A loom running state monitoring and motor group control method integrated in a cloud platform

Abstract

The application discloses a loom operation state monitoring and motor group control method integrated in a cloud platform, relates to the field of loom operation monitoring, and comprises the following steps: installing a multi-parameter sensor on a main shaft, a let-off mechanism, a take-up mechanism and a motor driving end of a loom, collecting rotation speed, torque, vibration frequency and motor current and voltage data, and uploading the collected data to a cloud platform in real time; the application combines time-sharing collection of the multi-parameter sensor with synchronous calibration, guarantees data accuracy, removes abnormal data, constructs a dynamic characteristic matrix, accurately extracts loom operation and motor load characteristics, and relies on an evaluation model to evaluate a state in real time and accurately prewarn faults and associated motors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of loom operation monitoring, in particular to a loom operation state monitoring and motor group control method integrated in a cloud platform. BACKGROUND

[0002] A loom is a mechanical device for weaving cloth, which can interweave warp and weft into fabric. It is a core device in the textile industry and has promoted the development of clothing, home textiles and other fields.

[0003] In the invention patent application with the application number 202411339618.0, a monitoring system based on the operation state of a loom is disclosed, which includes a loom operation data monitoring and collecting module, a data automatic processing module, and a fault identification model establishment module. The output end of the loom operation data monitoring and collecting module is connected to the input end of the data automatic processing module. The output ends of the data automatic processing module and the fault identification model establishment module are both connected to the input end of the data detection module. The output end of the data detection module is connected to the input end of the fault early warning module. The output end of the fault early warning module is connected to the input end of the data receiving terminal. The output end of the data receiving terminal is connected to the input end of the data analysis module. The output end of the data analysis module is connected to the input end of the data feedback module. The data receiving terminal is one or more of a notebook computer or a mobile phone. The application aims to solve the problem of "the existing monitoring system can only detect abnormalities and eliminate these abnormal data, but does not trace and supplement the data, which may cause data inconsistency. At the same time, the system cannot provide a solution to the monitoring problem, cannot quickly and substantially solve the problem, and the overall system application effect is not good."

[0004] However, the control of the operation state parameters of the loom or the control of the loom group in the prior art is often single, and it is difficult to achieve good and stable group control of the loom.

[0005] Therefore, we propose a loom operation state monitoring and motor group control method integrated in a cloud platform. SUMMARY

[0006] In view of the above shortcomings of the prior art, the present application provides a loom operation state monitoring and motor group control method integrated in a cloud platform, which can effectively solve the problems of the prior art.

[0007] To achieve the above purpose, the present application realizes the following technical solutions;

[0008] The present application discloses a loom operation state monitoring and motor group control method integrated in a cloud platform, which comprises:

[0009] The multi-parameter sensors are installed on the main shaft, the let-off mechanism, the take-up mechanism and the motor driving end of the loom, the rotation speed, torque, vibration frequency and motor current and voltage data are collected, and the collected data are uploaded to the cloud platform in real time; the cloud platform receives the collected data, removes the abnormal data in the original data according to the fluctuation of the loom operation data, synchronously constructs a loom operation state feature matrix to extract the characteristic parameters reflecting the operation stability and the motor load, and the dimension of the feature matrix is adjusted in real time according to the type of the loom and the number of motors; the cloud platform calls the preset loom operation state evaluation model, compares the characteristic parameters with the standard state threshold in the evaluation model to generate real-time evaluation results, and when any characteristic parameter does not meet the corresponding standard state threshold, the fault warning mechanism is triggered to determine the fault type and the associated motor, and the warning information is pushed to the management personnel terminal; according to the operation state evaluation results and the current production task requirements, the cloud platform sets a group control strategy to maximize the loom production efficiency and minimize the motor energy consumption, combines the loom fabric weaving density, the unit time output target and the motor load balancing demand, sets a group control strategy, and generates the group control strategy of the rotation speed, start-stop sequence, load distribution and cooperative operation parameters of the associated motor; the cloud platform converts the group control strategy into control instructions, which are sent to each loom motor controller through the preset communication link, and the motor controller adjusts the operation parameters in real time after receiving the instructions, and feeds back the execution feedback information to the cloud platform; the cloud platform establishes a loom single and cluster operation state trace file based on the loom historical operation data and the motor control instruction execution record, and pushes it to the management personnel terminal.

