Control method based on line sampling metering

Abstract

The invention discloses a control method based on line sampling metering, which belongs to the field of metering sampling control, and comprises the following steps: distributing a plurality of sampling nodes on a line, configuring a first sampling plan, and gathering data items into first line data; classifying the first line data through 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 judging whether an abnormal node exceeding a deviation value exists in the key data set or not through a preset deviation value; determining an abnormal area through the position code of the abnormal node, and accessing the cooperative equipment to work; the key data set screens key aging data through timestamps and obtains the change degree of each sampling node; the method is used for judging whether the change degree is within a safety threshold range. If the change degree is within the safety threshold range, the risk is controllable; and if the change degree exceeds a safety threshold range, the risk is relatively high, so that the problems of fuzzy positioning of a line abnormal region and limited potential risk early warning are expected to be improved.

Description

Technical Field

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

[0002] In industrial areas, power lines are very important infrastructure. The stability and safety of the lines directly affect the production and operation of the entire industrial area. The current line monitoring method usually relies on segmented monitoring. When a line has a problem, it needs to be checked. For some industrial areas, due to different electricity demands, the overall line architecture is relatively complex. When some of the lines have problems, there is a lack of real-time data processing and analysis capabilities, making it difficult to quickly analyze and judge a 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 mutual interference between lines, the fault location range is larger, and a larger location range is likely to prolong the fault handling time.

[0004] More importantly, during the operation of the line, even if there are no abnormalities, risk factors need to be checked. The inspection is mostly done manually, relying mainly on manual experience to judge risks. There is a lack of scientific risk assessment mechanisms, and effective early warning of potential risks is relatively limited. When the risk exceeds expectations, data collection and analysis are prone to lags. When the potential risk factors increase, the number of rescue operations increases. Therefore, it is worth studying how to optimize the situation where abnormal problems are difficult to find, risks are not discovered in time, and the solution cycle is 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 metering, the line includes a heavy-load line and a basic line, a plurality of interconnection points are set between the heavy-load line and the basic line, and 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 item, the timestamp and the position code respectively correspond to the acquisition time and the node code of the data item, and the first sampling plan is an execution plan for real-time sampling performed by all sampling nodes within a set time interval.

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

[0009] S300, compare the data of the key data set with the historical data, and determine whether there is an abnormal node in the key data set that exceeds the deviation value by using the preset deviation value. If there is an abnormal node, execute S400; if there is no abnormal node, record the key data set and execute S500.

[0010] S400, determine the abnormal area through the position coding 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] S500, filter the key data set by the timestamp for data items of similar time periods and obtain the key time-effectiveness data, obtain the degree of change of each sampling node in the key time-effectiveness data, and determine whether the degree of change is within the safety threshold range. If the degree of change is within the safety threshold range, it means that the risk is controllable; if the degree of change exceeds the safety threshold range, it means that the risk is relatively large, and adjust the working state of the interconnection point on the overloaded line through the preset strategy of the collaborative device. After the adjustment is completed, repeat S100 to S500 until the degree of change is within the safety threshold range.

[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 the position code, and the data items of the same time period are filtered through the timestamp 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 variation degree of the above-mentioned sampling nodes is within the safety threshold range, 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 performed by all sampling nodes within a set time interval. The position codes are integrated according to the association requirements of the sampling nodes to obtain a position code set; the above-mentioned second line data is screened 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 above-mentioned 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.

[0015] A further technical solution is that, when checking whether the sampling nodes in the position coding set are associated, the data items of each sampling node in the associated line data are extracted, the two adjacent data of the same sampling node are compared to obtain the fluctuation amplitude of the sampling node, the fluctuation amplitude of each sampling node in the position coding set is obtained, and the server verifies the degree of association of the sampling nodes through the fluctuation amplitude of adjacent sampling nodes.

[0016] Preferably, at least two collection devices are arranged at the key nodes, and the collection devices all adopt the same position coding, and each collection device is provided with a unique identification code; a plurality of identification areas are divided on the heavy-load line, and each identification area covers at least two key nodes, and a relay processor is arranged in the identification area, and the relay processor is connected with 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 of the corresponding identification area.

