A control method based on line sampling and measurement

Abstract

The present invention discloses a control method based on line sampling metering, which belongs to the field of metering sampling control. The method includes allocating a number of sampling nodes on a line and configuring a first sampling plan, collecting data items into first line data; classifying the first line data by position coding to obtain a key data set and a basic data set, comparing the data of the key data set with historical data, and determining whether there are abnormal nodes in the key data set that exceed the deviation value by a preset deviation value; determining abnormal areas by the position coding of the abnormal nodes and connecting to collaborative equipment for operation; filtering key time-sensitive data in the key data set by timestamps and obtaining the degree of change of each sampling node; and determining whether the degree of change is within a safety threshold range. If the degree of change is within the safety threshold range, it indicates that the risk is controllable; if the degree of change exceeds the safety threshold range, it indicates that the risk is greater, in order to improve the problem of fuzzy positioning of abnormal areas in the line and limited early warning of potential risks.

Description

Technical Field

[0001] The present invention relates to metering sampling control, and in particular to a control method based on line sampling metering Background Art

[0002] In industrial zones, power lines are crucial infrastructure, and their stability and safety directly impact the production and operation of the entire zone. Current line monitoring methods typically rely on segmented monitoring. When a line problem arises, it requires investigation. However, in some industrial zones, varying power demands create a complex overall line architecture. When problems arise on certain lines, the lack of real-time data processing and analysis capabilities makes it difficult to quickly analyze and assess the large amount of sampled data.

[0003] In the actual line metering and control process, the main method of monitoring the bus and branch lines separately is mainly adopted. In the case of possible interference between lines, the fault location range is large, and a large location range is likely to extend the fault handling time.

[0004] More importantly, even when no anomalies occur during line operation, risk factors must be investigated. This investigation is often manual, relying primarily on experience to determine risk. This lacks a scientific risk assessment mechanism, and effective early warning of potential risks is relatively limited. When risks exceed expectations, data collection and analysis can lag. When potential risk factors increase, the number of emergency response efforts increases. Therefore, it is worthwhile to explore how to optimize the situation where anomalies are difficult to detect, risks are not discovered promptly, and resolution cycles are long. Summary of the Invention

[0005] The purpose of the present invention is to provide a control method based on line sampling and metering, in order to improve the problem that when a line fault occurs, the positioning of the abnormal area is vague, and when risk factors are checked, effective early warning of potential risks is relatively limited.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A control method based on line sampling and metering, wherein the line includes a heavy-load line and a basic line, and multiple interconnection points are set between the heavy-load line and the basic line. The interconnection points are externally connected to collaborative equipment. The method includes the following steps: S100: a plurality of sampling nodes are allocated on the line and a first sampling plan is configured, data items of all sampling nodes in the first sampling plan are obtained, and the data items of all sampling nodes are aggregated into first line data; wherein a timestamp and a position code are added to the data items, and the timestamp and position code correspond to the acquisition time and node code of the data item respectively. The first sampling plan is an execution plan for real-time sampling of all sampling nodes within a set time interval.

[0008] S200, obtaining position codes of sampling nodes corresponding to interconnected points and marking them as key nodes, and marking the position codes of remaining sampling nodes as basic nodes; classifying the first link data according to the position codes to obtain key data sets and basic data sets corresponding to the key nodes and basic nodes.

[0009] At step S300, the key data set is compared with historical data. A predetermined deviation value is used to determine whether there are abnormal nodes in the key data set that exceed the deviation value. If an abnormal node exists, step S400 is executed. If not, the key data set is recorded and step S500 is executed.

[0010] S400, determine the abnormal area through the position code of the abnormal node, access the collaborative device through the interconnection point to work, obtain the data items of the sampling nodes in the abnormal area again to obtain abnormal line data, and repeat S200 to 300 for the above abnormal line data until there is no abnormal node.

[0011] At step S500, the key data set is filtered by timestamp to identify data items from similar time periods and obtain key time-sensitive data. The degree of change for each sampling node in the key time-sensitive data is determined to determine whether the degree of change is within the safety threshold. If the degree of change is within the safety threshold, the risk is controllable. If the degree of change exceeds the safety threshold, the risk is significant. The preset strategy of the collaborative device is used to adjust the working state of the interconnection point on the overloaded line. After the adjustment is completed, steps S100 to S500 are repeated until the degree of change is within the safety threshold.

[0012] A further technical solution is that in S400, when determining the abnormal area, the data items of the abnormal area are retrieved from the basic data set through position coding, and the data items of the same time period are filtered through timestamps to obtain the abnormal data set, and the above abnormal data set is used to be sent to the server for recording.

[0013] A further technical solution is that in S500, the steps for obtaining the above-mentioned degree of change are: S501, extracting all data items of a sampling node in the key timeliness data and obtaining the median P; S502, arranging each data item of the sampling node according to the time sequence of the timestamp; S503, comparing the arranged data items with the median P in turn, and making a curve graph of the comparison results; S504, obtaining the difference between the peak and the trough of the curve graph, and the above-mentioned difference represents the degree of change of the sampling node.

