A method and system for automatic detection of a reactor coil winding

Abstract

The present application relates to the technical field of electric reactor coil winding, and discloses an automatic detection method and system for electric reactor coil winding, which comprises the following steps: obtaining the historical similar winding log of the current electric reactor coil, calculating the influence coefficient of each historical winding parameter according to the historical similar winding log, setting the characteristic winding parameter and the corresponding detection parameter according to the influence coefficient; collecting the real-time characteristic winding parameter according to the detection parameter, constructing a plurality of characteristic winding parameter sequences according to the real-time characteristic winding parameter, and analyzing the same, calculating the winding coefficient of each real-time characteristic winding parameter in the characteristic winding parameter sequence according to the analysis result; constructing the winding coefficient sequence, dividing the same into a plurality of winding coefficient groups, determining the winding trend factor according to the winding coefficient group, and judging whether the calibration instruction is triggered according to the winding trend factor and the winding coefficient, so as to improve the detection precision and production efficiency of the electric reactor coil winding.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of reactor coil winding, in particular to a reactor coil winding automatic detection method and system. BACKGROUND

[0002] In the process of reactor coil winding, the number of turns is usually realized according to the preset number of turns on the winding equipment. However, sometimes the counter of the coil winding equipment may not be accurate, or the operator may be careless, which is easy to cause the actual winding number of turns of the coil to be inconsistent with the design, and finally causes the winding product to deviate from the design. Secondly, the deviation of the diameter of the aluminum wire will also affect the winding of the coil, which will cause the winding height and outer diameter of the coil to deviate.

[0003] In the prior art, the winding parameters are usually checked manually, which has the problems of large workload and low efficiency and accuracy of manual detection, and it is difficult to meet the needs of efficient and accurate production. Therefore, there is an urgent need for a reactor coil winding automatic detection method and system to monitor the change of winding parameters in real time and make accurate judgments to ensure product quality and production efficiency. SUMMARY

[0004] To solve the above technical problems, the present application provides a reactor coil winding automatic detection method and system, which determines the characteristic winding parameters and the corresponding detection parameters, obtains the real-time characteristic winding parameters according to the detection parameters, constructs the characteristic winding parameter sequence and analyzes it to obtain the winding coefficient, and combines the winding trend factor to judge whether to trigger the calibration instruction, thereby improving the winding parameter detection precision and judgment accuracy, and ensuring product quality and production efficiency.

[0005] In some embodiments of the present application, a reactor coil winding automatic detection method is provided, which comprises:

[0006] Obtaining the historical similar winding log of the current reactor coil, calculating the influence coefficient of each historical winding parameter according to the historical similar winding log, and setting the characteristic winding parameters and the corresponding detection parameters according to the influence coefficient;

[0007] According to the detection parameters, real-time characteristic winding parameters are collected, a plurality of characteristic winding parameter sequences are constructed according to the real-time characteristic winding parameters, and the sequences are analyzed, and the winding coefficient of each real-time characteristic winding parameter in the characteristic winding parameter sequence is calculated according to the analysis result.

[0008] The winding coefficient sequence is constructed and divided into a plurality of winding coefficient groups, the winding trend factor is determined according to the winding coefficient groups, and whether to trigger the calibration instruction is judged according to the winding trend factor and the winding coefficient.

[0009] In some embodiments of the present application, the influence coefficient of each historical winding parameter is calculated according to the historical similar winding log, including:

[0010] Obtaining the characteristic parameters of the current reactor to be detected, and performing characteristic similarity analysis on the characteristic parameters of the detected reactor to obtain a characteristic similarity degree;

[0011] Setting the historical calibration winding log of the detected reactor coil with a characteristic similarity degree greater than a preset similarity threshold as the historical similar winding log of the current reactor coil to be detected;

[0012] Dividing the historical similar winding log into a plurality of category log sets according to the calibration reason, and each category log set corresponds to a calibration reason and a target winding parameter group;

[0013] The target winding parameter group includes a target winding parameter and a parameter difference value sequence corresponding to the target winding parameter, and the parameter difference value sequence includes a plurality of parameter difference values corresponding to the target winding parameter, and the parameter difference value sequence is sorted according to the parameter difference value size;

[0014] Generating a historical winding influence data packet of the parameter difference value of each target winding parameter in the corresponding target winding parameter group according to the same category log set;

[0015] Generating a historical influence evaluation value corresponding to the parameter difference value based on the historical winding influence data packet, and generating a historical influence evaluation value sequence;

[0016] Judging whether there is a correlation based on the parameter difference value sequence and the corresponding historical influence evaluation value sequence, if yes, generating an influence sub-coefficient according to the plurality of parameter difference values in the parameter difference value sequence and the plurality of historical influence evaluation values in the corresponding historical influence sub-evaluation value sequence;

[0017] Extracting time characteristics from a plurality of historical similar winding logs in the category log set of each target winding parameter to obtain time characteristic data, and setting a compensation coefficient;

[0018] Generating an influence coefficient corresponding to the target winding parameter according to the influence sub-coefficient and the compensation coefficient.

[0019] In some embodiments of the present application, the influence coefficient corresponding to the target winding parameter is generated according to the influence sub-coefficient and the compensation coefficient, including:

[0020] Pre-setting a plurality of influence evaluation indexes and a plurality of risk evaluation indexes;

[0021] Evaluating the historical winding influence data packet of the parameter difference value of each target winding parameter based on the plurality of influence evaluation indexes to obtain a plurality of first influence evaluation values;

[0022] Historical impact assessment values ​​are generated based on several first impact assessment values ​​of several impact assessment indicators and their corresponding weight coefficients to determine the corresponding parameter differences.

[0023] Influence sub-coefficients are generated based on the differences of several parameters and the corresponding historical impact assessment values;

[0024] The formula for calculating the influence coefficient is as follows:

[0025] ;

[0026] Where Y is the influence coefficient, and n is the number of parameter differences in the parameter difference sequence. Let p0 be the i-th parameter difference in the parameter difference sequence, p0 be the average parameter difference in the parameter difference sequence, yi be the i-th historical impact assessment value corresponding to the i-th parameter difference, and y0 be the average historical impact assessment value in the historical impact assessment value sequence.