[0010] Further, when the multi-parameter sensors collect data, a combination of time-sharing collection and synchronous calibration is adopted:

[0011] The time-sharing collection divides the collection period according to the rotation period of the loom main shaft, and the parameters of the let-off mechanism, the take-up mechanism and the motor driving end are collected synchronously in each collection period;

[0012] The synchronous calibration unifies the time stamps of the data collected by each sensor to the time reference of the cloud platform through a preset time synchronization protocol, and checks the collection accuracy of the sensors every preset calibration period. If the checking result exceeds the preset accuracy error interval, a preset sensor parameter correction process is triggered.

[0013] Further, when the cloud platform removes the abnormal data in the original data, it is subject to:

[0014] The running safety interval of each type of parameter is set according to the historical normal operation data of the loom, whether the collected data is in the corresponding running safety interval is identified in real time, and if it is not in the corresponding running safety interval, it is marked as preliminary abnormal data;

[0015] The preliminary abnormal data is subjected to inter-parameter correlation verification, a correlation coefficient of the parameter corresponding to the preliminary abnormal data and other associated parameters is calculated, if the correlation coefficient is lower than a preset correlation coefficient threshold, it is determined as real abnormal data and eliminated, if the correlation coefficient is higher than or equal to the preset correlation coefficient threshold, it is determined as false abnormal data and retained;

[0016] The correlation coefficient calculation formula of the parameter corresponding to the preliminary abnormal data and other associated parameters is:

[0017] ;

[0018] In the formula: is the correlation coefficient of the parameter X corresponding to the preliminary abnormal data and the associated parameter Y; is the number of collection times in the preset time window; 、 is the value of the abnormal parameter at the a-th collection time, and the value of the associated parameter at the a-th collection time; 、 is the average value of the abnormal parameter in the preset time window, and the average value of the associated parameter in the preset time window.

[0019] Further, when the loom running state feature matrix is constructed, it is subjected to:

[0020] The first layer is a basic parameter layer, and the collected rotation speed, torque, vibration frequency and motor current and voltage data are taken as basic feature vectors;

[0021] The second layer is a derived feature layer, and the loom running stability coefficient and the motor load coefficient are calculated based on the basic feature vectors:

[0022] ;

[0023] In the formula: is the loom running stability coefficient; is the number of collection times of the rotation speed data in the collection period; is the rotation speed data collected at the i-th collection; is the average value of the rotation speed data in the collection period; is a preset stability adjustment coefficient; is the motor load coefficient; is the number of collection times of the current and voltage data in the collection period; is the motor current data collected at the j-th collection; is the motor voltage data collected at the j-th collection; is the motor power factor; is the rated power of the motor;

[0024] The third layer is a fusion feature layer, which fuses the basic feature vector with the stability coefficient and the load coefficient of the derived feature layer to form a loom running state feature matrix, and the dimension of the feature matrix is increased or decreased according to the parameter difference of the loom type and the number of motors.

[0025] Further, the loom running state evaluation model is:

[0026] weight coefficients are set for each feature parameter , wi represents the weight coefficient of the i th feature parameter, wi represents the safety weight distribution coefficient and the efficiency weight distribution coefficient, wi represents the i th feature parameter influence priority standardization value on loom running safety, wi represents the i th feature parameter contribution degree standardization value on production efficiency;

[0027] The normalized evaluation value of each feature parameter is calculated , wi represents the normalized evaluation value of the i th feature parameter, wi represents the real-time acquisition value of the i th feature parameter, wi represents the upper and lower threshold values of the i th feature parameter in the standard state;

[0028] The loom running state comprehensive evaluation value is , wi represents the total amount of feature parameters, if The loom running state is determined to be abnormal if the comprehensive evaluation value is lower than the preset comprehensive evaluation threshold value, triggering the fault early warning mechanism.

[0029] Further, when determining the fault type and the associated motor based on the fault early warning mechanism, the following is followed:

[0030] A fault feature database is established, which stores the feature parameter abnormal set and the parameter abnormal threshold interval corresponding to each fault type;

[0031] The feature parameter abnormal set is a set of associated parameters that are abnormal when the corresponding fault type occurs, and the parameter abnormal threshold interval is the value interval of each abnormal parameter deviating from the standard threshold value under the corresponding fault type;

[0032] The abnormal parameter set obtained by real-time monitoring and the actual deviation value of each abnormal parameter are matched and analyzed with the feature parameter abnormal set and the parameter abnormal threshold interval of each fault in the fault feature database to determine the fault type with the highest matching degree as the preliminary determination result of the fault type;

[0033] Based on the fault type in the preliminary determination result, the correlation degree of the fault type and each motor is calculated , represents the correlation degree of the ith fault and the jth motor, represents the total number of continuous collection time points before the fault warning trigger, represents the characteristic abnormal value of the ith fault at the tth collection time point, which is the difference between the abnormal parameter at the corresponding time and the standard threshold value, represents the running parameter value of the jth motor at the tth collection time point;

[0034] If the correlation degree of a motor is higher than the preset correlation degree threshold value, the motor is determined as the associated motor of the fault type; if the correlation degrees of non-unique motors are all higher than the preset correlation degree threshold value, the main associated motor and the secondary associated motor are determined in the order of the correlation degree value from large to small.