[0014] Preferably, when the degree of change in the sampling nodes is within a safety threshold, a second sampling plan is configured, whereby the second sampling plan acquires a set of data items from all sampling nodes in a time-sharing manner to obtain second line data; wherein the second sampling plan is an execution plan for time-sharing sampling performed by all sampling nodes within a set time interval. Position codes are integrated according to the association requirements of the sampling nodes to obtain a position code set; the second line data is filtered using the position code set to obtain associated line data; wherein the associated line data includes data items sampled multiple times by the sampling nodes corresponding to the position code set; the associated line data is output to a server, which verifies, through the associated line data, whether the sampling nodes corresponding to the position code set are associated.

[0015] A further technical solution is to extract the data items of each sampling node in the associated line data when checking whether the sampling nodes in the position code set are associated, compare the two adjacent data of the same sampling node to obtain the fluctuation amplitude of the sampling node, obtain the fluctuation amplitude of each sampling node in the position code set, and verify the degree of association of the sampling nodes by the fluctuation amplitude of adjacent sampling nodes by the server.

[0016] Preferably, at least two collection devices are set up at the above-mentioned key nodes, and the collection devices all use the same position coding, and each collection device is set with a unique identification code; a number of identification areas are divided on the above-mentioned heavy-load line, and each identification area covers at least two key nodes, and a relay processor is set in the above-mentioned identification area. The above-mentioned relay processor is connected to the collection device signal in the identification area, and the relay processor pre-processes the data items and synchronously transmits the processing results to the server.

[0017] A further technical solution is that when the above-mentioned relay processor performs preprocessing, the above-mentioned relay processor sets an error threshold, obtains data items with the same position code, obtains the weighted average value and degree of deviation of the same data items, compares the degree of deviation with the error threshold, identifies valid data items, and sends the valid data items to the server.

[0018] The calculation formula for the weighted average is: Where D ik is the data item parameter collected by the i-th collection device at the same time point; i is the historical performance weight of the i-th collection device, n is the total number of collection devices in the identification area; D is the weighted average value of the identification area.

[0019] The calculation formula for the degree of deviation is: ΔD=|D i -D|; where ΔD is the degree of deviation, D i is the data item parameter collected by the i-th collection device at the same time point, and D is D i The weighted average value of the corresponding identification area.

[0020] Compare the degree of deviation ΔD with the error threshold; if ΔD ≤ the error threshold, it is considered that the acquisition device is collecting normally and its data item is valid data; if ΔD > the error threshold, it is considered that the acquisition device is collecting doubtful data and its data item is doubtful data.

[0021] A further technical solution is that the above-mentioned relay processor obtains the identification code and position code of the questionable data, and determines the key node corresponding to the questionable data through the cross-position code and identification code; selects the valid data corresponding to the questionable data from the data items in the key node through the timestamp; and the relay processor uploads the valid data corresponding to the questionable data to the server.

[0022] A further technical solution is that the relay processor marks the questionable data, and the relay processor extracts all data items of the questionable collection device through the identification code to obtain analysis data, which is sent to the server for characteristic analysis.

[0023] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0024] The present invention ensures comprehensive coverage of the line by setting sampling nodes, comprehensively acquires data items of the line using a first sampling plan, and through the positional relationship between nodes, by marking key nodes and basic nodes, focusing on interconnected points, and acquiring key data sets and basic data sets. By comparing and analyzing the key data sets with historical data, it is determined whether there are abnormal nodes near the interconnected points. If there are abnormal nodes, the abnormal area is determined by position coding. By introducing the intervention of collaborative equipment, the data items near the key nodes (interconnected points) can be corrected. On the one hand, it is conducive to restoring the normal operation of the line. On the other hand, it is conducive to collecting data on abnormal nodes, thereby obtaining abnormal data more quickly and identifying potential problems (load imbalance or equipment failure).

[0025] When the present invention detects the absence of abnormal nodes in a key data set, it can also detect potential risks within the key data set. The degree of change in the sampling node is determined by timestamps, and the degree of change is calculated and compared with the safety threshold to achieve change monitoring. During this change monitoring process, it is expected that based on real-time feedback on the degree of data change, adaptive adjustments can be made when significant risks are identified. When risks are controllable, the sampling plan can be adjusted, thereby coordinating dynamic monitoring with the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0028] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. in a certain specific working state. If the specific posture changes, the directional indication will also change accordingly. In the present invention, unless otherwise clearly specified and limited, the term "connection" and the like should be understood in a broad sense. For example, "connection" can be an electrical signal connection or a signal connection; it can also be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] If there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0030] refer to Figure 1As shown, one embodiment of the present invention is a control method based on line sampling and metering, wherein the line includes a heavy-load line and a basic line, wherein multiple interconnection points are set between the heavy-load line and the basic line, and the interconnection points are connected to external collaborative equipment. The control method includes the following steps: S100, assigning a number of sampling nodes to the line and configuring a first sampling plan, obtaining data items from all sampling nodes in the first sampling plan, and aggregating the data items from all sampling nodes into first line data. The data items are added with a timestamp and a position code, wherein the timestamp and position code correspond to the acquisition time and node code of the data item, respectively; the first sampling plan is an execution plan for real-time sampling at all sampling nodes within a set time interval.

