference between the power generation side and the load side in the current time point and the adjacent time point, reflects the dynamic fluctuation of power supply and demand in the local time period in the power grid. The index not only reflects the change rate of power in the time dimension, but also contains the transmission structure in space, that is, the corresponding relationship between power generation and load, which is an important characterization of the state change in the short period of the power system.

[0087] The value has the following characteristics:

[0088] Time correlation: by analyzing the data difference of adjacent time points, the rising or falling trend of the trend is revealed;

[0089] Spatial contrast: through the numerical comparison between upstream and downstream nodes, the imbalance degree of energy flow is expressed;

[0090] Sensitivity: the numerical size is sensitive to system fluctuation, which can capture small disturbances;

[0091] Stability control value: used to identify the trend deviation area and judge whether there is a structural change.

[0092] The calculation principle of the upstream and downstream trend change value is as follows:

[0093] ​The calculation process is based on the numerical difference of the generation node and the load node at three consecutive key points (previous point, current point, next point). The sum of the differences is used as the dominant quantity of the trend. A correction factor based on voltage difference is introduced to normalize and adjust the local grid state changes.

[0094] The specific calculation logic is as follows:

[0095] Local difference summation: Take the current point of the power generation minus the previous point of the load power, and the next point of the power generation minus the current point of the load power. The sum of the two results represents the net change of power in the short term at both supply and demand ends.

[0096] Normalization processing: Due to the difference in voltage level in different grid structures and different time periods, which will cause nonlinear response of power change, the square root of voltage difference is used to construct the normalization denominator, and a constant term is introduced to prevent division by zero and other abnormal situations, so as to realize the equivalent standardization of power difference between different nodes.

[0097] Take the absolute value: Avoid the mutual offset of different directions, and emphasize the magnitude expression of fluctuation amplitude.

[0098] This calculation principle combines three dimensions of difference, fluctuation amplitude and voltage state correction, accurately represents the trend deviation degree of power system in short time scale from numerical point of view, and is an important basic parameter for extracting trend deviation interval and identifying abnormal period.

[0099] Parameter acquisition and numerical setting:

[0100] Pi: The power value of the i-th point of the generation node. It is obtained by monitoring the output power of the generation node. According to the data of Dinorwig pumped storage power station in the UK, its output power is 1.8GW. In this example, set MW.

[0101] Pi-1: The power value of the i-1-th point of the load node. It is obtained by monitoring the power consumption of the load node. According to the data of the National Renewable Energy Laboratory in the United States, the typical power consumption of the load node is about 1500MW. Set MW.

[0102] Pi+1: The power value of the i+1-th point of the generation node. It is obtained by prediction model or actual monitoring. Set MW.

[0103] Pi: The power value of the i-th point of the load node. It is obtained by monitoring the power consumption of the load node. Set MW.

[0104] : Voltage value of the i-th time point generation node. Obtained by voltage sensor measurement. Let kV.

[0105] : Voltage value of the i-th time point load node. Obtained by voltage sensor measurement. Let kV.

[0106] Formula calculation process:

[0107] Calculate the numerator part:

[0108] ;

[0109] Calculate the denominator part:

[0110] ;

[0111] ;

[0112] Result explanation:

[0113] The result shows that the upstream and downstream trend change value at the i-th time point is about 96.15. This value reflects the power change of the generation node and the load node at the adjacent time point, and considers the influence of voltage difference. Higher value may indicate that the power grid has a larger power fluctuation at this time point, which needs to be further analyzed for its impact on power grid stability.