[0020] 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 acquisition device is collecting doubtful 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 cross-determines the key node corresponding to the questionable data through the position code and the 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, and the above analysis data is used to send to the server for characteristic analysis.

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

[0024] The present invention ensures comprehensive coverage of the line by setting sampling nodes, uses the first sampling plan to comprehensively obtain the data items of the line, and through the positional relationship between the nodes, by marking the key nodes and the basic nodes, by focusing on the interconnection points, and obtaining the key data set and the basic data set, through the key data set by comparing and analyzing with the historical data, so as to determine whether there are abnormal nodes near the interconnection points, if there are abnormal nodes, the abnormal area is determined by position coding, and by introducing the intervention of collaborative equipment, the data items near the key nodes (interconnection points) can be corrected, which is conducive to restoring the normal operation state of the line on the one hand. On the other hand, it is conducive to collecting the data of the abnormal nodes, so as to obtain the abnormal data faster, and then identify potential problems (load imbalance or equipment failure).

[0025] When the key data set detection of the present invention does not have abnormal nodes, it can also detect possible risk items in the key data set, determine the degree of change of the sampling node through the timestamp, and achieve the degree of change monitoring by calculating the degree of change and comparing it with the safety threshold. In the process of monitoring the degree of change, it is expected to provide real-time feedback on the degree of change of the data. When a large risk is identified, adaptive adjustments can be made; when the risk is controllable, the sampling plan is adjusted, so as to coordinate the equipment for dynamic monitoring. 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 solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention 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 used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. 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 the field to implement. When the combination of technical solutions is 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 metering, wherein the line includes a heavy-load line and a basic line, wherein a plurality of interconnection points are set between the heavy-load line and the basic line, and the interconnection points are externally connected to collaborative devices. The control method comprises the following steps: S100, allocating a number of sampling nodes on the above-mentioned line and configuring a first sampling plan, obtaining data items of all sampling nodes in the first sampling plan, and collecting the data items of all sampling nodes into first line data. Among them, a timestamp and a position code are added to the data item, and the timestamp and the position code correspond to the acquisition time and the node code of the data item respectively; the first sampling plan is an execution plan for real-time sampling performed by all sampling nodes within a set time interval.

[0031] The data items may include factors such as voltage, current, power, and load data. The collaborative equipment is an existing auxiliary power equipment. The specific equipment type is adaptively set according to the nature of different line usage. After the collaborative equipment is involved, it is mainly suitable for improving power factor, stabilizing voltage, filtering harmonics, and balancing loads. Different types of collaborative equipment allow for joint use through different interconnection points.

[0032] For reference, the collaborative device can be a compensation device. When there is a deviation in the electrical parameters (such as voltage, current or phase, etc.) in the line, its 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 the collaborative device externally, it is convenient to adjust the work done by the compensation device in different areas in the later stage to ensure the stable operation of the power system.

[0033] For reference, the collaborative device can be a load distribution device. Through the collaborative device, the load is transferred using the interconnection point. For example, when the voltage of the heavy-load line is too high or too low, the collaborative device can transfer part of the load to the basic line to reduce the pressure on the heavy-load line. Through the setting of the collaborative device, the line can dynamically adjust the load distribution of the heavy-load line and the basic line.

[0034] For reference, the first sampling plan is to set the time interval mainly in hours, and the time interval is set to N hours, where 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 longer real-time sampling time in the initial stage, the larger the value of N is set, and vice versa, if the line requires shorter real-time sampling time 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] Among them, the first sampling plan is mainly used in the initial stage of line sampling. The first sampling plan mainly performs real-time sampling. All sampling nodes are used to perform real-time sampling within a preset set time interval, and the data collected by all sampling nodes are summarized into the first line data. Since the first sampling plan adopts real-time sampling, the operation data of the entire line is relatively large. By summarizing it into the first line data, it is mainly based on data overall consideration. On the one hand, it can provide basic data for abnormal judgment. On the other hand, the first line data is stored in the form of data packets, which is conducive to the reading of periodic data to avoid the influence of data crosstalk that may occur when scattered data is stored, and reduce unnecessary data processing losses. The above timestamp corresponds to the acquisition time of the data item, and the position code corresponds to the node code of the data item. The data of the first line data are all added with timestamps and position codes. On the one hand, the introduction of timestamps makes each data item have a time series mark. The real-time nature of data sampling can be verified through timestamps, and the specific time of the data source can be accurately obtained, so as to facilitate the analysis of periodic data. On the other hand, through the setting of position coding, the accurate attribution of each data item is mainly ensured, which is convenient for subsequent data analysis and fault location according to the node position.