[0027] The time characteristic data of each target winding parameter are evaluated based on several risk assessment indicators to obtain several first risk assessment values.

[0028] The risk assessment value of the corresponding target winding parameter is generated based on several first risk assessment values ​​of several risk assessment indicators and their corresponding weighting coefficients.

[0029] The compensation coefficient r is set based on the risk assessment value;

[0030] The influence coefficient is generated based on the compensation coefficient and the influence sub-coefficient. ;

[0031] .

[0032] In some embodiments of this application, the characteristic winding parameters and corresponding detection parameters are set according to the influence coefficient, including:

[0033] Pre-set the threshold for the influence coefficient;

[0034] The target winding parameter with an influence coefficient greater than the influence coefficient threshold is set as the characteristic winding parameter, and the weight coefficient of each characteristic winding parameter is set.

[0035] All feature winding parameters are sorted according to their weighting coefficients to obtain a sequence of feature winding parameters;

[0036] A first preset weight partitioning node, a second preset weight partitioning node, and a third preset weight partitioning node are pre-defined;

[0037] According to the preset weight, each feature winding parameter in the feature winding parameter sequence is divided, and a first feature winding parameter group, a second feature winding parameter group, a third feature winding parameter group and a fourth preset feature winding parameter group are obtained;

[0038] The detection time interval of each feature winding parameter in the first feature winding parameter group is set as a first preset time interval, the number of detection devices of each feature winding parameter is set as a fourth preset number, and a plurality of detection devices are generated in combination with a detection device reference library;

[0039] The detection time interval of each feature winding parameter in the second feature winding parameter group is set as a second preset time interval, the number of detection devices of each feature winding parameter is set as a third preset number, and a plurality of detection devices are generated in combination with a detection device reference library;

[0040] The detection time interval of each feature winding parameter in the third feature winding parameter group is set as a third preset time interval, the number of detection devices of each feature winding parameter is set as a second preset number, and a plurality of detection devices are generated in combination with a detection device reference library;

[0041] The detection time interval of each feature winding parameter in the fourth feature winding parameter group is set as a fourth preset time interval, the number of detection devices of each feature winding parameter is set as a first preset number, and a plurality of detection devices are generated in combination with a detection device reference library;

[0042] According to the detection time interval of each feature winding parameter, a plurality of detection time nodes are generated, and the detection parameters of each feature winding parameter are generated in combination with the plurality of detection devices.

[0043] In some embodiments of the present application, the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence is calculated according to the analysis result, comprising:

[0044] According to the detection parameters, the real-time feature winding parameters are obtained, and the feature winding parameter sequence M of the same parameter is constructed, M (m1, m2, …, ms), wherein mj is the real-time feature winding parameter at the jth detection time node;

[0045] A plurality of standard winding parameter intervals of the current reactor coil are generated;

[0046] Each feature winding parameter sequence is compared with the corresponding standard winding parameter interval to determine whether each real-time feature winding parameter in the feature winding parameter sequence is in the standard winding parameter interval;

[0047] If no, a first parameter difference value of the corresponding real-time feature winding parameter and the preferred critical winding parameter of the corresponding standard winding parameter interval is calculated, and an abnormal deviation coefficient of the corresponding real-time feature winding parameter is generated according to the first parameter difference value;

[0048] A winding coefficient of the abnormal deviation coefficient of the real-time feature winding parameter is generated based on the abnormal deviation coefficient-winding coefficient mapping table.

[0049] If yes, a second parameter difference value of the corresponding real-time feature winding parameter and the standard winding parameter median of the corresponding standard winding parameter interval is calculated, and a normal deviation coefficient of the corresponding real-time feature winding parameter is generated according to the second parameter difference value.

[0050] A winding coefficient of the normal deviation coefficient of the real-time feature winding parameter is generated based on the normal deviation coefficient-winding coefficient mapping table.

[0051] In some embodiments of the present application, a winding coefficient sequence is constructed, comprising:

[0052] A winding coefficient sequence H (H1, H2, …, Hs) of the corresponding feature winding parameter sequence is generated according to the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence, wherein hj is the winding coefficient corresponding to the jth real-time feature winding parameter.

[0053] The winding coefficient difference value of each winding coefficient in the winding coefficient sequence and the previous adjacent winding coefficient is calculated, and a sequence fluctuation coefficient is calculated.

[0054] ;

[0055] Wherein D is the sequence fluctuation coefficient, is the winding coefficient difference value of the jth winding coefficient and the previous adjacent winding coefficient.

[0056] The confidence coefficient of each winding coefficient is calculated.

[0057] ;

[0058] Wherein Kj is the confidence coefficient of the jth winding coefficient.

[0059] If the confidence coefficient is less than a preset confidence coefficient threshold, the corresponding winding coefficient is modified according to the adjacent winding coefficient, the winding coefficient with the confidence coefficient less than the preset confidence threshold is replaced according to the modified winding coefficient, and a new winding coefficient sequence is obtained.

[0060] In some embodiments of the present application, a plurality of winding coefficient groups are divided, and a winding trend factor is determined according to the winding coefficient groups, comprising:

[0061] The winding coefficient combination number is set according to the sequence fluctuation coefficient of each feature winding parameter sequence, and adjacent winding coefficients in the corresponding winding coefficient sequence are combined to obtain a plurality of winding coefficient groups;

[0062] The winding coefficients in each winding coefficient group are curve-fitted, and the curve slope at each fitting point is determined;

[0063] The mean value of the curve slope in the same winding coefficient group is calculated, and the winding trend factor corresponding to the winding coefficient group is generated in combination with the coefficient variance in the winding coefficient group.

[0064] In some embodiments of the present application, the winding coefficient combination number is set according to the sequence fluctuation coefficient of each feature winding parameter sequence, including:

[0065] The first, second, third and fourth preset fluctuation coefficient intervals are preset;

[0066] When the sequence fluctuation coefficient is in the first preset fluctuation coefficient interval, the winding coefficient combination number is set as the fourth preset combination number;

[0067] When the sequence fluctuation coefficient is in the second preset fluctuation coefficient interval, the winding coefficient combination number is set as the third preset combination number;

[0068] When the sequence fluctuation coefficient is in the third preset fluctuation coefficient interval, the winding coefficient combination number is set as the second preset combination number;

[0069] When the sequence fluctuation coefficient is in the fourth preset fluctuation coefficient interval, the winding coefficient combination number is set as the first preset combination number.