[0114] S203: According to the time period position in the trend offset interval set, call the time period record data of the photovoltaic edge node controller, match the start and end time of the controller response record in the interval, extract the time span, and establish a time synchronization offset range set;

[0115] According to the time period position in the trend offset interval set, the start and end time points in the set are read one by one, and the response log information recorded in the photovoltaic edge node controller is called in the form of a timestamp, and all response records of the controller in these time periods are found. For each trend offset interval, it is matched whether the timestamp in the controller response record falls within the interval. If the start time of a response record is after the start point of the offset interval and the end time is before the end point of the interval, it is considered that the response record completely falls within the offset interval. If the start or end time of the response record slightly exceeds the offset interval range but is within 1 minute, it is also considered to be partially matched. The time span extraction method is to record the start and end time difference of the controller response, and convert it into minutes or seconds as the response duration. In this process, the time format difference in the response record needs to be handled, and the timestamp format is used for comparison and matching. The extraction operation is performed on the time period in which the controller response record exists in all offset intervals, and the start time, end time and time length are recorded to form a complete time synchronization offset range set. For example, a certain offset interval is 10:15 to 10:20, and the controller response record time is 10:16 to 10:18. Then this paragraph matches successfully and extracts a time span of 2 minutes. In this way, the response matching and time span extraction process in all intervals are completed one by one, and finally the time synchronization offset range set is constructed.

[0116] The specific steps of S3 are:

[0117] S301: Read the node request content in the time synchronization offset range set, extract the resource target identifier in each request, compare the repetition of the resource target identifier, filter multiple request records with the same resource target, and establish a resource target repeated request group list;

[0118] First, the start and end time of each time period is extracted from the time synchronization offset range set output in the previous stage, and the resource request records initiated by the generation node or load node in the time range are called. These request data are traversed one by one, and the resource target identification field recorded in each request is extracted. This field is usually a resource unique code or name string, such as "RES_A01", "RES_B05", etc. The resource identification values extracted from all records are compared one by one, and the string equal comparison method is used to determine which requests have the same resource target identification. For request records with the same resource identification, the corresponding original request number and timestamp are recorded and grouped into the same group. Through multiple loops, multiple request groups are constructed, and all requests in each group have the same resource target identification. For the same resource target, strict character equality comparison is used instead of fuzzy matching to ensure that the resource entity in each group is consistent. For example, in the offset interval [10:10-10:20], three request records appear, and their resource target fields are all "RES_X01". These three records are grouped into a repeated request group. If another group has two requests for "RES_Y01", it forms another group. Requests that appear only once do not form a group and are directly excluded from subsequent processing. Finally, a repeated request group list is established with the resource target as the index. Each element contains the resource identification, all request numbers in the group, and the corresponding time.

[0119] S302: According to the resource target repeated request group list, the intensity value and timestamp of each request in the group are extracted, the product index of intensity and time value is calculated, the requests are sorted by product value, the request structure is arranged according to the product value, and the behavior request sequence is generated.

[0120] The product index calculation formula of intensity and time value is as follows:

[0121] ;

[0122] Wherein, represents the product index of intensity and time value of request q in resource group r, represents the intensity value of request q in resource group r, represents the intensity value of the kth request in resource group r except request q, represents the number of requests in resource group r except request q, represents the sum of intensity values of the remaining requests in resource group r, represents the timestamp of request q in resource group r, represents the average value of all request timestamps in resource group r, represents the absolute value of the difference between the timestamp of request q and the average value of the timestamps in resource group r;

[0123] The product of intensity and time value index is used to measure the weight characteristic value of a certain resource request at a specific time point, which is defined as: combining the intensity value of a single request with its corresponding timestamp, reflecting the relative importance of the request in the overall behavior sequence by fusing the time position of the request in the resource group and its intensity performance. This index not only considers the intensity level of the request itself, but also comprehensively considers the timing relationship difference between the request and other requests in the same group on the time axis.

[0124] The calculation principle of the index is based on the following logic:

[0125] Intensity level: the intensity value of a single request is directly reflected as a measure of resource consumption, which is usually obtained by counting the number of bytes generated by each request response in the server log. The larger the value, the stronger the request occupies the resources.