[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 coordinate rapid response and adjustment. 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 operation 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 operation status information.

[0037] S300, compare the data of the key data set with the historical data, and determine whether there are abnormal nodes in the key data set that exceed the deviation value by using the preset deviation value. If there are abnormal nodes, execute S400; if there are no abnormal nodes, record the key data set and execute S500; wherein, the preset deviation value is preset based on the historical data, and the data of the key data set (each factor in the data item) is compared with the historical data (the same factor in the data item). If the difference between the key data set and the historical data is greater than the preset deviation value, it means that there are abnormal nodes in the key data set that exceed the deviation value. Otherwise, there are no abnormal nodes.

[0038] The appearance of abnormal nodes is caused by unbalanced line load, failure of collaborative equipment, or unreasonable setting parameters of collaborative equipment. When there is an abnormality in the key data set, measures need to be taken to eliminate the abnormality, and then S400 is performed to determine the abnormal area through the position coding of the abnormal node, access the collaborative equipment through the interconnection point to work, and obtain the data items of the sampling nodes in the abnormal area again to obtain abnormal line data. The above abnormal line data repeats S200 to 300 until there is no abnormal node.

[0039] Exemplarily, since the position code of the abnormal node corresponds to the physical position of the line, the specific location of the abnormal area can be determined through the position code of the abnormal node. After the abnormal area is determined, the appropriate collaborative device is accessed through the interconnection point. The collaborative device is used to select and adjust according to the specific situation of the abnormality. By determining the abnormal area, it is convenient to access the appropriate collaborative device, and the abnormal situation can be responded and adjusted quickly to avoid the expansion of the abnormality or causing more serious faults. When the collaborative device intervenes, the data items of the sampling nodes in the abnormal area need to be re-collected to form abnormal line data. The collection process of the above abnormal line data is the same as the aforementioned key data set.

[0040] During operation, the abnormal line data is classified according to S200 to obtain updated key data sets and basic data sets, and the abnormal line data is compared with the historical data again, and the preset deviation value is used to determine whether there are still abnormal nodes that exceed the deviation value. If there are still abnormal nodes, continue to execute S400, and through multiple collections and adjustments, gradually eliminate the abnormal nodes, restore the normal operation of the power system, and execute S500. Due to multiple data collection and comparison, a large amount of abnormal line data can be accumulated, and the data can provide sufficient data support for future power system model training.

[0041] For reference, if the data item abnormality is voltage or power, the required collaborative device is a compensation device, and voltage regulation and power factor compensation are performed through the collaborative device in the abnormal area. If the data item abnormality 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, filtering the key data set by the timestamp for data items of similar time periods and obtaining the key time-effectiveness data, obtaining the degree of change of each sampling node in the key time-effectiveness data, and determining whether the degree of change is within the safety threshold range.

[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 that the risk is relatively high, 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 as 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 timeliness data. By analyzing the data of each sampling node in the key timeliness 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 setting of the safety threshold can be based on the safety value obtained from the statistics of historical data. 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 degree of change of any sampling node exceeds the safety threshold range, it means that there is a greater risk and further adjustment is needed to the working state of the interconnection point on the overloaded line. The operation of the working state depends on the factors that need to exist in the data item. Operations include but are not limited to: transferring part of the load from the overloaded line to the basic line to reduce the pressure on the overloaded line. Or adjusting the voltage and power factor through the compensation device to ensure that the electrical parameters of the line are within a reasonable range. Or adjusting the parameters of the collaborative equipment to more effectively respond to the current abnormal situation. The specific operation depends on the risk type 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 that in order to avoid the risk of misjudgment, in the process of S400, when determining the abnormal area, firstly, the data items of the abnormal area are retrieved from the basic data set by position coding, and the data items of the same period are filtered by timestamp to obtain the abnormal data set, and the above abnormal data set is used to send to the server for recording. At the same time, the server analyzes the abnormal data set to determine whether the abnormal node in the abnormal area is an individual abnormality caused by data transmission error or sampling error. When it is determined that the abnormal node in the abnormal area is an individual abnormality, S400 is paused and S500 is directly executed.