[0031] Data items can include factors such as voltage, current, power, and load data. Collaborative devices are existing auxiliary power equipment. The specific device type is adaptively configured based on the nature of different line usage. After intervention, collaborative devices are primarily used to improve power factor, stabilize voltage, filter harmonics, and balance loads. Different types of collaborative devices can be used in conjunction through different interconnection points.

[0032] For reference, the collaborative device can be a compensation device. When there is a deviation in the electrical parameters of the line (such as voltage, current or phase, etc.), the collaborative device can intervene in the line and correct these deviations by adjusting the electrical characteristics of the circuit, which is conducive to maintaining the normal operation of the line. The collaborative device intervenes at reasonable nodes to improve the power quality and transmission efficiency of the line. For example, the compensation device can be used when the line is in a substation line. By setting an interconnection point between the focus line and the basic line and connecting an external collaborative device, the work done by the compensation device in different areas can be adjusted in the future to ensure the stable operation of the power system.

[0033] For reference, collaborative devices can be load distribution devices that utilize interconnection points to transfer load. For example, when the voltage of a heavily loaded line is too high or too low, the collaborative device can transfer part of the load to the underlying line to alleviate the pressure on the heavily loaded line. By configuring collaborative devices, the line can dynamically adjust the load distribution between the heavily loaded line and the underlying line.

[0034] For reference, the first sampling plan sets a time interval, which is mainly in hours. The time interval is set to N hours, and the value of N is a natural number between 1 and 24. It should be noted that the specific value of N is generally determined according to the line requirements; if the line requires a longer time for real-time sampling in the initial stage, the larger the value of N is set; conversely, if the line requires a shorter time for real-time sampling in the initial stage, the smaller the value of N is set. In principle, based on the control requirements of line abnormality risks, the value of N is not less than 1.

[0035] The first sampling plan is primarily used in the initial phase of line sampling. It primarily performs real-time sampling, with all sampling nodes performing real-time sampling within a preset time interval. The data collected by all sampling nodes is aggregated into the first line data. Because the first sampling plan utilizes real-time sampling, it collects a significant amount of operational data for the entire line. Aggregating the data into the first line data is primarily based on comprehensive data considerations. This provides foundational data for anomaly assessments and, in addition, stores the first line data in data packets, facilitating the review of periodic data. This avoids the potential for data crosstalk when storing fragmented data and reduces unnecessary data processing losses. The timestamp corresponds to the acquisition time of the data item, and the location code corresponds to the node code of the data item. The addition of timestamps and location codes to each data item in the first line data establishes a time series marker. The timestamps allow verification of the real-time nature of data sampling and accurate identification of the specific time of the data source, facilitating periodic data analysis. Furthermore, the location code ensures the accurate attribution of each data item, facilitating subsequent data analysis and fault location based on node location.

[0036] S200, in order to more accurately monitor and adjust the operating status of the power system, obtain the position code of the sampling node corresponding to the interconnection point and mark it as a key node, and mark the position code of the remaining sampling nodes as a basic node; classify the first line data by the position code to obtain the key data set and basic data set corresponding to the key node and the basic node. Among them, the key data set is used to provide relevant information of the key position, and use the relevant information to quickly respond and adjust in a coordinated manner. The key data set contains the data items of the key nodes. Since the key data set covers all interconnection points, the key data set is used to monitor and analyze the data items of the interconnection points in real time, so as to find anomalies in the key data set. Comprehensive operating information is provided through the basic data set. The basic data set contains the sampling data of all non-key nodes (basic nodes). Its basic nodes are distributed at various locations throughout the line, providing comprehensive line operating status information.

[0037] S300: Compare the data in the key dataset with the historical data and determine whether there are any abnormal nodes in the key dataset that exceed the deviation value using a preset deviation value. If an abnormal node exists, execute S400; if not, record the key dataset and execute S500. The preset deviation value is preset based on historical data. By comparing the data in the key dataset (each factor in the data item) with the historical data (the same factor in the data item), if the difference between the key dataset and the historical data is greater than the preset deviation value, it indicates that there is an abnormal node in the key dataset that exceeds the deviation value. Otherwise, there is no abnormal node.

[0038] Abnormal nodes are caused by unbalanced line loads, collaborative device failures, or improper collaborative device parameter settings. When an abnormality exists in a key data set, measures must be taken to eliminate it. S400 is performed to determine the abnormal area using the location code of the abnormal node. The collaborative device is connected to the interconnection point for operation, and data items of the sampling nodes in the abnormal area are again obtained to obtain abnormal line data. S200 to 300 are repeated for this abnormal line data until no abnormal nodes exist.