[0126] Time level: the timestamp reflects the occurrence time of the request in the entire resource behavior sequence. The difference between the timestamp of the request and the average timestamp of the requests in the same group is calculated to obtain the timing offset of the request in the group, and then the synchronization degree of the request and the behavior pattern in the group is described.

[0127] Molecular composition: the intensity value of the request is added to the sum of the intensities of other requests in the same group to form an extension of the comprehensive expression of the request in the local intensity structure, and then multiplied by its timestamp, so that earlier or later initiated requests obtain different degrees of product amplification according to their intensity performance.

[0128] Denominator adjustment: a timing difference factor is constructed by the absolute value of the difference between the request and the average timestamp of the resource group it belongs to, and is scaled by square root, so that the request closer to the time of other requests in the group has a more significant impact on the final index, and vice versa, the product weight of the request deviating from the main behavior rhythm is reduced, and the sorting weight of the request is reduced.

[0129] Absolute value processing: finally, the absolute value of the product value is taken to ensure that the product index is non-negative in the behavior characteristic expression, which is convenient for subsequent sorting and comparison operations.

[0130] Overall, the product of intensity and time value index is a composite measure value that combines intensity scale and timing position characteristics. By weighted accumulation of local request intensity structure and synchronization analysis of global behavior rhythm, the significance of each request in resource access behavior is quantified.

[0131] Parameter acquisition and value setting:

[0132] Request intensity : the response byte size of each request in the Web server access log is obtained by analyzing the response byte size of each request in the Web server access log. For example, the response size of a request is 2048 bytes, then .

[0133] Other request strength sum in the same group : Sum the response byte size of all requests in the same resource group except the current request. For example, the response size of the other three requests is 1024, 512 and 256 bytes respectively, then the sum is bytes.

[0134] Request timestamp : Extract the timestamp of the request from the server log and convert it to the number of seconds since the UNIX epoch. For example, the timestamp is June 4, 2025 05:00:00, which is converted to 1749022800 seconds.

[0135] Resource group average timestamp : Average the timestamps of all requests in the resource group. For example, the timestamps of four requests in the resource group are 1749022800, 1749022860, 1749022920 and 1749022980 seconds respectively, then the average is seconds.

[0136] Formula calculation process:

[0137] Calculate the numerator part:

[0138] ;

[0139] Calculate the denominator part:

[0140] ;

[0141] Calculate the product value :

[0142] ;

[0143] Result explanation:

[0144] The result shows that the product value of request q in resource group r is about 705264000000. This value is used to sort the requests and construct the request structure arranged by the product value to generate the behavior request order sequence.

[0145] S303: Call the resource target and time information of the request in the behavior request order sequence, screen the request group with overlapping time periods, locate the occupation of multiple requests to the same resource in the same time period, and establish the resource overlapping request result;

[0146] The resource target and time information of the requested resource in the behavior request order sequence is called first, the request items in each request group are read according to the resource target, then the time periods of the requests in the group are compared, the time period includes the start and end time, if a record only contains a time point, the duration time needs to be derived according to the request type, for example, set the default duration of ordinary request as 60 seconds, after obtaining the time period of each request, the two-by-two comparison operation is performed on all requests in the group, and whether the time periods overlap is compared, the specific judgment is: if the start time of a request is less than the end time of another request, and the end time is greater than the start time of another request, it is considered that the two requests overlap in time and occupy the same resource, all request combinations in the group are processed according to the above judgment logic, for example, the time period of request 1 is [600, 660] seconds, and the time period of request 2 is [630, 690] seconds, because there are 630≤660 and 600≤690, the overlap condition is met, and it is added to the resource overlap mark structure, the structure needs to include the resource target identifier, the conflict request number list, the conflict time period range and other fields, the conflict sub-result is generated for all overlapping requests in each group, if there are multiple conflict request pairs in a group, all overlapping combinations are recorded respectively, and finally all conflict request combinations on the resource target are merged and arranged to establish the resource overlap request result.