[0070] In some embodiments of the present application, whether a calibration instruction is triggered is judged according to the winding trend factor and the winding coefficient, including:

[0071] The winding coefficient mean value of each winding coefficient group is calculated, a correction coefficient is set according to the corresponding winding trend factor, and the winding coefficient mean value is corrected to obtain a corrected winding coefficient;

[0072] The corrected winding coefficient corresponding to each feature winding parameter is compared with the corresponding preset winding coefficient threshold value, and if the corrected winding coefficient is less than the preset winding coefficient threshold value, it is judged that the calibration instruction is triggered;

[0073] If the corrected winding coefficient is not less than the preset winding coefficient threshold value, it is judged that the calibration instruction is not triggered.

[0074] In some embodiments of the present application, an automatic detection system for a reactor coil winding is further included:

[0075] The acquisition module is used to acquire the historical similar winding log of the current reactor coil, calculate the influence coefficient of each historical winding parameter based on the historical similar winding log, and set the characteristic winding parameters and corresponding detection parameters based on the influence coefficient;

[0076] The analysis module is used to collect real-time feature winding parameters according to the detection parameters, construct several feature winding parameter sequences based on the real-time feature winding parameters, perform analysis, and calculate the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence based on the analysis results.

[0077] The judgment module is used to construct the winding coefficient sequence and divide it into several winding coefficient groups. It determines the winding trend factor based on the winding coefficient groups and determines whether to trigger the calibration command based on the winding trend factor and the winding coefficients.

[0078] The automatic detection method and system for reactor coil winding according to the embodiments of this application have the following advantages compared with the prior art:

[0079] By determining the characteristic winding parameters and the corresponding detection parameters, the real-time characteristic winding parameters are obtained according to the detection parameters. The characteristic winding parameter sequence is constructed and analyzed to obtain the winding coefficient. Combined with the winding trend factor, it is determined whether a calibration command is triggered, thereby improving the detection accuracy and judgment accuracy of the winding parameters and ensuring product quality and production efficiency. Attached Figure Description

[0080] Figure 1 This is a flowchart illustrating an automatic detection method for reactor coil winding in an embodiment of this application.

[0081] Figure 2 This is a schematic diagram of an automatic detection system for reactor coil winding in an embodiment of this application. Detailed Implementation

[0082] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0083] like Figure 1 As shown in the figure, an automatic detection method for reactor coil winding according to an embodiment of this application includes:

[0084] Step S101: Obtain the historical similar winding log of the current reactor coil, calculate the influence coefficient of each historical winding parameter based on the historical similar winding log, and set the characteristic winding parameters and corresponding detection parameters based on the influence coefficients;

[0085] Step S102: Collect real-time feature winding parameters according to the detection parameters, construct a plurality of feature winding parameter sequences according to the real-time feature winding parameters, and analyze, and calculate the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence according to the analysis result;

[0086] Step S103: Construct a winding coefficient sequence, divide it into a plurality of winding coefficient groups, determine a winding trend factor according to the winding coefficient groups, and judge whether to trigger a calibration instruction according to the winding trend factor and the winding coefficient.

[0087] In the embodiment, the historical winding parameters include but are not limited to the number of turns, the outer diameter, the winding height, the tension and the like, and the influence coefficient refers to the influence degree and the abnormal risk degree of the deviation of the historical winding parameters on the winding quality and the winding efficiency.

[0088] In the embodiment, the feature winding parameter refers to a parameter with a greater influence degree and abnormal risk degree on the winding quality and the winding efficiency, and the detection parameter includes a detection time interval and a detection device.

[0089] In some embodiments of the present application, the influence coefficient of each historical winding parameter is calculated according to the historical similar winding log, including:

[0090] Obtain the feature parameters of the current to-be-detected reactor, and perform feature similarity analysis on the feature parameters of the detected reactor to obtain a feature similarity;

[0091] The historical calibration winding log of the detected reactor coil with a feature similarity greater than a preset similarity threshold is set as the historical similar winding log of the current to-be-detected reactor coil;

[0092] The historical similar winding log is divided into a plurality of category log sets according to the calibration reason, and each category log set corresponds to a calibration reason and a target winding parameter group;

[0093] The target winding parameter group includes a target winding parameter and a parameter difference value sequence corresponding to the target winding parameter, the parameter difference value sequence includes a plurality of parameter difference values corresponding to the target winding parameter, and the parameter difference value sequence is sorted according to the parameter difference value size;

[0094] According to the same category log set, a historical winding influence data packet of each parameter difference value in the corresponding target winding parameter group is generated;

[0095] Based on the historical winding influence data packet, a historical influence evaluation value of the corresponding parameter difference value is generated, and a historical influence evaluation value sequence is generated;

[0096] judging whether the correlation exists based on the parameter difference value sequence and the corresponding historical influence evaluation value sequence, and if so, generating an influence sub-coefficient according to a plurality of parameter difference values in the parameter difference value sequence and a plurality of historical influence evaluation values in the corresponding historical influence sub-evaluation value sequence;

[0097] extracting time features from a plurality of historical similar winding logs in the category log set of each target winding parameter, obtaining time feature data, and setting a compensation coefficient;

[0098] generating an influence coefficient of the corresponding target winding parameter according to the influence sub-coefficient and the compensation coefficient.

[0099] In the embodiment, the calibration reason includes single deviation reasons such as turn number deviation, height deviation, and outer diameter deviation.

[0100] In the embodiment, the calibration reason is a single deviation reason, that is, there is only one target winding parameter in the target winding parameter group. For example, when the calibration reason is turn number deviation, the target winding parameter group is historical turn number, and the parameter difference value sequence refers to the turn number deviation value of the historical turn number and the standard turn number in each historical similar winding log of the same category log set.