[0039] For example, since the location codes of abnormal nodes correspond to the physical locations of the lines, the location codes of abnormal nodes can be used to determine the specific location of the abnormal area. After the abnormal area is determined, appropriate collaborative equipment is connected through the interconnection point. The collaborative equipment is used to select and adjust according to the specific circumstances of the abnormality. Determining the abnormal area facilitates access to appropriate collaborative equipment, allowing for rapid response and adjustment to the abnormal situation, preventing the abnormality from escalating or causing more serious failures. When the collaborative equipment intervenes, data items from the sampling nodes in the abnormal area need to be recollected to form abnormal line data. The collection process of this abnormal line data is the same as that of the key data set mentioned above.

[0040] During operation, abnormal line data is classified according to S200 to obtain updated key and basic data sets. The abnormal line data is then compared again with historical data, and a preset deviation value is used to determine whether abnormal nodes exceeding the deviation value still exist. If abnormal nodes still exist, S400 is executed. Through multiple data collection and adjustments, the abnormal nodes are gradually eliminated, and the normal operation of the power system is restored, and S500 is executed. Through multiple data collection and comparisons, a large amount of abnormal line data can be accumulated, providing sufficient data support for future power system model training.

[0041] For reference, if the data item anomaly is voltage or power, the required collaborative device is a compensation device, which uses the collaborative device in the abnormal area to perform voltage regulation and power factor compensation. If the data item anomaly is load data, the required collaborative device is a load distribution device: load transfer (load balancing) is performed through the collaborative device in the abnormal area to relieve the pressure on the overloaded line.

[0042] S500: Filter the key data set by timestamp to obtain data items of similar time periods and obtain key time-sensitive data. Obtain the degree of change of each sampling node in the key time-sensitive data and determine whether the degree of change is within a safety threshold.

[0043] If the degree of change is within the safety threshold, it means that the risk is controllable.

[0044] If the degree of change exceeds the safety threshold range, it indicates a high risk, and the working state of the interconnection point on the overloaded line is adjusted. After the adjustment is completed, S100 to S500 are repeated until the degree of change is within the safety threshold range.

[0045] Among them, the data items in the key data set are filtered according to the timestamps, and the data items with similar time periods within the last 1 hour, 2 hours, 4 hours, 8 hours, 12 hours or 24 hours are selected. The filtered data are integrated to form key time-sensitive data. By analyzing the data of each sampling node in the key time-sensitive data, the degree of change of each sampling node is obtained, and the degree of change of each sampling node is compared with the preset safety threshold to determine whether it is within the safety threshold range.

[0046] The safety threshold can be set based on a safety value obtained from historical data statistics. If the change degree of all sampling nodes is within the safety threshold, it means that the system risk is controllable, the current key data set is recorded, and the processing flow ends.

[0047] If the change in any sampling node exceeds the safety threshold, it indicates a significant risk and requires further adjustment to the interconnection point's operating status on the overloaded line. The operating status action depends on the risk factor identified in the data item. These actions include, but are not limited to, transferring some load from the overloaded line to the base line to alleviate stress on the overloaded line. Alternatively, compensating devices can be used to adjust voltage and power factor to ensure that the line's electrical parameters are within acceptable ranges. Alternatively, parameters of coordinated equipment can be adjusted to more effectively address the current abnormal situation. The specific action depends on the risk type identified in the data item, such as voltage risk, current risk, power risk, or load risk.

[0048] Based on the above embodiment, another embodiment of the present invention is to mitigate the risk of misjudgment. During S400, when determining an abnormal region, data items in the abnormal region are first retrieved from the base dataset using position coding. Data items in the same time period are then filtered using timestamps to obtain an abnormal dataset. This abnormal dataset is then sent to a server for recording. Simultaneously, the server analyzes the abnormal dataset to determine whether the abnormal node in the abnormal region is an individual anomaly caused by a data transmission error or sampling error. If the abnormal node in the abnormal region is determined to be an individual anomaly, S400 is paused and S500 is executed directly.

[0049] For reference, the data items of the abnormal area are retrieved from the basic data set, with the aim of selecting sampling nodes close to the abnormal node. For anomalies to occur in the same abnormal area, most of the sampling nodes usually need to be abnormal. If the data of the sampling nodes close to the abnormal node in the abnormal area are normal, the anomaly may be caused by data transmission errors or sampling errors, and the abnormal node is an individual anomaly.

[0050] For reference, we filter data from sampling nodes that are close to the anomaly area during the same time period by timestamp. This is mainly to ensure that the data sampling time of all sampling nodes is synchronized. By analyzing and comparing node data in different time periods, if a node is anomaly in a specific time period, while other nodes are normal in the same time period, there may be an individual anomaly.

[0051] Based on the above embodiment, another embodiment of the present invention is that in S500, the step of obtaining the degree of change is as follows: S501, extracting all data items of a sampling node in the key timeliness data and obtaining a median P. A sampling node is selected from the key timeliness data, all data items of the sampling node within a predetermined time range are extracted, and the median P of these data items is calculated. The median P indicates the typical state of the sampling node within a specified time period. The median P is used as a benchmark value for comparison of subsequent data items and calculation of the degree of change.