[0147] The specific steps of S4 are:

[0148] S401: Call the continuous running data in the resource overlap request result, extract the numerical difference value of the node at the adjacent time points, compare the difference value range change amplitude item by item, and judge with the set numerical jump threshold value, filter the time points with jump amplitude exceeding the threshold value, and generate a mutation time set;

[0149] The continuous running data in the resource overlap request result is called first. The relevant node identifier and its corresponding continuous running data are extracted from the established resource overlap request result one by one. The running values of the node at adjacent two time points are read in sequence. The values can be current, voltage or power, etc. Starting from the second item, the sequential traversal method is used to calculate the difference value between the current value and the previous value, that is, the current value minus the previous value to obtain the difference value. Then the change amplitude of the obtained difference value and the previous and next difference values is compared. The specific operation is to compare the absolute difference between the current difference value and the previous difference value, and the absolute difference between the current difference value and the next difference value. For example, the power values of the node at time points 10:00, 10:01 and 10:02 are 120W, 125W and 140W, respectively. The adjacent difference values are 5W and 15W, respectively. The difference between the two difference values is 10W. The value is compared with the set value jump threshold. If the change amplitude is greater than the jump threshold, the current time point is determined as a mutation point. The jump threshold is set according to the equipment measurement accuracy and the actual running fluctuation rule. If the normal change of the equipment is within ±6W, the jump threshold can be set to 10W, which is used to identify the mutation behavior exceeding the normal fluctuation range. For example, the power changes from 130W to 160W in one sampling period, and the difference is 30W, which is much larger than the threshold 10W. The point can be marked as a mutation point. The above difference value acquisition and difference amplitude comparison process is performed for each time point in the continuous record. If the result is greater than the set threshold, the time point is added to the mutation time set. Finally, the traversal is completed and all the mutation time sets that meet the conditions are generated.

[0150] S402: According to the mutation time set, the time record information of the corresponding node is called. Each mutation time point is matched with the time value in the node running time sequence to identify a section in which multiple node mutation times coincide, and a time coincidence intensive section interval is obtained.

[0151] According to the mutation time set, read the time points in the set one by one, and call the node identifier corresponding to each mutation point and the complete time sequence record information of the node. Match the mutation time point with the running time sequence of the node item by item. The specific way is to compare the mutation time point with the time field in the node running record one by one according to the timestamp format. If the matching is successful, it is recorded that the node has mutation behavior at this time point. The information is appended to the matching result table. Continue to traverse all nodes and mutation times, and construct the mapping table between all mutation behavior times and nodes. Then aggregate the mapping table according to the time dimension. Count the number of nodes that have mutation behavior at each time point. If the number of nodes corresponding to a time point is greater than 2, that is, multiple nodes have mutation at the same time point, it is considered that the time point has time coincidence behavior. Collect all time points that meet the condition in order, and further judge whether these time points are continuous. If the interval between continuous time points is less than a set tolerance value, for example, the interval does not exceed 1 minute, they are merged into a time period to form a time coincidence dense segment interval. For example, if there are three time points 10:02, 10:03 and 10:05 in the mutation set, the first two points can be combined into [10:02-10:03] segment with a difference of 1 minute, and 10:05 is not combined with 10:03 with a difference of 2 minutes, so it is taken as a new segment starting point. Finally, complete the aggregation and continuity judgment of all mutation times, form multiple node mutation time coincidence segments, and construct a time coincidence dense segment interval.