[0101] In the embodiment, the historical winding influence data packet refers to data that the deviated historical winding parameter has an influence on winding quality and effect, such as inductance, insulation performance, and mechanical strength.

[0102] In the embodiment, the time feature data includes time interval, frequency, and time length of the target winding parameter deviation.

[0103] In some embodiments of the present application, generating an influence coefficient of the corresponding target winding parameter according to the influence sub-coefficient and the compensation coefficient includes:

[0104] pre-setting a plurality of influence evaluation indexes and a plurality of risk evaluation indexes;

[0105] evaluating the historical winding influence data packet of the parameter difference value of each target winding parameter based on the plurality of influence evaluation indexes, to obtain a plurality of first influence evaluation values;

[0106] generating a historical influence evaluation value of the corresponding parameter difference value according to the plurality of first influence evaluation values of the plurality of influence evaluation indexes and the corresponding weight coefficients;

[0107] generating an influence sub-coefficient according to a plurality of parameter difference values and corresponding historical influence evaluation values;

[0108] The calculation formula of the influence sub-coefficient is:

[0109] ;

[0110] wherein Y is a sub-influence coefficient, n is a number of parameter difference values in the parameter difference value sequence, is the i-th parameter difference value in the parameter difference value sequence, p0 is a parameter difference value average of the parameter difference value sequence, yi is an i-th historical influence evaluation value corresponding to the i-th parameter difference value, y0 is a historical influence evaluation value average in the historical influence evaluation value sequence;

[0111] evaluate the time characteristic data of each target routing parameter based on a plurality of risk evaluation indexes to obtain a plurality of first risk evaluation values;

[0112] generate a risk evaluation value of the corresponding target routing parameter according to the plurality of first risk evaluation values of the plurality of risk evaluation indexes and corresponding weight coefficients;

[0113] set a compensation coefficient r according to the risk evaluation value;

[0114] generate an influence coefficient according to the compensation coefficient and the sub-influence coefficient .

[0115] .

[0116] In the embodiment, the influence evaluation index refers to evaluating the influence degree on the routing quality, production efficiency, etc., and the risk evaluation index refers to evaluating the risk probability and the risk probability.

[0117] In the embodiment, a first preset risk evaluation value interval, a second preset risk evaluation value interval, a third preset risk evaluation value interval, and a fourth preset risk evaluation value interval are set in advance, when the risk evaluation value is in the first preset risk evaluation value interval, the compensation coefficient r is set as a first preset compensation coefficient r1, when the risk evaluation value is in the second preset risk evaluation value interval, the compensation coefficient r is set as a second preset compensation coefficient r2, when the risk evaluation value is in the third preset risk evaluation value interval, the compensation coefficient r is set as a third preset compensation coefficient r3, when the risk evaluation value is in the fourth preset risk evaluation value interval, the compensation coefficient r is set as a fourth preset compensation coefficient r4, and 0.8 < r1 < r2 < 1 < r3 < r4 < 1.2.

[0118] In the embodiment, the preset risk evaluation value interval and the corresponding preset compensation coefficient are set by the historical routing log, the deviation probability of the historical routing parameter and the influence are set, so that the sub-influence coefficient is appropriately corrected, and the subsequent detection time interval accuracy and equipment detection accuracy are improved.

[0119] In some embodiments of the present application, the characteristic routing parameter and the corresponding detection parameter are set according to the influence coefficient, comprising:

[0120] a preset influence coefficient threshold is set.

[0121] setting the target winding parameter with an influence coefficient greater than the influence coefficient threshold as a characteristic winding parameter, and setting a weight coefficient of each characteristic winding parameter;

[0122] sequencing all the characteristic winding parameters according to the weight coefficients to obtain a characteristic winding parameter sequence;

[0123] pre-setting a first preset weight division node, a second preset weight division node, and a third preset weight division node;

[0124] dividing each characteristic winding parameter in the characteristic winding parameter sequence according to the preset weight division nodes to obtain a first characteristic winding parameter group, a second characteristic winding parameter group, a third characteristic winding parameter group, and a fourth preset characteristic winding parameter group;

[0125] setting a detection time interval of each characteristic winding parameter in the first characteristic winding parameter group as a first preset time interval, setting a detection equipment quantity of each characteristic winding parameter as a fourth preset quantity, and generating a plurality of detection equipment in combination with a detection equipment reference library;

[0126] setting a detection time interval of each characteristic winding parameter in the second characteristic winding parameter group as a second preset time interval, setting a detection equipment quantity of each characteristic winding parameter as a third preset quantity, and generating a plurality of detection equipment in combination with the detection equipment reference library;

[0127] setting a detection time interval of each characteristic winding parameter in the third characteristic winding parameter group as a third preset time interval, setting a detection equipment quantity of each characteristic winding parameter as a second preset quantity, and generating a plurality of detection equipment in combination with the detection equipment reference library;

[0128] setting a detection time interval of each characteristic winding parameter in the fourth characteristic winding parameter group as a fourth preset time interval, setting a detection equipment quantity of each characteristic winding parameter as a first preset quantity, and generating a plurality of detection equipment in combination with the detection equipment reference library;

[0129] generating a plurality of detection time nodes according to the detection time interval of each characteristic winding parameter, and generating a detection parameter of each characteristic winding parameter in combination with the plurality of detection equipment.

[0130] In the embodiment, the detection equipment reference library includes historical detection equipment and corresponding historical detection accuracy of each characteristic winding parameter, the historical detection equipment is sequenced according to the historical detection accuracy, the top-ranked detection equipment is selected according to the set detection equipment quantity, and a detection parameter is generated in combination with the corresponding detection time interval, for example, the detection equipment includes an infrared sensor, a laser sensor, an image acquisition device, etc.

[0131] In the embodiment, the preset weight coefficients corresponding to the first preset weight division node, the second preset weight division node, the third preset weight division node and the fourth preset weight division node are 0.8, 0.5, 0.3, that is, the feature winding parameter with a weight coefficient greater than or equal to 0.8 is divided into the first feature winding parameter group, and the feature winding parameters are divided in this way to obtain the first feature winding parameter group, the second feature winding parameter group, the third feature winding parameter group and the fourth preset feature winding parameter group.