[0035] Further, the group control strategy is subject to the following conditions when being set:

[0036] To maximize the production efficiency of the loom and minimize the motor energy consumption, an optimization objective function is constructed:

[0037] ;

[0038] In the formula: , is the production efficiency objective function and the energy consumption objective function; is the yield weight coefficient; is the real-time unit time yield of the loom; is the unit time yield target; is the normal operation time of the loom; is the total operation time; is the real-time energy consumption weight coefficient; is the total number of motors; is the real-time power of the kth motor; is the operation time of the kth motor; is the power fluctuation value of the kth motor;

[0039] An initial preset number of group control strategy parameter combinations containing motor speed adjustment coefficients, torque distribution ratios, and start-stop timing parameters are generated, the production efficiency and energy consumption target values corresponding to each parameter combination are calculated, and the logic is determined as follows: if a parameter combination is not lower than another combination in production efficiency and not higher than the combination in energy consumption, it is determined as a more optimal combination, the current more optimal parameter combination is selected to form an initial optimal set;

[0040] In the initial optimal set, the parameter combination with the highest comprehensive closeness degree of the production efficiency target value and the energy consumption target value is selected as the group control strategy parameter.​​

[0041] Further, the preset communication link adopts a combination of encrypted transmission and data fragmentation transmission when performing the transmission task.

[0042] The encrypted transmission performs encryption processing on the control instruction through a preset asymmetric encryption algorithm to generate an encrypted instruction data packet.

[0043] The data fragmentation transmission splits the encrypted instruction data packet into a plurality of data fragments according to a preset fragmentation size, adds a check code and a fragmentation sequence number to each data fragment, and transmits the data fragments in parallel to the motor controller through different communication sub-links.

[0044] After receiving the data fragments, the motor controller reassembles the data packet according to the fragmentation sequence number, verifies the integrity of the data packet through the check code, decrypts the data packet to obtain the control instruction if the verification is passed, sends a retransmission request to the cloud platform if the verification fails, and the cloud platform re-sends the corresponding data fragments according to the retransmission request.

[0045] Further, when establishing the loom single machine and cluster operation state trace archives, data is stored through a hierarchical storage architecture.

[0046] Single loom archive layer: stores historical operation data, motor control instruction execution records, fault warning records, and fault disposal records of each loom, indexes according to time dimension, and supports data retrieval according to loom number, time interval, and fault type.

[0047] Cluster operation archive layer: based on the historical operation data of all looms in the single loom archive layer, the following calculations are performed on the operation parameters of the single loom according to a predetermined statistical period to generate cluster characteristic indexes.

[0048] The unit time output, energy consumption data, and fault occurrence number of each loom are respectively statistically calculated to obtain the unit time output value, total energy consumption value, and single machine fault number of the single loom.

[0049] The unit time output values of all looms are accumulated to obtain the total cluster unit time output, and the total cluster unit time output is compared with the theoretical maximum total output of the cluster within the predetermined statistical period to obtain the cluster operation efficiency.

[0050] The total energy consumption values of all looms are accumulated to obtain the total cluster energy consumption, and the total cluster energy consumption is compared with the total cluster operation time within the predetermined statistical period to obtain the average energy consumption.

[0051] The single machine fault numbers of all looms are accumulated to obtain the total cluster fault number, and the total cluster fault number is compared with the total cluster operation time within the predetermined statistical period to obtain the fault occurrence rate.

[0052] All intermediate data and final calculation results of the above calculation are associated with the original data identifier of the corresponding single loom;

[0053] The archive update layer: real-time monitoring of loom running data and control instruction execution state, when the data meet the preset update condition, automatically update the data of single loom archive layer and cluster running archive layer, and archive the historical data;

[0054] The preset update condition includes: data volume reaches the preset threshold, time interval reaches the preset period.