[0152] S403: For each time period in the time coincidence dense segment interval, extract the node identifier that has mutation behavior at the same time, remove duplicate nodes and sort them by time group, extract the node set with synchronous mutation characteristics, and establish a co-time mutation node identifier group;

[0153] For each time period in the time-coincident dense section interval, read the section start and end times one by one and query all nodes in the section that have mutations in the node mutation mapping table, extract all node numbers that meet the time range condition to form an initial node list, perform de-duplication processing on the node numbers in the list to obtain a unique node identification set after removing duplicate records, then group the node mutation records according to the time dimension to ensure that the mutation behavior of the same node in different time periods is classified separately, for example, node A mutates at 10:02 and 10:03, then it is classified into the corresponding time points according to two records respectively, perform the node collection operation on all time periods to obtain an independent node set with mutation behavior under each section, and then arrange the node numbers in each set in ascending order to make the output result structure consistent and facilitate comparison, for example, the node list in the time period [10:02-10:03] is [A, C, B, A], after de-duplication, it is [A, B, C], and then it is sorted in dictionary order as [A, B, C], finally a node list with simultaneous mutation behavior in the same time period is obtained, and the node collection, de-duplication and sorting operations are performed on all time periods to build multiple independent co-time mutation node sets, and the co-time mutation node identification groups are output after summarizing.

[0154] The specific steps of S5 are:

[0155] S501: According to the node path information listed in the co-time mutation node identification group, extract the forwarding channel identifier and the corresponding load value currently used by the node in the path, retrieve the channel and load combination in turn according to the node path order, and establish a channel load mapping table;

[0156] Firstly, read the node number in each group of isochronous mutation node identification, and call the path data structure of each node at the current time in turn. The path data structure needs to record the sequence of the forwarding channel number used in the communication path to which the node belongs, for example, the path of node A is [CH_01, CH_03, CH_05], which means that node A sends information out through three forwarding channels in turn. During the calling process, the number of each channel is obtained, and the current load value of the node on the channel is further extracted from the node running data. The load value can be the traffic data (such as kbps) or the power transmission amount (such as W) transmitted on the current channel. For each node, the channel number and the corresponding real-time load value are extracted in the path order to form the correspondence between the channel and the load, for example, the path of node B is [CH_02, CH_04], and the corresponding loads are 120 kbps and 190 kbps respectively. Write the correspondence into a structure, with "node identification-channel number-load value" as the basic recording unit. Traverse all the nodes in the isochronous mutation node identification group and their path channel information in turn, and accumulate to build the combination information of all related channels and their load values. Pay attention to the situation of repeated channels or abnormal values in the path during the process. The channel number needs to be de-duplicated, and the channel data with invalid load value needs to be excluded, for example, if the load value returned by a channel is "-1" or "NULL", it is excluded. Finally, all valid channel numbers and corresponding load values are recorded and arranged into a complete channel load mapping table.

[0157] S502: According to the channel load value in the channel load mapping table, call the channel state record of the photovoltaic communication router relay, extract the channel enable state identification in the same period, compare whether the channel load value and the enable state are in the abnormal range at the same time, filter the channel numbers that meet the replacement condition, and generate a replaceable channel set;

[0158] First, read the channel number and its corresponding load data in the table one by one, and then according to the time stamp range of each record, call the channel state record data of the photovoltaic communication router repeater, which needs to include the enable state field of each channel in each time period. The field uses a Boolean type or a state code to record, for example, "0" for disabled, "1" for enabled, "2" for fault interruption, etc. For each channel record, first compare whether its load value falls within the abnormal load range. The abnormal range needs to set the threshold in combination with the design capacity of the communication system, for example, the maximum design bandwidth of a certain channel is 200kbps, then the abnormal load threshold can be set to ≥180kbps. If the current load value of a certain channel is 185kbps, it is judged to be abnormal. Then compare whether its enable state is abnormal. For example, the state code is "2" indicating communication failure. If the load value exceeds the limit and the enable state is abnormal at the same time, the channel meets the replacement condition, and its channel number is added to the replaceable channel preliminary selection list. When setting the abnormal judgment threshold, a safety margin should be set according to the design tolerance range, such as setting the high load threshold to 90% of the maximum bandwidth. At the same time, the repeater state record should meet the time period completely consistent with the load record. For the inconsistent part, make time alignment compensation, for example, the repeater state updates every 5 minutes and the load sampling is every 1 minute. Based on the repeater record time period, aggregate the judgment, and finally record all the channel numbers that meet the "load abnormality + state abnormality" to generate a replaceable channel set.