[0132] In the embodiment, the first preset time interval < the second preset time interval < the third preset time interval < the fourth preset time interval, the first preset number < the second preset number < the third preset number < the fourth preset number, and the preset time interval and the preset number are set by the required accuracy of the historical winding parameters and the historical detection device reliability in the historical winding log.

[0133] In the embodiment, the feature winding parameters are divided into a plurality of feature winding parameter groups according to the preset weight division nodes, and reasonable and reliable detection time intervals and detection devices are set, so as to improve the winding parameter detection accuracy and lay a foundation for subsequent judgment of whether to trigger the calibration instruction.

[0134] In some embodiments of the present application, the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence is calculated according to the analysis result, including:

[0135] The real-time feature winding parameters are obtained according to the detection parameters, and the feature winding parameter sequence M of the same parameter is constructed, M (m1, m2, …, ms), wherein mj is the real-time feature winding parameter at the jth detection time node;

[0136] A plurality of standard winding parameter intervals of the current reactor coil are generated;

[0137] Each feature winding parameter sequence is compared with the corresponding standard winding parameter interval to determine whether each real-time feature winding parameter in the feature winding parameter sequence is in the standard winding parameter interval;

[0138] If not, the first parameter difference between the corresponding real-time feature winding parameter and the optimal critical winding parameter of the corresponding standard winding parameter interval is calculated, and the abnormal deviation coefficient of the corresponding real-time feature winding parameter is generated according to the first parameter difference;

[0139] The winding coefficient of the abnormal deviation coefficient of the real-time feature winding parameter is generated based on the abnormal deviation coefficient-winding coefficient mapping table;

[0140] If yes, the second parameter difference between the corresponding real-time feature winding parameter and the standard winding parameter median of the corresponding standard winding parameter interval is calculated, and the normal deviation coefficient of the corresponding real-time feature winding parameter is generated according to the second parameter difference.

[0141] The winding coefficient of the normal deviation coefficient of the real-time feature winding parameter is generated based on the normal deviation coefficient-winding coefficient mapping table.

[0142] In the embodiment, the critical winding parameter refers to the critical value closest to the real-time feature winding parameter in the standard winding parameter interval, and the standard winding parameter median value refers to the middle value of the standard winding parameter interval.

[0143] In the embodiment, the abnormal deviation coefficient = the first parameter difference value / the critical value difference value of the standard winding parameter interval, for example, the real-time feature winding parameter is 12, the standard winding coefficient interval is (5, 10), the critical value difference value is 10-5, and the abnormal deviation coefficient is (12-10) / (10-5), that is, it is explained that the upper limit is exceeded by 40%.

[0144] In the embodiment, the normal deviation coefficient = the second parameter difference value / half of the standard winding parameter interval range, for example, the real-time feature winding parameter is 7, the standard winding coefficient interval is (1, 9), and the normal deviation coefficient is (7-5) / 4, that is, it is explained that the deviation median value is 50%.

[0145] In the embodiment, the abnormal deviation coefficient-winding coefficient mapping table and the normal deviation coefficient-winding coefficient mapping table are both constructed by calculating the historical parameter difference value of the historical winding parameter and the standard winding parameter interval, and the historical winding coefficient corresponding to the winding precision quantization, when the abnormal deviation coefficient is greater, the preset winding coefficient mapped in the mapping table is smaller, and the preset winding coefficient in the abnormal deviation coefficient-winding coefficient mapping table is (-1, 0), when the normal deviation coefficient is greater, the preset winding coefficient mapped in the mapping table is smaller, and the preset winding coefficient in the normal deviation coefficient-winding coefficient mapping table is (0, 1).

[0146] In the embodiment, by matching the corresponding winding coefficient according to the abnormal deviation coefficient and the normal deviation coefficient, the deviation degree of the real-time feature winding parameter is accurately and detailedly quantized, which lays a foundation for subsequent judgment whether to trigger the calibration instruction, and improves the detection precision and the detection efficiency.

[0147] In some embodiments of the present application, the winding coefficient sequence is constructed, comprising:

[0148] The winding coefficient sequence H (H1, H2, …, Hs) of the corresponding feature winding parameter sequence is generated according to the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence, wherein hj is the winding coefficient corresponding to the jth real-time feature winding parameter;

[0149] The winding coefficient difference value of each winding coefficient in the winding coefficient sequence and the previous adjacent winding coefficient is calculated, and the sequence fluctuation coefficient is calculated.

[0150] ;

[0151] wherein D is a sequence fluctuation coefficient, is a winding coefficient difference between the jth winding coefficient and the previous adjacent winding coefficient;

[0152] a confidence coefficient of each winding coefficient is calculated;

[0153] ;

[0154] wherein Kj is a confidence coefficient of the jth winding coefficient;

[0155] if the confidence coefficient is less than a preset confidence coefficient threshold, the corresponding winding coefficient is modified according to the adjacent winding coefficient, the winding coefficient whose confidence coefficient is less than the preset confidence threshold is replaced according to the modified winding coefficient, and a new winding coefficient sequence is obtained.

[0156] In the embodiment, the sequence fluctuation coefficient is used to evaluate the fluctuation degree of the winding coefficient sequence as a whole. When the sequence fluctuation coefficient is larger, the fluctuation degree is larger, and vice versa.

[0157] In the embodiment, the value range of the confidence coefficient is (0, 1). When the winding coefficient exceeds the overall fluctuation reference, the confidence coefficient is lower, and is closer to 0, and vice versa.

[0158] In the embodiment, the winding coefficient with low accuracy is replaced by calculating the sequence fluctuation coefficient of the winding coefficient sequence and the confidence coefficient of each winding coefficient, and the foundation is laid for subsequent calculation of the winding trend factor, and the calculation accuracy of the winding coefficient and the winding trend factor is improved.