[0055] Compared with the known prior art, the technical scheme provided by the application has the following beneficial effects:

[0056] The application provides a loom running state monitoring and motor group control method integrated in a cloud platform, which, in the execution process, combines time-sharing collection of multiple parameter sensors with synchronous calibration to ensure data accuracy, and accurately extracts loom running and motor load characteristics through abnormal data elimination and dynamic characteristic matrix construction, relies on an evaluation model to evaluate the state in real time and accurately warn faults and associated motors;

[0057] The group control strategy is set to maximize production efficiency and minimize energy consumption, the motor running parameters are optimized, the encrypted and fragmented transmission is combined to ensure safe and efficient transmission of control instructions, a hierarchical traceability archive is established to realize traceability of running data, and the method can adapt to multiple types of looms and multiple motor collaborative management, effectively improves the loom running stability and production efficiency, reduces motor energy consumption, reduces the impact of faults, and provides reliable support for efficient operation and maintenance of loom clusters. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical scheme in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0059] Figure 1 It is a flowchart of a loom running state monitoring and motor group control method integrated in a cloud platform. DETAILED DESCRIPTION

[0060] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0061] The present application will be further described below in conjunction with the embodiments.

[0062] Embodiment:

[0063] The loom running state monitoring and motor group control method integrated in the cloud platform of the present embodiment, as shown in Figure 1 , comprises:

[0064] A plurality of parameter sensors are installed at the loom main shaft, the warp let-off mechanism, the take-up mechanism and the motor driving end to collect the data of rotation speed, torque, vibration frequency and motor current and voltage, and the collected data is uploaded to the cloud platform in real time;

[0065] When the plurality of parameter sensors collect data, a time-sharing collection and synchronous calibration combined mode is adopted:

[0066] The time-sharing collection divides the collection period according to the rotation period of the loom main shaft, and the parameters of the warp let-off mechanism, the take-up mechanism and the motor driving end are synchronously collected in each collection period;

[0067] The synchronous calibration unifies the time stamp of the data collected by each sensor to the time reference of the cloud platform through a preset time synchronization protocol, and the collection accuracy of the sensor is checked every preset calibration period. If the checking result exceeds the preset accuracy error interval, a preset sensor parameter correction process is triggered:

[0068] The preset sensor parameter correction process comprises: calling a preset parameter compensation algorithm, calculating a corresponding compensation coefficient according to the accuracy error value; writing the compensation coefficient into the parameter configuration module of the sensor to correct the collection deviation; synchronously recording the correction time, the compensation coefficient and the accuracy checking values before and after the correction to the sensor calibration file of the cloud platform;

[0069] The cloud platform receives the collected data, removes the abnormal data in the original data according to the fluctuation of the loom running data, synchronously constructs a loom running state feature matrix to extract the feature parameters reflecting the running stability and the motor load, and the dimension of the feature matrix is adjusted in real time according to the loom type and the number of motors;

[0070] When the cloud platform removes the abnormal data in the original data, it is subject to:

[0071] The running safety interval of each type of parameter is set according to the loom historical normal operation data, and whether the collected data is in the corresponding running safety interval is identified in real time, if not, it is marked as preliminary abnormal data;

[0072] The correlation between the parameters of the preliminary abnormal data is verified, the correlation coefficient of the corresponding parameters of the preliminary abnormal data and other related parameters is calculated, if the correlation coefficient is lower than the preset correlation coefficient threshold, it is determined as real abnormal data and eliminated, if the correlation coefficient is higher than or equal to the preset correlation coefficient threshold, it is determined as false abnormal data and retained;

[0073] The correlation coefficient calculation formula of the corresponding parameters of the preliminary abnormal data and other related parameters is:

[0074] ;

[0075] In the formula: is the correlation coefficient of the corresponding parameters X and the related parameters Y of the preliminary abnormal data; is the number of collection in the preset time window; 、 is the value of the abnormal parameter at the a-th collection time, and the value of the related parameter at the a-th collection time; 、 is the average value of the abnormal parameter in the preset time window, and the average value of the related parameter in the preset time window;

[0076] The above formula introduces the number of collection in the preset time window, and the values of the corresponding parameters of the preliminary abnormal data and the related parameters at each collection time are respectively subtracted from the average values in the time window and multiplied and summed, and then divided by the square root product of the square of the deviation of each parameter, so as to quantify the linear correlation degree of the two, effectively distinguish real abnormality from false abnormality, avoid misjudgment of abnormal data due to accidental fluctuation, ensure the accuracy of data elimination, and provide reliable data basis for subsequent state evaluation;