[0052] The number of bits P is obtained as follows: the same factors are extracted from all data items of a sampling node, and the values of the factors are arranged in ascending order to obtain the factor set {X1, X2, ...Xn}, where Xn is the value of the nth factor after sorting.

[0053] If n is an odd number, the median P is the value of the (n+1) / 2th factor.

[0054] If n is an even number, the median P is the average of the two numbers in the middle after sorting. Its calculation formula is:

[0055]

[0056] Where, is the value of the n / 2th factor; is the value of the (n+1) / 2th factor.

[0057] S502: Arrange each data item of the sampling node in the time sequence of the timestamp. By arranging the data items in time sequence, the changing trend of the data over time can be observed more clearly, which helps to identify and analyze the change pattern and fluctuation.

[0058] S503 compares the sorted data items with the median P in sequence, plotting the comparison results in a curve. The sorted data items are compared with the median P one by one, calculating the difference between each data item and the median P. These difference values are then plotted in a curve, where the horizontal axis represents time and the vertical axis represents the difference between the data item and the median P. The curve can intuitively display the fluctuation of the data item at the sampling node relative to the median P. The fluctuation amplitude and shape of the curve can be used to intuitively judge data changes.

[0059] S504: Obtain the difference between the peak and valley values of the graph. This difference represents the degree of change of the sampling node. By identifying the highest point (peak) and lowest point (valley) in the graph and calculating the difference between them, the difference between the peak and valley values represents the maximum fluctuation amplitude of the sampling node within the specified time period. This difference can be used to quantify the change of the sampling node and determine whether it exceeds a preset safety threshold.

[0060] Based on the above embodiment, another embodiment of the present invention is that when the degree of change of the above sampling nodes is within a safety threshold range, a second sampling plan is configured, and the second sampling plan uses all sampling nodes to obtain a set of data items in a time-sharing manner to obtain second line data; wherein the second sampling plan is an execution plan for time-sharing sampling performed on all sampling nodes within a set time interval.

[0061] The second sampling plan time interval is set based on system requirements and operating status. The time interval and frequency of time-sharing sampling are typically calculated in minutes. For example, all sampling nodes are sampled every 5 minutes. Time-sharing sampling can reduce sampling energy consumption and provide more comprehensive monitoring of line operating status information.

[0062] Among them, the second sampling plan obtains the data item set of all sampling nodes in a time-sharing manner. All sampling nodes work according to the time interval and frequency of the second sampling plan, and obtains the operation data at different time points through time-sharing sampling and integrates them into the second line data.

[0063] The location codes are integrated according to the sampling node association requirements to create a location code set. Association requirements are line associations based on the sampling node's physical location, function, and load distribution. Multiple different location code sets are generated by classifying and integrating the location codes. It should be noted that when selecting association requirements, they are generally directly invoked through the server. These association requirements are usually preset.

[0064] One possible configuration approach is to use a support vector machine (SVM) model on the server. This model can be trained using historical data, and then the trained model can be used to predict associated line data, thereby setting the associated requirements for sampling nodes. This model training method is for technical personnel's reference only and does not limit the solution of this embodiment.

[0065] The second line data is filtered through the position code set to obtain associated line data; wherein the associated line data includes data items sampled multiple times by the sampling nodes corresponding to the position code set; the associated line data is output to the server, and the server verifies whether the sampling nodes corresponding to the position code set are associated through the associated line data.

[0066] Exemplarily, the second link data is filtered through the position code set to identify multiple sampling data items corresponding to the sampling node at different time points, forming associated link data. This associated link data includes data items for sampling nodes with specific locations and functions. Analyzing these different associated link data facilitates accurate analysis and verification. Therefore, the associated link data needs to be output to a server for verification. The server receives the associated link data and confirms whether the sampling nodes corresponding to the position code set are associated, thereby ensuring accurate data association between the sampling nodes.

[0067] Furthermore, to verify the association of sampling nodes in the position code set, the data item for each sampling node in the associated line data is extracted. Two adjacent data items for the same sampling node are compared to obtain the fluctuation amplitude of the sampling node. The fluctuation amplitude is used to determine the magnitude of the data change between two adjacent samplings of a sampling node. The fluctuation of the sampling node can be quantified by calculating the difference between the two adjacent data items. The fluctuation amplitude of each sampling node in the position code set is obtained, and the server verifies the degree of association of the sampling nodes based on the fluctuation amplitudes of adjacent sampling nodes.

[0068] The calculation formula for the fluctuation range is: △x i (t) = x i (t+1)-x i (t); where △x i (t) is the fluctuation amplitude of the data item of sampling node i between time t and t+1, x i (t+1) is the data item of sampling node i at time t+1; x i (t) is the data item of sampling node i at time t.

[0069] The degree of correlation refers to the similarity in fluctuation amplitude between different sampling nodes. When the fluctuation amplitudes of two nodes within adjacent time periods are highly correlated, they are considered highly correlated. This correlation line data is verified by a server-based model. The server extracts the data item for each sampling node in the correlation line data, calculates the fluctuation amplitude of each sampling node between two adjacent samplings, forms a fluctuation amplitude sequence, and calculates the correlation coefficient of the fluctuation amplitudes between adjacent sampling nodes.