[0159] In some embodiments of the present application, the winding coefficient sequence is divided into a plurality of winding coefficient groups, and the winding trend factor is determined according to the winding coefficient groups, comprising:

[0160] The number of winding coefficient groups is set according to the sequence fluctuation coefficient of each characteristic winding parameter sequence, and the adjacent winding coefficients in the corresponding winding coefficient sequence are combined to obtain a plurality of winding coefficient groups;

[0161] The winding coefficients in each winding coefficient group are curve fitted, and the curve slope at each fitting point is determined;

[0162] The mean value of the curve slopes in the same winding coefficient group is calculated, and the winding trend factor of the corresponding winding coefficient group is generated in combination with the coefficient variance in the winding coefficient group.

[0163] In the embodiment, the winding trend factor is equal to the average of the curve slope divided by the coefficient variance, that is, the average of the curve slope is positive and greater, and the coefficient contrast is smaller, and the corresponding winding trend factor is greater, and vice versa.

[0164] In the embodiment, the winding coefficient sequence is divided into a plurality of winding coefficient groups, the number of winding coefficient groups is dynamically adjusted according to the sequence fluctuation coefficient, and the trend analysis accuracy and the calculation efficiency are improved.

[0165] In some embodiments of the present application, the number of winding coefficient groups is set according to the sequence fluctuation coefficient of each feature winding parameter sequence, including:

[0166] The first preset fluctuation coefficient interval, the second preset fluctuation coefficient interval, the third preset fluctuation coefficient interval and the fourth preset fluctuation coefficient interval are set in advance;

[0167] When the sequence fluctuation coefficient is in the first preset fluctuation coefficient interval, the number of winding coefficient groups is set to the fourth preset combination number;

[0168] When the sequence fluctuation coefficient is in the second preset fluctuation coefficient interval, the number of winding coefficient groups is set to the third preset combination number;

[0169] When the sequence fluctuation coefficient is in the third preset fluctuation coefficient interval, the number of winding coefficient groups is set to the second preset combination number;

[0170] When the sequence fluctuation coefficient is in the fourth preset fluctuation coefficient interval, the number of winding coefficient groups is set to the first preset combination number.

[0171] In the embodiment, the first preset fluctuation coefficient interval < the second preset fluctuation coefficient interval < the third preset fluctuation coefficient interval < the fourth preset fluctuation coefficient interval, and the first preset combination number < the second preset combination number < the third preset combination number < the fourth preset combination number.

[0172] In the embodiment, the preset fluctuation coefficient interval and the preset combination number are determined according to the statistical analysis result of the historical data, a reasonable combination number is selected according to the fluctuation coefficient interval, so that a higher trend analysis accuracy can be maintained under different fluctuation degrees, and the calculation accuracy of the winding trend factor is improved.

[0173] In some embodiments of the present application, whether a calibration instruction is triggered is determined according to the winding trend factor and the winding coefficient, including:

[0174] The average winding coefficient of each winding coefficient group is calculated, the correction coefficient is set according to the corresponding winding trend factor, and the average winding coefficient is corrected to obtain the corrected winding coefficient;

[0175] The correction winding coefficient corresponding to each feature winding parameter is compared with the corresponding preset winding coefficient threshold value, and if the correction winding coefficient is less than the preset winding coefficient threshold value, it is determined that the calibration instruction is triggered;

[0176] If the correction winding coefficient is not less than the preset winding coefficient threshold value, it is determined that the calibration instruction is not triggered.

[0177] In the present embodiment, the larger the winding trend factor, the larger the corresponding correction coefficient, and vice versa. The value range of the correction coefficient is (0.75, 1.25).

[0178] In the present embodiment, by calculating the correction winding coefficient of each winding coefficient group, the accuracy of the analysis of each feature winding parameter is improved, and according to the comparison result of the correction winding coefficient and the preset winding coefficient threshold value, it is intelligently determined whether the calibration instruction is triggered, so as to ensure the winding quality of the reactor coil. If the calibration instruction is triggered, the system automatically adjusts the winding parameter or prompts the operator to perform manual calibration, so as to reduce the production quality problems caused by the deviation of the winding parameter. In addition, the method and system also have self-adaptive ability, which can continuously optimize the detection parameter and calibration strategy according to the historical data and real-time detection data, and further improve the detection efficiency and accuracy.

[0179] In some embodiments of the present application, as shown in Figure 2 , the present application also provides an automatic detection system for winding of a reactor coil:

[0180] The acquisition module is configured to acquire a historical similar winding log of the current reactor coil, calculate an influence coefficient of each historical winding parameter according to the historical similar winding log, and set a feature winding parameter and a corresponding detection parameter according to the influence coefficient;

[0181] The analysis module is configured to collect real-time feature winding parameters according to the detection parameters, construct a plurality of feature winding parameter sequences according to the real-time feature winding parameters, and analyze the feature winding parameter sequences, and calculate a winding coefficient of each real-time feature winding parameter in the feature winding parameter sequences according to the analysis result;

[0182] The judgment module is configured to construct a winding coefficient sequence and divide the winding coefficient sequence into a plurality of winding coefficient groups, determine a winding trend factor according to the winding coefficient groups, and determine whether to trigger a calibration instruction according to the winding trend factor and the winding coefficient.

[0183] The above only describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make some improvements and replacements without departing from the technical principles of the present application, and these improvements and replacements should also be considered as the protection scope of the present application.