[0077] Wherein, the loom historical normal operation data is always the loom historical normal operation data in the preset time interval before the current timestamp, the running safety interval of each type of parameter is iteratively updated in real time based on the loom historical normal operation data, each running safety interval is composed of the maximum value and the minimum value of the corresponding data type in the loom historical normal operation data, and the related parameters of the corresponding parameters of the preliminary abnormal data are determined based on the preset basic correlation atlas;

[0078] When the loom running state feature matrix is constructed, it is subject to:

[0079] The first layer is the basic parameter layer, and the collected rotation speed, torque, vibration frequency and motor current voltage data are taken as basic feature vectors;

[0080] The second layer is a derived feature layer, and a loom running stability coefficient and a motor load coefficient are calculated based on the basic feature vector:

[0081] ;

[0082] In the formula: is the loom running stability coefficient; is the number of times of collecting the speed data in the collection period; is the speed data collected at the ith time of collection; is the average value of the speed data in the collection period; is a preset stability adjustment coefficient; is the motor load coefficient; is the number of times of collecting the current and voltage data in the collection period; is the motor current data collected at the jth time of collection; is the motor voltage data collected at the jth time of collection; is the motor power factor; is the rated power of the motor;

[0083] The above formula is based on the number of times of collecting the speed data in the collection period, and the sum of squares of the difference between each speed data and the average value of the speed in the collection period is calculated, and then multiplied by a preset stability adjustment coefficient, to obtain the loom running stability coefficient. The loom running stability coefficient can accurately reflect the running stability state of the loom under different fabric weaving scenarios, and provide a feature parameter that meets the actual needs for state evaluation;

[0084] And the number of times of collecting the current and voltage data in the collection period is used to multiply the motor current and voltage data collected each time, and then multiplied by the motor power factor to sum up, and finally divided by the product of the rated power of the motor and the number of times of collection, to obtain the motor load coefficient, which accurately measures the actual load of the motor, provides an important basis for load distribution in subsequent group control strategy formulation, and realizes accurate control of the motor load;

[0085] The third layer is a fusion feature layer, which fuses the basic feature vector and the stability coefficient and the load coefficient of the derived feature layer to form a loom running state feature matrix. The dimension of the feature matrix is increased or decreased according to the difference in the number of parameters corresponding to the type of loom and the increase or decrease in the number of features columns of the motor;

[0086] The dimension of the feature matrix increases or decreases the number of feature columns in the following way:

[0087] First, the type of loom is identified through the model identification information of the loom. Based on the preset loom type-parameter mapping table, the monitoring parameters specific to the type of loom are determined, such as the rapier head displacement parameter of the rapier loom, the air flow pressure parameter of the air jet loom, etc., and the specific parameters are taken as the basis for adding new feature columns;

[0088] Next, count the number of motors in the current loom cluster, and for each motor, determine its corresponding independent monitoring parameters as an independent feature column;

[0089] Furthermore, according to the preset dynamic mapping rules, the loom-specific parameters and the independent parameters of each motor are classified and integrated. Among them, parameters of the same type share the feature column prefix identifier, while parameters of different types correspond to independent feature columns.

[0090] Finally, through the parameter integrity verification mechanism, the redundancy of the integrated feature columns is checked. If there are feature columns corresponding to duplicate or invalid parameters, they are automatically removed. If there are missing necessary parameters, the corresponding feature columns are added, and finally a feature matrix dimension that is adapted to the type of loom and the number of motors is formed.

[0091] Among them, the preset stability adjustment coefficient The preset value range is [0.1, 0.8]. A smaller value is used when the loom is weaving fabrics requiring high operational stability (such as high-precision silk fabrics), and vice versa. This affects the motor power factor. The value ranges from [0, 1], and its value increases as the actual load rate of the motor increases, and decreases as the ratio decreases.

[0092] The cloud platform calls the preset loom operation status evaluation model, compares the feature parameters with the standard status thresholds in the evaluation model to generate real-time evaluation results. When any feature parameter does not match the corresponding standard status threshold, the fault warning mechanism is triggered to determine the fault type and associated motor and push the warning information to the management personnel terminal.

[0093] The loom operating status assessment model is as follows:

[0094] Set weight coefficients for each feature parameter , This represents the weight coefficient of the i-th feature parameter. This represents the weighting coefficients for security and efficiency. This represents the standardized value indicating the priority of the impact of the i-th feature parameter on the safety of loom operation. This represents the standardized value of the contribution of the i-th feature parameter to production efficiency;

[0095] The above formula determines the weight coefficient of each characteristic parameter by multiplying the safety weight allocation coefficient with the standardized value of the priority of the characteristic parameter's impact on the safety of loom operation, and adding the efficiency weight allocation coefficient with the standardized value of the characteristic parameter's contribution to production efficiency. This makes the subsequent comprehensive evaluation value more in line with actual operating needs and improves the rationality of the status assessment.