[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 position. 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 nodes 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, the data of sampling nodes close to the abnormal area in the same period are filtered by timestamp, mainly to ensure that the data sampling time of all sampling nodes is synchronized. By analyzing the node data in different time periods for comparison, if a node is abnormal in a specific time period, while other nodes are normal in the same time period, there may also be individual anomalies.

[0051] Based on the above embodiment, another embodiment of the present invention is that in S500, the step of obtaining the above degree of change is: S501, extracting all data items of a sampling node in the key timeliness data and obtaining the 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, wherein the median P indicates the typical state of the sampling node within a specified period of time, and the median P is used as a reference value for comparison of subsequent data items and calculation of the degree of change.

[0052] The number of digits 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 a factor set of {X1, X2, ...Xn}, where Xn is the value of the nth factor after sorting.

[0053] Wherein, 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] In the formula, is the value of the n / 2th factor; is the value of the (n+1) / 2th factor.

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

[0058] S503, the arranged data items are compared with the median P in turn, the comparison results are plotted into a curve graph, the arranged data items are compared with the median P one by one, the difference between each data item and the median P is calculated, and these difference values ​​are plotted into a curve graph, where the horizontal axis is time and the vertical axis is the difference between the data item and the median P. On the one hand, the curve graph can intuitively show the fluctuation of the data item of the sampling node relative to the median P, and the change of the data can be intuitively judged by the fluctuation amplitude and shape of the curve.

[0059] S504, obtaining the difference between the peak and the valley of the curve graph, the difference representing the degree of change of the sampling node. By identifying the highest point (peak) and the lowest point (valley) in the curve graph, the difference between them is calculated, that is, the difference between the peak and the valley represents the maximum fluctuation range of the sampling node in the specified time period. Through this difference, the change of the sampling node can be quantified and it can be determined whether it exceeds the 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 the safety threshold range, 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 performed by all sampling nodes within a set time interval.

[0061] The second sampling plan time interval is set according to system requirements and operating status. The time interval and frequency of time-sharing sampling are usually calculated in minutes. For example, all sampling nodes are sampled once every 5 minutes. Time-sharing sampling can reduce the energy consumption required for sampling and meet the monitoring of more comprehensive 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, and all sampling nodes work according to the time interval and frequency of the second sampling plan. The operation data at different time points are obtained through time-sharing sampling and integrated into the second line data.

[0063] The location codes are integrated according to the association requirements of the sampling nodes to obtain a location code set; wherein the association requirements are line associations formed according to the physical location, function, and load distribution of the sampling nodes, and multiple different location code sets are generated by classifying and integrating the location codes. It should be noted that when selecting the association requirements, it is generally called directly through the server. The association requirements are usually preset.

[0064] A setting method for reference is that a support vector machine (SVM) model can be used through the server, and the model can be trained through historical data, so that the trained model can be used to predict the associated line data, thereby setting the associated requirements of the sampling node. The model training method is only for reference by technicians and does not limit the solution of this embodiment.

[0065] The second line data is filtered through the position coding 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 coding set; the associated line data is output to the server, and the server verifies whether the sampling nodes corresponding to the position coding set are associated through the associated line data.

[0066] Exemplarily, the second line data is screened through the position code set to screen out multiple sampling data items of the corresponding sampling node at different time points to form associated line data, and the above-mentioned associated line data contains data items of sampling nodes of specific positions and functions. By analyzing different associated line data, it is helpful to perform accurate analysis and verification. Therefore, it is necessary to output the associated line data to the server for verification. The server receives the associated line data and confirms whether there is a correlation between the sampling nodes corresponding to the position code set, thereby ensuring that the data correlation of the sampling nodes is accurate.