[0070] For ease of understanding, the following explanation is given using parameters:

[0071] For example, extract the data items of each sampling node in the associated line data, and the data items of a sampling node i are n. Its data sequence is:

[0072] {x i (t1), x i (t2), x i (t3),……,x i (t n )};

[0073] Among them, the fluctuation amplitude sequence △x corresponding to the data sequence i for:

[0074] △x i ={x i (t2)-x i (t1),x i (t3)-x i (t2),......,x i (t n )-x i (t n-1 )};

[0075] Among them, the calculation of the fluctuation amplitude sequence △x j Average value The calculation formula is:

[0076]

[0077] Where n is the total number of data sequences, x i is the data item of sampling node i.

[0078] The Pearson correlation coefficient between two sampling nodes is calculated by using the correlation data of the fluctuation amplitude between adjacent sampling nodes. The calculation formula of the Pearson correlation coefficient is:

[0079]

[0080] Where R ijis the Pearson correlation coefficient between sampling nodes i and j; △x i is the fluctuation amplitude sequence of sampling node i, △x j is the fluctuation amplitude sequence of sampling node j, is the fluctuation amplitude sequence △x i The average value of is the fluctuation amplitude sequence △x j The average value of .

[0081] Specifically, R ij The result is between -1 and 1. If R ij = 0, it means that there is no association between sampling node i and sampling node j; otherwise, if R ij ≠0, it means that there is a relationship between sampling node i and sampling node j.

[0082] It is worth noting that R ij The closer the result is to 1, the greater the positive linear correlation between sampling node i and sampling node j is. ij The closer the result is to -1, the greater the negative linear correlation between sampling node i and sampling node j.

[0083] Based on the above embodiment, another embodiment of the present invention is that, due to the large number of nodes, node errors or invalid data may occur. To better ensure the stability of data collection, at least two or more collection devices are installed at the above-mentioned key nodes, and all collection devices use the same position code. Each collection device is also assigned a unique identification code. By installing multiple collection devices at key nodes, it is possible to prevent one collection device from malfunctioning or experiencing an error while the other collection devices can continue to operate normally, ensuring data continuity and reducing the risk of single-point failure. Furthermore, using the same position code allows multiple collection devices to verify data with each other. By comparing the data items collected by the collection devices, it is helpful to identify outliers or invalid data from the collection devices, which is objectively beneficial for calibration and correction. Furthermore, if the data collected by collection devices with the same position code is consistent, the credibility of the data can be enhanced. Secondly, the position code and unique identification code are used to identify and associate different collection devices at the same location. On the one hand, the position code can integrate the data of multiple collection devices at the same key node. On the other hand, the use of the identification code can partition the integrated data, avoiding data confusion caused by position code errors.

[0084] Several identification areas are divided on the above-mentioned heavy-load line, and each identification area covers at least two or more key nodes. The design of the identification area points is mainly used to divide the heavy-load line into multiple areas. Each area covers multiple key nodes, which facilitates regional management and gradual improvement. A relay processor is set in the above-mentioned identification area. The above-mentioned relay processor is connected to the acquisition device signal in the identification area. The relay processor pre-processes the data items and synchronously transmits the processing results to the server. Among them, a relay processor is set in each identification area. The relay processor pre-processes the data transmitted by the acquisition device in the identification area, eliminates obvious erroneous data through pre-processing, and reduces the processing burden of the server. The relay processor synchronously transmits the processed data to the server. If necessary, the relay processor can integrate and compress the data of multiple devices and send them to the server in a packaged form, thereby reducing the bandwidth requirement for data transmission.

[0085] For reference, the above-mentioned relay processor can be an existing regional server, in which the relay processor can use routing to forward the transmitted data. At the same time, the relay processor can be pre-set with a program. After receiving a signal of failure or abnormality of the collection device, the relay processor can send an alarm or automatically switch to other collection devices.

[0086] Based on the above embodiment, another embodiment of the present invention is that when the above relay processor performs preprocessing, the above relay processor sets an error threshold, obtains data items with the same position code, obtains a weighted average value and a degree of deviation of the same data items, compares the degree of deviation with the error threshold, identifies valid data items, and sends the valid data items to the server. By calculating the weighted average value, the data of multiple acquisition devices can be comprehensively considered. The data collected by multiple acquisition devices may contain noise or errors. By calculating the weighted average value, the data can be smoothed and the impact of noise can be reduced, thereby reducing the overall impact of individual data anomalies on the overall data. By calculating the degree of deviation, the degree of deviation is used to identify and eliminate obviously abnormal data, thereby improving the overall data quality.

[0087] The calculation formula for the weighted average is:

[0088] Where D i k is the data item parameter collected by the i-th collection device at the same time point; i is the historical performance weight of the i-th collection device, n is the total number of collection devices in the identification area; D is the weighted average value of the identification area.