[0096] Calculate the standardized evaluation values ​​of each characteristic parameter. , normalized evaluation value of the i-th characteristic parameter, real-time acquisition value of the i-th characteristic parameter, upper and lower threshold values of the i-th characteristic parameter in the standard state;

[0097] the comprehensive evaluation value of the loom running state is , total number of characteristic parameters, if the comprehensive evaluation value is lower than the preset comprehensive evaluation threshold value, it is determined that the loom running state is abnormal, and the fault early warning mechanism is triggered;

[0098] wherein the safety weight distribution coefficient and the efficiency weight distribution coefficient are both greater than zero and their sum is 1, and are subject to: increasing with the increase of the safety requirement level of the loom running and decreasing with the decrease thereof; , both are in the range of 0~1, and the greater the threat to the safe running of the loom when the characteristic parameter is abnormal, the greater the value, and vice versa, the more significant the improvement of the production efficiency of the loom when the characteristic parameter is optimized, the greater the value, and vice versa;

[0099] When determining the fault type and the associated motor based on the fault early warning mechanism, it is subject to:

[0100] A fault feature database is established, and the fault feature database stores the abnormal parameter set and the parameter abnormal threshold interval corresponding to each fault type;

[0101] The abnormal parameter set is a set of associated parameters that are abnormal when the corresponding fault type occurs, and the parameter abnormal threshold interval is the value interval of each abnormal parameter deviating from the standard threshold value under the corresponding fault type;

[0102] The abnormal parameter set and the actual deviation value of each abnormal parameter obtained by real-time monitoring are matched and analyzed with the abnormal parameter set and the parameter abnormal threshold interval of each fault in the fault feature database to determine the fault type with the highest matching degree as the preliminary determination result of the fault type;

[0103] Based on the fault type in the preliminary determination result, the correlation degree of the fault type and each motor is calculated , denotes the correlation degree of the i-th fault and the j-th motor, denotes the total number of continuous acquisition time points before the fault early warning is triggered, denotes the characteristic abnormal value of the i-th fault at the t-th acquisition time, which is the difference between the abnormal parameter at the corresponding time and the standard threshold value, represents the running parameter value of the jth motor at the tth collection time point;

[0104] The above formula is based on the total number of continuous collection time points before the fault warning trigger. The sum of the product of the characteristic abnormal value of the ith type of fault at each collection time point and the running parameter value of the jth motor at the corresponding time point is calculated to obtain the correlation degree of the two, which can accurately locate the associated motor of the fault and can sort and determine the primary and secondary associated motors when multiple motors are associated, providing a clear direction for fault disposal.

[0105] If the correlation degree of a certain motor is higher than the preset correlation degree threshold, the motor is determined to be the associated motor of the fault type; if the correlation degrees of non-unique motors are all higher than the preset correlation degree threshold, the primary associated motor and the secondary associated motor are determined in order from large to small according to the correlation degree values.

[0106] Among them, before the correlation degree is obtained, the causal relationship network of the fault and the motor is constructed based on the mechanical transmission connection relationship between the loom main shaft, the warp let-off mechanism, the take-up mechanism and each motor (such as the main shaft driving the warp let-off motor and the take-up motor through the gear set to run synchronously, the warp let-off mechanism and the warp let-off motor being directly connected through the shaft coupling) and the electrical control association logic (such as the main shaft speed signal being used as the speed regulation reference signal of the warp let-off motor and the take-up motor, and the motor current feedback signal being used to control the load output of the corresponding mechanism), the network pre-stores the association rules of different mechanism faults being transmitted to the corresponding motor through mechanical transmission or electrical control path, the set of motors with association is filtered based on the causal relationship network, and the correlation degree of the fault type in the preliminary determination result with each motor in the set is calculated.