[0159] S503: Call the node path information corresponding to the replaceable channel set, compare the channel number sequence of the path with the replacement number position, rearrange the channel sequence, extract the changed path number structure, and establish a distributed photovoltaic power supply path change list;

[0160] First, read each channel number in the replaceable channel set, and find the original node path structure corresponding to the channel in the co-time mutation node path record, confirm the position number of each channel occurrence one by one, for example, in the path [CH_01, CH_05, CH_07], if CH_05 is a replaceable channel, its position in the original path is the second item, then call the backup channel list in the system, select the replaceable channel number to replace the second position in the original path, complete the path rearrangement operation, for example, CH_05 is replaced by CH_06, the changed path is [CH_01, CH_06, CH_07], for path change operation, it is required to ensure that the order of the path number after replacement is not changed, that is, it is not allowed to appear loop or skip number in the path, for example, the original path number is CH_01→CH_02→CH_03, then the number after replacement also needs to be continuously increased or arranged according to the design rule, the same processing is performed on all nodes paths involving replacement operation, and the operation steps of "finding, positioning, replacing and rearranging" are executed item by item, after the rearrangement is completed, the new path number sequence is output, and the path structure before and after the path change is recorded in node units, and finally the complete changed path list is established according to the node number, and the output is the distributed photovoltaic power supply path change list.

[0161] Please refer to Figure 2 , based on the big data interconnection distributed photovoltaic power supply system, comprising:

[0162] The node trend extraction module obtains the photovoltaic node power and load time sequence value, compares the power change direction of adjacent time periods and records the inversion time, calculates the power difference value and compares the fluctuation identification reference value, extracts the time point with the change amplitude exceeding the limit, integrates the inversion and mutation time, and generates the node behavior change position;

[0163] The behavior offset comparison module extracts the power direction of the power generation and load nodes according to the node behavior change position, judges the consistency of the direction, selects the trend synchronization point and calculates the power difference value, extracts the time period with the difference value exceeding the limit and the edge record for time comparison, and generates a time synchronization offset range set;

[0164] The repeated request screening module reads the node request in the time synchronization offset range set, screens the resource name repeated records and groups them, extracts the request intensity and time of each group, performs product sorting and reconstructs the request order, and generates a resource overlapping request result;

[0165] The mutation co-time extraction module calls the node power data in the resource overlapping request result, compares the adjacent power change and the mutation identification value, extracts the mutation time point and cross compares with the node behavior change position, selects the repeated time mutation node, and generates a co-time mutation node identification group;

[0166] The path line updating module queries the power supply path and channel time consumption data according to the isochronous mutation node identification group, judges whether the time consumption exceeds the limit value, filters and replaces the channel, and updates the path ranking to generate a distributed photovoltaic power supply path change list.