[0089] The calculation formula for the degree of deviation is: ΔD=|D i -D|; where ΔD is the degree of deviation, D iis the data item parameter collected by the i-th collection device at the same time point, and D is D i The weighted average value of the corresponding identification area. The degree of deviation ΔD is compared with the error threshold; if ΔD ≤ the error threshold, the acquisition device is considered to be collecting data normally and the data item is valid. If ΔD > the error threshold, the acquisition device is considered to be collecting data in doubt and the data item is questionable. By comparing the degree of deviation with the error threshold, the credibility of the data item can be effectively identified. If the questionable data is invalid, it is removed before the data is transmitted to the server, thereby reducing the server's processing burden, ensuring the quality of the data transmitted to the server, and preventing invalid data from affecting the normal analysis of the server.

[0090] Furthermore, in order to ensure that the data output by the relay processor has good reliability, the above-mentioned relay processor obtains the identification code and position code of the questionable data, and cross-determines the key node corresponding to the questionable data through the position code and identification code; selects the valid data corresponding to the questionable data from the data items in the key node through the timestamp; and the relay processor uploads the valid data corresponding to the questionable data to the server.

[0091] During operation, the relay processor pre-processes the data items, compares the degree of deviation with the error threshold, identifies the questionable data items and marks them as questionable data, and records the identification code and position code of the questionable data; the relay processor uses the position code to determine the key node corresponding to the questionable data, and determines the specific collection device in combination with the identification code. The relay processor extracts the data items of valid data of other seats in the same time period from the key node according to the timestamp of the questionable data, determines the correspondence between the data items and the questionable data, and after confirming the correspondence, the relay processor uploads the valid data corresponding to the questionable data to the server.

[0092] Furthermore, the relay processor marks the suspect data and extracts all data items from the suspect collection device using the identification code to generate analysis data. This analysis data is then sent to the server for characteristic analysis. During data preprocessing, the relay processor marks data items with deviations exceeding an error threshold as suspect data and records their identification codes. The relay processor then extracts all data items from the suspect collection device based on the identification codes to generate analysis data. The relay processor then sends this analysis data to the server, which performs characteristic analysis on the data to identify the cause and characteristics of the device anomaly.

[0093] For reference, the server receives questionable data uploaded by the relay processor and stores it in a database. When feature analysis is required, it indexes the data using its identification code, location code, and timestamp. During analysis, the server retrieves the questionable data, removes any duplicate, missing, or obviously erroneous data, and then performs feature analysis.

[0094] For ease of understanding, let's use voltage data as an example. A server receives abnormal voltage data from a key node. The server first categorizes the abnormal voltage data according to identification codes. After confirming that it originates from the same data acquisition device, the server calculates and analyzes the characteristics of the suspect data to comprehensively understand the data's variation patterns and potential issues. For example, the server can obtain the average value from the suspect data (voltage). This average value reflects the overall data level and determines whether the data deviates from the normal range, facilitating the detection of persistent deviations. For example, the server can obtain the variance of the suspect data to determine the degree of fluctuation in the voltage data, thereby reflecting the degree of instability of the sampling device. For example, the server can obtain the maximum and minimum values of the suspect voltage data to understand extreme variations in the data, helping to identify short-term, sudden issues such as transient overvoltage or undervoltage. For example, time series analysis can generate a temporal trend in the voltage data, facilitating the identification of potential issues such as device aging or load changes. By extracting all data items from the suspect data acquisition device and performing characteristic analysis, a deeper understanding of the device's operating status and the causes of the data anomaly can be obtained.

[0095] References in this specification to "one embodiment," "another embodiment," "an embodiment," "preferred embodiment," etc., refer to specific features, structures, or characteristics described in conjunction with that embodiment as included in at least one embodiment generally described in this application. The appearance of the same term in multiple places in the specification does not necessarily refer to the same embodiment. Furthermore, when a specific feature, structure, or characteristic is described in conjunction with any embodiment, it is intended that such feature, structure, or characteristic, when implemented in conjunction with other embodiments, also fall within the scope of the present invention.