[0107] According to the running state evaluation result and the current production task demand, the cloud platform sets the group control strategy to maximize the loom production efficiency and minimize the motor energy consumption, combines the loom fabric weaving density, the unit time output target and the motor load balance demand, sets the group control strategy of the associated motor speed, start-stop timing, load distribution and cooperative running parameters;

[0108] The group control strategy is subject to:

[0109] The optimization objective function is constructed with the optimization objectives of maximizing the loom production efficiency and minimizing the motor energy consumption:

[0110] ;

[0111] In the formula: , is the production efficiency target function and the energy consumption target function; is the output weight coefficient; is the real-time unit time output of the loom; ​​is a unit time yield target; is a loom uptime; is a total uptime; is a real-time energy consumption weight coefficient; is a total number of motors; is a real-time power of the kth motor; is a running time of the kth motor; is a power fluctuation value of the kth motor;

[0112] The above formula constitutes a production efficiency target function by the product of the yield weight coefficient and, which comprehensively considers the actual yield compliance and equipment operation reliability, highlights the importance of the yield target through the weight coefficient, quantifies the production efficiency achievement, provides a clear production efficiency optimization direction for group control strategy optimization, and thus realizes production efficiency maximization;

[0113] The energy consumption target function comprehensively measures the motor energy consumption level through real-time power, running time and power fluctuation, emphasizes the importance of energy consumption control through the weight coefficient, provides a clear energy consumption optimization basis for group control strategy development, and minimizes motor energy consumption;

[0114] An initial preset number of group control strategy parameter combinations containing motor speed adjustment coefficients, torque distribution ratios and start-stop timing parameters are generated, the production efficiency and energy consumption target values corresponding to each parameter combination are calculated, and the logic of: if a parameter combination is not lower than another combination in production efficiency and not higher than the combination in energy consumption, then it is determined to be a better one, is used to select the current better parameter combination to form an initial optimal set;

[0115] In the initial optimal set, the parameter combination with the highest comprehensive closeness degree of the production efficiency target value and the energy consumption target value is selected as the group control strategy parameter;

[0116] wherein the yield weight coefficient ∈(0, 1), is greater when the current production task of the loom is urgent and the unit time yield target needs to be prioritized to be achieved, and is smaller when the unit time yield target of the loom is close to completion or needs to prioritize motor energy consumption control, the real-time energy consumption weight coefficient ∈(0, 1), is greater when the total motor energy consumption and power fluctuation need to be prioritized to be reduced, such as in the case of energy supply shortage or strict energy consumption evaluation index, and is smaller otherwise;

[0117] The comprehensive closeness degree normalizes and fuses the production efficiency compliance rate and the energy consumption reduction rate based on a preset weight to realize quantification;

[0118] The cloud platform converts the group control strategy into control instructions, which are sent to each loom motor controller through a preset communication link. The motor controller adjusts the operating parameters in real time after receiving the instructions and transmits the execution feedback information back to the cloud platform.

[0119] The preset communication link adopts a combination of encrypted transmission and data fragmentation transmission when performing transmission tasks.

[0120] The encrypted transmission uses a preset asymmetric encryption algorithm to encrypt the control instructions to generate encrypted instruction data packets.

[0121] The data fragmentation transmission splits the encrypted instruction data packets into multiple data fragments according to a preset fragmentation size, adds a check code and a fragment sequence number to each data fragment, and transmits the data fragments in parallel through different communication sub-links to the motor controller.

[0122] After receiving the data fragments, the motor controller reassembles the data packets according to the fragment sequence numbers, verifies the data packet integrity through the check code, decrypts the data packet to obtain the control instructions if the verification is successful, or sends a retransmission request to the cloud platform if the verification fails, and the cloud platform re-sends the corresponding data fragments according to the retransmission request.

[0123] The cloud platform establishes a loom single-machine and cluster operation state traceability archive based on loom historical operation data and motor control instruction execution records, and pushes it to the administrator terminal.

[0124] When establishing the loom single-machine and cluster operation state traceability archive, data storage is performed through a hierarchical storage architecture.

[0125] Single loom archive layer: stores the historical operation data, motor control instruction execution records, fault warning records, and fault handling records of each loom, indexes them by time dimension, and supports data retrieval by loom number, time interval, and fault type.

[0126] Cluster operation archive layer: based on the historical operation data of all looms in the single loom archive layer, the following calculations are performed on the operating parameters of single looms to generate cluster characteristic indicators according to a predetermined statistical period.

[0127] The unit time output, energy consumption data, and fault occurrence frequency of each loom are individually counted to obtain the unit time output value, total energy consumption value, and single loom fault frequency.

[0128] The unit time output values of all looms are accumulated to obtain the total cluster unit time output. The total cluster unit time output is compared with the theoretical maximum total output of the cluster within the predetermined statistical period to obtain the cluster operation efficiency.