[0167] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A distributed photovoltaic power supply method based on big data interconnection, characterized in that, The method comprises the following steps: S1: collecting photovoltaic node timing data, comparing the value change direction of consecutive time periods, recording the trend turning point, constructing the difference sequence, judging whether the jump meets the mutation standard, merging the mutation and turning point position, and obtaining the node behavior change position; S2: based on the node behavior change position, extracting the power generation and load node change direction, judging the consistency, positioning the trend synchronization point, comparing the trend difference, marking the change offset area, combining the edge controller record, and outputting the time synchronization offset range set; S3: reading the time synchronization offset range set, screening repeated resource target request groups, extracting intensity and time value, executing product sorting, and outputting resource overlap request results; S4: calling the continuous operation data in the resource overlap request results, identifying the value mutation point, extracting the mutation time set, and cross comparing with the node time set, verifying the time coincidence intensive section, and obtaining the common time mutation node identification group; S5: according to the node path listed in the common time mutation node identification group, retrieving the current forwarding channel and load condition, screening the channel to be replaced, and outputting the distributed photovoltaic power supply path change list; The node behavior change position includes trend turning time, difference mutation amplitude, and trend change direction. The time synchronization offset range set includes synchronization offset start and end time, trend difference range, and direction consistency identification. The resource overlap request result includes repeated resource target, request intensity value, and reorganization sorting index. The common time mutation node identification group includes mutation occurrence time, time coincidence interval, and mutation node number. The distributed photovoltaic power supply path change list includes the channel to be replaced, the adjusted path order, and the current channel load value. The definition of mutation standard: the mutation amplitude of node behavior value reaches the preset change threshold, indicating value jump; The definition of change offset area: the area marked when the change direction of node behavior is inconsistent with the change direction of power generation and load nodes. 2.The method of claim 1, wherein, The specific steps of S1 are: S101: collect continuous timing data from photovoltaic nodes, compare the increase and decrease relationship of adjacent two time point values in turn, record the time point of value change direction inversion, and generate trend change time sequence; S102: calculate the difference value of adjacent values based on timing data, compare the change amplitude of difference value and the difference value before and after each item, judge whether it exceeds the difference jump amplitude threshold, and generate mutation jump position sequence; S103: based on the trend change time sequence and the mutation jump position sequence, extract all time points in the merged sequence as the behavior change points of the node, and generate the node behavior change position; The definition of difference jump amplitude threshold: the difference amplitude limit for judging whether the difference value of node behavior value has mutation. 3.The method of claim 1, wherein, The specific steps of S2 are: S201: according to the node behavior change position, extract the value change direction of power generation node and load node at the same time point, compare the increase and decrease trend direction one by one, screen the time point set with consistent change direction, and generate trend consistent time sequence; S202: Based on the time point in the consistent trend time sequence, the numerical difference value of the power generation node and the load node at the time point and the time period before and after the time point is called, the upstream and downstream trend change values are calculated, the coefficient is compared with the trend deviation threshold value, the positions exceeding the threshold value are extracted, and the trend deviation interval set is obtained; S203: According to the time period position in the trend deviation interval set, the time period record data of the photovoltaic edge node controller is called, the start and end time of the controller response record in the interval is matched, the time span is extracted, and the time synchronization deviation range set is established; Definition of increase and decrease trend direction: the increase and decrease trend of the node value at consecutive time points compared with the previous time point; Definition of trend deviation threshold value: the change coefficient limit for judging whether the power generation and load change trend of the node deviates or not.