[0096] Although the present invention has been described herein with reference to a number of illustrative embodiments thereof, it will be understood that numerous other modifications and implementations may be devised by those skilled in the art that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, the drawings, and the claims, numerous variations and modifications may be made to the components and / or layout of the subject combination arrangement. In addition to variations and modifications to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A control method based on line sampling and metering, wherein the line includes a heavy-load line and a basic line, and multiple interconnection points are set between the heavy-load line and the basic line, and the interconnection points are externally connected to collaborative equipment, characterized in that: The method comprises the following steps: S100, allocating a number of sampling nodes on a line and configuring a first sampling plan, obtaining data items of all sampling nodes in the first sampling plan, and aggregating the data items into first line data; The data items are timestamp and position code added, and the first sampling plan is an execution plan for real-time sampling performed by all sampling nodes within a set time interval; S200, obtaining position codes of sampling nodes corresponding to interconnected points and marking them as key nodes, and marking the position codes of remaining sampling nodes as basic nodes; classifying the first link data according to the position codes to obtain key data sets and basic data sets corresponding to the key nodes and basic nodes; S300, comparing the data of the key data set with the historical data, and determining whether there is an abnormal node in the key data set that exceeds the deviation value by using a preset deviation value; if there is an abnormal node, executing S400; if there is no abnormal node, recording the key data set and executing S500; S400: Determine the abnormal area based on the position code of the abnormal node, connect to the collaborative device through the interconnection point to work and dynamically adjust the load distribution of the overloaded line and the basic line; obtain the data items of the sampling nodes in the abnormal area again to obtain abnormal line data, and repeat S200 to S300 with the abnormal line data as the new first line data until there are no more abnormal nodes; S500: Filter the key data set by timestamp to obtain the key time-sensitive data. Obtain the degree of change of each sampling node in the key time-sensitive data. Select the data items in the key data set for the same time period according to the timestamp and extract the median of the sampling node data. Arrange the data items in time sequence and compare them with the median in sequence to form a curve graph. Calculate the difference between the peak and the valley to obtain the degree of change. Determine whether the degree of change is within the safety threshold. If the degree of change is within the safety threshold, it indicates that the risk is controllable. A second sampling plan is then configured to integrate the location codes according to the association requirements of the sampling nodes to obtain a location code set. The fluctuation amplitude of each sampling node in the location code set is obtained, and the server verifies the degree of association of the sampling nodes. If the degree of change exceeds the safety threshold range, it indicates a high risk. The working state of the interconnection point on the overloaded line is adjusted through the preset strategy of the collaborative device. After the adjustment is completed, S100 to S500 are repeated until the degree of change is within the safety threshold range.

2. The control method based on line sampling and metering according to claim 1, characterized in that: In S400, when determining the abnormal area, a sampling node close to the abnormal node is selected, and the data items of the abnormal area are retrieved from the basic data set through the position coding. The data items of the same time period are filtered through the timestamp to obtain the abnormal data set, and the abnormal data set is sent to the server for recording. If the data of the sampling nodes close to the abnormal node in the abnormal area are all normal, the abnormal node is regarded as an individual abnormality.

3. The control method based on line sampling and metering according to claim 1, characterized in that: When the degree of change of the sampling nodes is within the safety threshold, a second sampling plan is configured, and the second sampling plan obtains a set of data items from all sampling nodes in a time-sharing manner to obtain second line data; wherein the second sampling plan is an execution plan for time-sharing sampling of all sampling nodes within a set time interval; The second line data is filtered through the position code set to obtain associated line data; wherein the associated line data includes data items sampled multiple times by the sampling nodes corresponding to the position code set; the associated line data is used to be output to the server, and the server verifies whether the sampling nodes corresponding to the position code set are associated through the associated line data.

4. The control method based on line sampling and metering according to claim 3 is characterized in that: When verifying whether the sampling nodes in the position code set are associated, extract the data items of each sampling node in the associated line data, compare the two adjacent data of the same sampling node, obtain the fluctuation amplitude of the sampling node, obtain the fluctuation amplitude of each sampling node in the position code set, and verify the degree of association of the sampling nodes by the fluctuation amplitude of adjacent sampling nodes.

5. The control method based on line sampling and metering according to claim 1, characterized in that: The key node is provided with at least two or more acquisition devices, and the acquisition devices all use the same position code, and each acquisition device is provided with a unique identification code; The overloaded line is divided into several identification areas, and each identification area covers at least two key nodes. A relay processor is set in the identification area. The relay processor is connected to the signal of the acquisition device in the identification area. The relay processor pre-processes the data items and synchronously transmits the processing results to the server.

6. The control method based on line sampling and metering according to claim 5, characterized in that: When the relay processor performs preprocessing, the relay processor sets an error threshold, obtains data items with the same position code, obtains a weighted average value and a degree of deviation of the same data items, compares the degree of deviation with the error threshold, identifies valid data items, and sends the valid data items to the server; The calculation formula for the weighted average is: ； Where, is the data item parameter collected by the i-th collection device at the same time point; is the historical performance weight of the i-th collection device, n is the total number of collection devices in the identification area; D is the weighted average value of the identification area; The calculation formula for the degree of deviation is: ； Where ΔD is the degree of deviation, is the data item parameter collected by the i-th collection device at the same time point, and D is The weighted average value of the corresponding identification area; Compare the degree of deviation ΔD with the error threshold; If ΔD≤error threshold, it is considered that the acquisition device is collecting normally and its data item is valid data. If ΔD>error threshold, it is considered that the data collected by the collection device is questionable and its data item is questionable data.

7. The control method based on line sampling and metering according to claim 6, characterized in that: The relay processor obtains the identification code and position code of the questionable data, and determines the key node corresponding to the questionable data by cross-checking the position code and the identification code; selects the valid data corresponding to the questionable data from the data items in the key node by the timestamp; and the relay processor uploads the valid data corresponding to the questionable data to the server.

8. The control method based on line sampling and metering according to claim 6, characterized in that: The relay processor marks the questionable data, and extracts all data items of the questionable collection device through the identification code to obtain analysis data, which is sent to the server for characteristic analysis.

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