[0067] Furthermore, when checking whether the sampling nodes in the position coding set are associated, the data items of each sampling node in the associated line data are extracted, and the two adjacent data of the same sampling node are compared to obtain the fluctuation amplitude of the sampling node. The data change amplitude of a sampling node between two adjacent samplings is determined by the fluctuation amplitude. By calculating the difference between the data items between two adjacent samplings, the fluctuation of the sampling node can be quantified. The fluctuation amplitude of each sampling node in the position coding set is obtained, and the server verifies the degree of association of the sampling nodes through the fluctuation amplitude of the 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 in adjacent time periods are highly correlated, it is considered that the two nodes have a high degree of correlation. The above-mentioned correlation line data is verified by calling the model through the server. The server extracts the data items of 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 amplitude between adjacent sampling nodes.

[0070] For ease of understanding, the following explanation is given through 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 The average 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] Through the correlation data of the fluctuation amplitude between adjacent sampling nodes, the Pearson correlation coefficient of two sampling nodes is calculated. The calculation formula of the Pearson correlation coefficient is:

[0079]

[0080] In the formula, 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. ij = 0, it means that there is no association between sampling node i and sampling node j; on the contrary, 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, and R 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 set, there may be node errors or invalid data, in order to better ensure the stability of the collection. The above key nodes are provided with at least two or more collection devices, and the collection devices all use the same position code, and each collection device is provided with a unique identification code. By setting multiple collection devices at the key nodes, it is used to avoid that when one of the collection devices fails or has an error, the other collection devices can still work normally, ensure the continuity of the data, and reduce the risk of single point failure. At the same time, the same position code is used, mainly for multiple collection devices to verify the data with each other, and by comparing the data items collected by the collection devices, it is conducive to identifying the abnormal values ​​or invalid data of the collection devices, which is objectively beneficial for calibration and correction. At the same time, if the data collected by the collection devices with the same position code are consistent, the credibility of the data can also be enhanced. Secondly, different collection devices at the same position are identified and associated by the position code and the unique identification code. On the one hand, the position code can integrate the data of multiple collection devices at the same key node, and on the other hand, the use of the identification code can divide the integrated data to avoid data confusion caused by the disorder of the position code.

[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 is convenient for regional management and gradual improvement. A relay processor is set in the above-mentioned identification area. The above-mentioned relay processor is connected with 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, and eliminates obvious erroneous data by pre-processing to reduce 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 preset 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 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. By calculating the weighted average value, the data of multiple acquisition devices can be comprehensively considered. There may be noise or errors in the data collected by multiple acquisition devices. By calculating the weighted average value, the data can be smoothed and the impact of noise can be reduced, thereby reducing the impact of individual data anomalies on the overall data as a whole. 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. 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. By comparing the degree of deviation with the error threshold, it is possible to effectively identify whether the data item is credible. If the doubtful data is invalid, it is eliminated before the data is transmitted to the server, thereby reducing the processing burden of the server, 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 the 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] When working, 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 suspicious data, and the relay processor extracts all data items of the suspicious collection device through the identification code to obtain analysis data, which is sent to the server for characteristic analysis. During the data preprocessing process, the relay processor marks the data items whose deviation exceeds the error threshold as suspicious data and records its identification code. The relay processor extracts all data items of the suspicious collection device according to the identification code to form analysis data. The relay processor sends the analysis data to the server, which performs characteristic analysis on these data to identify the causes and characteristics of the device abnormality.

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

[0094] For ease of understanding, here we take the voltage data as an example. The server receives abnormal voltage data from a key node. The server first classifies the abnormal voltage data according to the identification code. After determining that it comes from the same acquisition device, it calculates and analyzes the characteristics of the suspicious data, hoping to fully understand the data change pattern and potential problems. For example, the average value is obtained from the suspicious data (voltage), and the average value reflects the overall level of the data. It is judged whether the data deviates from the normal range, which is convenient for detecting persistent deviation problems. For example, the variance of the suspicious data is obtained, and the fluctuation degree of the voltage data is determined by the variance, thereby reflecting the instability of the sampling device. For example, the maximum and minimum values ​​of the voltage data of the suspicious data are obtained to understand the extreme changes of the data, which is conducive to determining short-term sudden problems, such as instantaneous overvoltage or undervoltage. For example, through time series analysis, the change trend of voltage data over time is generated, so that it is convenient to identify potential problems such as equipment aging, load changes, etc. through the change trend over a period of time. By extracting all data items of the suspicious acquisition device and performing characteristic analysis, the working status of the device and the cause of data abnormality can be deeply understood.