4. The method of claim 3, wherein, The upstream and downstream trend change value calculation formula is specifically: ； wherein, a value representing an upstream trend change at the i-th point in time, a value representing a generation node power at the i-th point in time, a value representing a load node power at the i-1-th point in time, a value representing a generation node power at the i+1-th point in time, a value representing a load node power at the i-th point in time, a value representing a generation node value at the i-th point in time, a value representing a load node value at the i-th point in time. 5.The method of claim 1, wherein, The specific steps of S3 are: S301: Read the node request content in the time synchronization deviation range set, extract the resource target identifier in each request, compare the repetition of the resource target identifier, filter multiple request records with the same resource target, and establish a resource target repeated request group list; S302: According to the resource target repeated request group list, the intensity value and timestamp of the request in each group are extracted, the product index of intensity and time value is calculated, the product value of the request is sorted, the request structure arranged according to the product value is constructed, and the behavior request order sequence is generated; S303: Call the resource target and time information of the request in the behavior request order sequence, screen the request groups with overlapping time periods, locate the occupation of multiple requests to the same resource in the same time period, and establish a resource overlapping request result; Definition of multiple request records with the same resource target: in the time synchronization deviation range, multiple request sets pointing to the same resource identifier. 6.The method of claim 5, wherein, The intensity and time value product index calculation formula is specifically: ； wherein, an indicator representing the product of the intensity of request q in resource group r and the time value, an indicator representing the intensity value of request q in resource group r, an indicator representing the intensity value of the kth request other than request q in resource group r, an indicator representing the number of requests other than request q in resource group r, an indicator representing the sum of the intensity values of the remaining requests in resource group r, an indicator representing the timestamp of request q in resource group r, an indicator representing the average of all request timestamps in resource group r, an indicator representing the absolute value of the difference between the timestamp of request q and the average of the timestamps in resource group r. 7.The method of claim 1, wherein, The specific steps of S4 are: S401: Call the continuous running data in the resource overlapping request result, extract the numerical difference value of the node at adjacent time points, compare the difference value range change amplitude item by item, and judge with the set numerical jump threshold value, filter the time points with jump amplitude exceeding the threshold value, and generate a mutation time set; S402: According to the mutation time set, call the time record information of the corresponding node, match each mutation time point with the time value in the node running time sequence, identify the section with multiple node mutation time coincidences, and obtain the time coincidence intensive section interval; S403: For each time period in the time coincidence intensive section interval, extract the node identifier with simultaneous mutation behavior, remove the repeated nodes and sort them by time group, extract the node set with synchronous mutation characteristics, and establish a co-time mutation node identifier group; Definition of difference value range change amplitude: the fluctuation interval of the numerical difference value of the node at adjacent time points in a short time; Definition of numerical jump threshold value: the numerical change amplitude limit for judging numerical mutation; Definition of node set with synchronous mutation characteristics: in the same time section, the node identifier group in which the numerical values of multiple nodes are mutated. 8.The method of claim 1, wherein, The specific steps of S5 are: S501: According to the node path information listed in the co-time mutation node identification group, the forwarding channel identification and the corresponding load value currently used by the node in the path are extracted, the channel and load combination is retrieved in turn according to the node path order, and the channel load mapping table is established; S502: According to the channel load value in the channel load mapping table, the channel state record of the photovoltaic communication router is called, the channel enable state identification in the same period is extracted, and the channel load value and the enable state are compared to determine whether they are in the abnormal range at the same time. The channel number that meets the replacement condition is filtered to generate a replaceable channel set; S503: The node path information corresponding to the replaceable channel set is called, the channel number order of the path is compared with the replacement number position, the channel sequence is rearranged, the changed path number structure is extracted, and the distributed photovoltaic power supply path change list is established. Definition of abnormal range: the value interval of channel load value and enable state value exceeding the normal working threshold.

9. A distributed photovoltaic power supply system based on big data interconnection, characterized in that, The distributed photovoltaic power supply method based on big data interconnection according to any one of claims 1-8, the system comprises: The node trend extraction module obtains the photovoltaic node power and load time sequence value, compares the power change direction of adjacent time periods and records the inversion time, calculates the power difference value and compares the fluctuation identification reference value, extracts the time point with the change amplitude exceeding the limit, integrates the inversion and mutation time, and generates the node behavior change position; The behavior offset comparison module extracts the power direction of the power generation and load nodes according to the node behavior change position, judges the direction consistency, filters the trend synchronization point and calculates the power difference value, extracts the time period with the difference value exceeding the limit and performs time comparison with the edge record, and generates a time synchronization offset range set; The repeated request screening module reads the node request in the time synchronization offset range set, screens the resource name repeated records and groups them, extracts the request intensity and time of each group, performs product sorting and reconstructs the request order, and generates a resource overlapping request result; The mutation co-time extraction module calls the node power data in the resource overlapping request result, compares the adjacent power change and the mutation identification value, extracts the mutation time point and cross-compare with the node behavior change position, filters the repeated time mutation nodes, and generates a co-time mutation node identification group; The path line updating module queries the power supply path and channel time consumption data according to the co-time mutation node identification group, judges whether the time consumption exceeds the limit, filters the replacement channel and updates the path order, and generates a distributed photovoltaic power supply path change list.

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