Claims

1. A method for automatically detecting the winding of a reactor coil, characterized in that, The method comprises the following steps: obtaining a historical similar winding log of the current reactor coil, calculating an influence coefficient of each historical winding parameter according to the historical similar winding log, and setting a characteristic winding parameter and a corresponding detection parameter according to the influence coefficient; collecting real-time characteristic winding parameters according to the detection parameters, constructing a plurality of characteristic winding parameter sequences according to the real-time characteristic winding parameters, and analyzing the sequences, and calculating a winding coefficient of each real-time characteristic winding parameter in the characteristic winding parameter sequence according to the analysis result; constructing a winding coefficient sequence and dividing it into a plurality of winding coefficient groups, determining a winding trend factor according to the winding coefficient groups, and judging whether to trigger a calibration instruction according to the winding trend factor and the winding coefficient; calculating the winding coefficient of each real-time characteristic winding parameter in the characteristic winding parameter sequence according to the analysis result, comprising: obtaining real-time characteristic winding parameters according to the detection parameters, and constructing a characteristic winding parameter sequence M of the same parameter, M (m1, m2, …, ms), wherein mj is the real-time characteristic winding parameter at the jth detection time node; generating a plurality of standard winding parameter intervals of the current reactor coil; comparing each characteristic winding parameter sequence with the corresponding standard winding parameter interval to determine whether each real-time characteristic winding parameter in the characteristic winding parameter sequence is in the standard winding parameter interval; if not, calculating a first parameter difference between the corresponding real-time characteristic winding parameter and the preferred critical winding parameter of the corresponding standard winding parameter interval, and generating an abnormal deviation coefficient of the corresponding real-time characteristic winding parameter according to the first parameter difference; generating the winding coefficient of the abnormal deviation coefficient of the real-time characteristic winding parameter based on the abnormal deviation coefficient-winding coefficient mapping table; if yes, calculating a second parameter difference between the corresponding real-time characteristic winding parameter and the standard winding parameter median of the corresponding standard winding parameter interval, and generating a normal deviation coefficient of the corresponding real-time characteristic winding parameter according to the second parameter difference; generating the winding coefficient of the normal deviation coefficient of the real-time characteristic winding parameter based on the normal deviation coefficient-winding coefficient mapping table; dividing into a plurality of winding coefficient groups, determining a winding trend factor according to the winding coefficient groups, comprising: combining the number of winding coefficient groups according to the sequence fluctuation coefficient of each characteristic winding parameter sequence, combining adjacent winding coefficients in the corresponding winding coefficient sequence to obtain a plurality of winding coefficient groups; curve fitting is performed on the winding coefficients in each winding coefficient group, and the curve slope at each fitting point is determined; calculate the average of the curve slopes in the same winding coefficient group, and generate the winding trend factor of the corresponding winding coefficient group combined with the coefficient variance in the winding coefficient group.

2. The automatic detection method of the reactor coil winding according to claim 1, wherein calculate the influence coefficient of each historical winding parameter according to the historical similar winding log, comprising: obtaining the characteristic parameters of the current reactor to be detected, and performing characteristic similarity analysis with the characteristic parameters of the detected reactor to obtain a characteristic similarity degree; setting the historical calibration winding log of the detected reactor coil with a characteristic similarity degree greater than a preset similarity threshold as the historical similar winding log of the current reactor coil to be detected. The historical similar winding logs are divided into several category log sets according to calibration reasons, and each category log set corresponds to a calibration reason and a target winding parameter group; The target winding parameter group includes a target winding parameter and a parameter difference sequence corresponding to the target winding parameter, and the parameter difference sequence includes several parameter differences corresponding to the target winding parameter, and the parameter difference sequence is sorted according to the size of the parameter difference; The historical winding influence data packet of each parameter difference of the corresponding target winding parameter group is generated according to the same category log set; The historical influence evaluation value corresponding to the parameter difference is generated based on the historical winding influence data packet, and a historical influence evaluation value sequence is generated; If there is a correlation based on the parameter difference sequence and the corresponding historical influence evaluation value sequence, an influence sub-coefficient is generated according to the several parameter differences in the parameter difference sequence and the several historical influence evaluation values in the corresponding historical influence sub-evaluation value sequence; The time characteristics of several historical similar winding logs in the category log set of each target winding parameter are extracted to obtain time characteristic data, and a compensation coefficient is set; The influence coefficient corresponding to the target winding parameter is generated according to the influence sub-coefficient and the compensation coefficient.

3. The automatic detection method of the reactor coil winding according to claim 2, wherein The influence coefficient corresponding to the target winding parameter is generated according to the influence sub-coefficient and the compensation coefficient, including: A plurality of influence evaluation indexes and a plurality of risk evaluation indexes are preset; The historical winding influence data packet of each parameter difference of each target winding parameter is evaluated based on the plurality of influence evaluation indexes to obtain a plurality of first influence evaluation values; The historical influence evaluation value corresponding to the parameter difference is generated according to the plurality of first influence evaluation values of the plurality of influence evaluation indexes and the corresponding weight coefficients; The influence sub-coefficient is generated according to the plurality of parameter differences and the corresponding historical influence evaluation values; The calculation formula of the influence sub-coefficient is: ； Wherein, Y is the influence coefficient, n is the number of parameter difference in the parameter difference sequence, is the i th parameter difference in the parameter difference sequence, p0 is the average value of the parameter difference in the parameter difference sequence, yi is the i th historical influence evaluation value corresponding to the i th parameter difference, y0 is the average value of the historical influence evaluation value in the historical influence evaluation value sequence; The time characteristic data of each target winding parameter is evaluated based on the plurality of risk evaluation indexes to obtain a plurality of first risk evaluation values; The risk evaluation value corresponding to the target winding parameter is generated according to the plurality of first risk evaluation values of the plurality of risk evaluation indexes and the corresponding weight coefficients; The compensation coefficient r is set according to the risk evaluation value; According to the compensation coefficient and the influence sub-coefficient, the influence coefficient is generated ; 。 4. The automatic detection method of the reactor coil winding according to claim 3, wherein The characteristic winding parameter and the corresponding detection parameter are set according to the influence coefficient, including: The influence coefficient threshold is preset; The target winding parameter with an influence coefficient greater than the influence coefficient threshold is set as a characteristic winding parameter, and the weight coefficient of each characteristic winding parameter is set; The characteristic winding parameters are sorted according to the weight coefficients to obtain a characteristic winding parameter sequence; The first preset weight division node, the second preset weight division node, and the third preset weight division node are preset; Each characteristic winding parameter in the characteristic winding parameter sequence is divided according to the preset weight division node to obtain a first characteristic winding parameter group, a second characteristic winding parameter group, a third characteristic winding parameter group, and a fourth preset characteristic winding parameter group; Set the detection time interval of each feature winding parameter in the first feature winding parameter group as a first preset time interval, set the number of detection devices of each feature winding parameter as a fourth preset number, and generate a plurality of detection devices in combination with a detection device reference library; Set the detection time interval of each feature winding parameter in the second feature winding parameter group as a second preset time interval, set the number of detection devices of each feature winding parameter as a third preset number, and generate a plurality of detection devices in combination with a detection device reference library; Set the detection time interval of each feature winding parameter in the third feature winding parameter group as a third preset time interval, set the number of detection devices of each feature winding parameter as a second preset number, and generate a plurality of detection devices in combination with a detection device reference library; Set the detection time interval of each feature winding parameter in the fourth feature winding parameter group as a fourth preset time interval, set the number of detection devices of each feature winding parameter as a first preset number, and generate a plurality of detection devices in combination with a detection device reference library; Generate a plurality of detection time nodes according to the detection time interval of each feature winding parameter, and generate the detection parameter of each feature winding parameter in combination with the plurality of detection devices.