[0095] The "one embodiment", "another embodiment", "embodiment", "preferred embodiment" and the like mentioned in this specification refer to the specific features, structures or characteristics described in conjunction with the embodiment included in at least one embodiment generally described in this application. The same expression appearing in multiple places in the specification does not necessarily refer to the same embodiment. Further, when a specific feature, structure or characteristic is described in conjunction with any embodiment, it is claimed that the realization of such feature, structure or characteristic in conjunction with other embodiments also falls within the scope of the present invention.

[0096] Although the present invention is described herein with reference to a number of illustrative embodiments of the present invention, it will be appreciated that those skilled in the art may devise many other modifications and implementations that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, drawings, and claims, a variety of variations and modifications may be made to the components and / or layout of the subject combination layout. In addition to the variations and modifications made 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, the line includes a heavy-load line and a basic line, a plurality of interconnection points are set between the heavy-load line and the basic line, and the interconnection points are externally connected to a collaborative device, It is characterized in that The method comprises the following steps: S100, allocating a number of sampling nodes on the line and configuring a first sampling plan, obtaining data items of all sampling nodes in the first sampling plan, and combining the data items of all sampling nodes into first line data; Wherein, a timestamp and a position code are added to the data item, and the timestamp and the position code correspond to the acquisition time and the node code of the data item respectively; The first sampling plan is an execution plan for real-time sampling performed by all sampling nodes within a set time interval; S200, obtaining the position codes of the sampling nodes corresponding to the interconnection points and marking them as key nodes, and marking the position codes of the remaining sampling nodes as basic nodes; classifying the first line 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 are abnormal nodes in the key data set that exceed the deviation value by using a preset deviation value; If there is an abnormal node, execute S400; If there is no abnormal node, record the key data set and execute S500; S400, determining the abnormal area through the position coding of the abnormal node, accessing the collaborative device through the interconnection point to work, and obtaining the data items of the sampling nodes in the abnormal area again to obtain abnormal line data, and repeating S200 to S300 for the abnormal line data until there is no abnormal node; S500, filtering the key data set by the timestamp for data items of similar time periods and obtaining the key time-effectiveness data, obtaining the degree of change of each sampling node in the key time-effectiveness data, and determining whether the degree of change is within the safety threshold range, If the degree of change is within the safety threshold, it means the risk is controllable; If the degree of change exceeds the safety threshold range, it means that the risk is relatively high. Through the preset strategy of the collaborative equipment, 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.

2. The control method based on line sampling and metering according to claim 1, It is characterized in that In S400, when determining the abnormal area, the data items of the abnormal area are retrieved from the basic data set through the position code, and the data items in the same period are filtered through the timestamp to obtain the abnormal data set, which is sent to the server for recording.

3. The control method based on line sampling and metering according to claim 1, It is characterized in that In S500, the step of obtaining the degree of change is: S501, extract all data items of a sampling node in the key timeliness data and obtain 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 sequence, and making a curve graph of the comparison results; S504, obtaining a difference between a peak value and a valley value of the curve graph, wherein the difference represents a degree of change of the sampling node.

4. The control method based on line sampling and metering according to claim 1, Features: When the variation degree of the sampling node 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 performed by all sampling nodes within a set time interval; The 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 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 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.

5. The control method based on line sampling and metering according to claim 4, It is characterized in that When checking whether the sampling nodes in the position coding 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 coding set, and the server verifies the degree of association of the sampling nodes through the fluctuation amplitude of adjacent sampling nodes.

6. The control method based on line sampling and metering according to claim 1, Features: 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.

7. The control method based on line sampling and metering according to claim 6, Features: 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 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; The calculation formula for the degree of deviation is: ΔD=|D i -D|; In the formula, 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 of the corresponding identification area; Compare the degree of deviation ΔD with an 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 acquisition device is collecting doubtful and its data item is doubtful data.

8. The control method based on line sampling and metering according to claim 7, Features: 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.

9. The control method based on line sampling and metering according to claim 7, Features: The relay processor marks the questionable data, and extracts all data items of the questionable acquisition device through the identification code to obtain analysis data, which is sent to the server for characteristic analysis.

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