5. The automatic detection method of the reactor coil winding according to claim 4, characterized in that, Construct a winding coefficient sequence, including: Generate a winding coefficient sequence H (H1, H2,..., Hs) of the corresponding feature winding parameter sequence according to the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence, wherein hj is the winding coefficient corresponding to the jth real-time feature winding parameter; Calculate the winding coefficient difference value of each winding coefficient in the winding coefficient sequence and the previous adjacent winding coefficient, and calculate the sequence fluctuation coefficient; ； wherein D is a sequence fluctuation coefficient, is the winding coefficient difference value of the jth winding coefficient and the previous adjacent winding coefficient; Calculate the confidence coefficient of each winding coefficient; ； Wherein Kj is the confidence coefficient of the jth winding coefficient; If the confidence coefficient is less than a preset confidence coefficient threshold, modify the corresponding winding coefficient according to the adjacent winding coefficient, replace the winding coefficient with a confidence coefficient less than the preset confidence threshold according to the modified winding coefficient, and obtain a new winding coefficient sequence.

6. The automatic detection method of the reactor coil winding according to claim 5, wherein, Set the winding combination number according to the sequence fluctuation coefficient of each feature winding parameter sequence, including: Pre-set a first preset fluctuation coefficient interval, a second preset fluctuation coefficient interval, a third preset fluctuation coefficient interval, and a fourth preset fluctuation coefficient interval; When the sequence fluctuation coefficient is in the first preset fluctuation coefficient interval, set the winding combination number as a fourth preset combination number; When the sequence fluctuation coefficient is in the second preset fluctuation coefficient interval, set the winding combination number as a third preset combination number; When the sequence fluctuation coefficient is in the third preset fluctuation coefficient interval, set the winding combination number as a second preset combination number; When the sequence fluctuation coefficient is in the fourth preset fluctuation coefficient interval, set the winding combination number as a first preset combination number.

7. The automatic detection method of the reactor coil winding according to claim 6, wherein Determine whether to trigger a calibration instruction according to the winding trend factor and the winding coefficient, including: Calculate the average value of the winding coefficient of each winding coefficient group, set a correction coefficient according to the corresponding winding trend factor, and correct the average value of the winding coefficient to obtain a corrected winding coefficient; The correction winding coefficient corresponding to each feature winding parameter is compared with the corresponding preset winding coefficient threshold value, and if the correction winding coefficient is less than the preset winding coefficient threshold value, it is determined that the calibration instruction is triggered; If the correction winding coefficient is not less than the preset winding coefficient threshold value, it is determined that the calibration instruction is not triggered.

8. An automatic detection system for winding of a reactor coil, characterized in that, It comprises: The acquisition module is used for acquiring the historical similar winding log of the current reactor coil, calculating the influence coefficient of each historical winding parameter according to the historical similar winding log, and setting the feature winding parameter and the corresponding detection parameter according to the influence coefficient; The analysis module is used for collecting real-time feature winding parameters according to the detection parameters, constructing a plurality of feature winding parameter sequences according to the real-time feature winding parameters, and analyzing, calculating the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence according to the analysis result; The judgment module is used for constructing the winding coefficient sequence and dividing it into a plurality of winding coefficient groups, determining the winding trend factor according to the winding coefficient group, and determining whether the calibration instruction is triggered according to the winding trend factor and the winding coefficient; According to the analysis result, the winding coefficient of each real-time feature winding parameter in the feature winding parameter sequence is calculated, which comprises: The real-time feature winding parameters are obtained according to the detection parameters, and the feature winding parameter sequence M of the same parameter is constructed, M (m1, m2, …, ms), wherein mj is the real-time feature winding parameter at the jth detection time node; A plurality of standard winding parameter intervals of the current reactor coil are generated; Each feature winding parameter sequence is compared with the corresponding standard winding parameter interval to determine whether each real-time feature winding parameter in the feature winding parameter sequence is in the standard winding parameter interval; If not, the first parameter difference between the corresponding real-time feature winding parameter and the optimal critical winding parameter of the corresponding standard winding parameter interval is calculated, and the abnormal deviation coefficient of the corresponding real-time feature winding parameter is generated according to the first parameter difference; The winding coefficient of the abnormal deviation coefficient of the real-time feature winding parameter is generated based on the abnormal deviation coefficient-winding coefficient mapping table; If yes, the second parameter difference between the corresponding real-time feature winding parameter and the standard winding parameter median of the corresponding standard winding parameter interval is calculated, and the normal deviation coefficient of the corresponding real-time feature winding parameter is generated according to the second parameter difference; The winding coefficient of the normal deviation coefficient of the real-time feature winding parameter is generated based on the normal deviation coefficient-winding coefficient mapping table; The winding coefficient group is divided into a plurality of winding coefficient groups, and the winding trend factor is determined according to the winding coefficient group, which comprises: The number of winding coefficient groups is set according to the sequence fluctuation coefficient of each feature winding parameter sequence, and the adjacent winding coefficients in the corresponding winding coefficient sequence are combined to obtain a plurality of winding coefficient groups; The curve fitting is performed on the winding coefficients in each winding coefficient group, and the curve slope at each fitting point is determined; The mean value of the curve slope in the same winding coefficient group is calculated, and the winding trend factor of the corresponding winding coefficient group is generated in combination with the coefficient variance in the winding coefficient group.

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