[0129] The single total energy consumption values of all looms are accumulated to obtain the cluster total energy consumption, and the cluster total energy consumption is compared with the cluster total running time in the predetermined statistical period, and is recorded as the average energy consumption;

[0130] The single fault times of all looms are accumulated to obtain the cluster total fault times, and the cluster total fault times are compared with the cluster total running time in the predetermined statistical period, and are recorded as the fault occurrence rate;

[0131] All intermediate data and final calculation results calculated above are associated with the original data identifier of the corresponding single loom;

[0132] The archive updating layer: real-time monitoring of loom running data and control instruction execution state, when the data meet the preset updating condition, automatically update the data of single loom archive layer and cluster running archive layer, and archive the historical data;

[0133] Among them, the preset updating condition includes: the data amount reaches the preset threshold, the time interval reaches the preset period;

[0134] Among them, the cloud platform is a special cloud platform for loom running state monitoring and motor group control, which has the functions of data receiving, processing, running state evaluation, motor group control strategy generation, control instruction issuing, and running archive storage and calling, and can adapt to the collaborative management of multiple types of looms and multiple motors.

[0135] In the embodiment, the above method can accurately collect the running data of the key parts of the loom and the motor, ensure the reliability of the data through synchronous calibration and abnormal rejection, accurately evaluate the running state of the loom, timely alarm and locate the associated motor, and also can formulate motor group control strategy with the goal of high efficiency and low consumption, optimize the running parameters, at the same time, establish a complete running trace archive, help management, significantly improve the running stability of the loom, production efficiency, and reduce the motor energy consumption.

[0136] The above embodiment is an application example of one technical solution:

[0137] A textile mill deploys a cluster of 3 air-jet looms, and uses the loom monitoring and motor group control method integrated in the cloud platform to realize intelligent management, the specific application process is as follows:

[0138] A multi-parameter sensor is installed on the main shaft, the warp let-off mechanism, the take-up mechanism, and the motor drive end of each loom to collect data on rotation speed, torque, vibration frequency, motor current and voltage, and airflow pressure specific to the air-jet loom. The collection period is divided according to the rotation cycle of the loom main shaft (0.5 seconds per cycle), and the parameters of the warp let-off mechanism, the take-up mechanism, and the motor drive end are collected synchronously in each period. The time stamps of the data from each sensor are unified to the time reference of the cloud platform, and the collection accuracy is checked every 2 hours. In a certain check, the error of the motor current sensor of No. 1 loom exceeds the preset interval, and the system immediately calls the parameter compensation algorithm to calculate the compensation coefficient 0.03, which is written into the sensor configuration module to correct the deviation, and the correction time, coefficient, and accuracy values before and after correction are recorded to the sensor calibration file of the cloud platform.

[0139] After the cloud platform receives the data, it first sets the safe interval for each parameter based on the historical normal operation data of the looms in the previous 7 days, such as the rotation speed safe interval of 1200-1300 r / min. When the rotation speed of No. 2 loom is monitored to be 1150 r / min, it is marked as preliminary abnormal data. Further calculation of the correlation coefficient between this rotation speed and the associated parameters results in a value of 0.2 (lower than the preset threshold of 0.5), which is determined as true abnormal data and is excluded. If the correlation coefficient is 0.6 (higher than the threshold), it is determined as false abnormal data and is retained.

[0140] Subsequently, a loom operation state feature matrix is constructed: the basic parameter layer includes the collected rotation speed (average 1250 r / min), torque (average 8 N·m), vibration frequency (average 0.3 mm / s), current (average 5 A), voltage (380 V), and airflow pressure (average 0.6 MPa); the derived feature layer calculates the stability coefficient and the load coefficient - the stability coefficient is based on 20 times of rotation speed data collection, the deviation of each rotation speed from the average value is squared and multiplied by 0.2 (since high-precision cotton cloth is woven, high stability is required, and the adjustment coefficient is taken as a small value), and finally 0.75 is obtained; the load coefficient is based on 20 times of current and voltage data, each data is multiplied by the power factor 0.85 and then summed, and then divided by the rated power of the motor 7.5 kW, and finally 0.6 is obtained; the fusion feature layer fuses the basic parameters and the two coefficients, and since each motor of the three looms has independent monitoring parameters, three columns of motor independent parameter columns are added to the feature matrix, and the repeated vibration parameter column is excluded to form a feature matrix suitable for the current cluster.

[0141] The cloud platform calls the running state evaluation model, sets the security weight 0.6 and the efficiency weight 0.4 (the factory prioritizes security), allocates weight coefficients to each feature parameter, such as the priority of the safety influence of the rotating speed 0.8 and the efficiency contribution 0.7, and the weight coefficient is 0.76; and calculates the normalized evaluation value of each parameter, such as the rotating speed real-time 1230 r / min (the upper and lower limits of the standard are 1200-1300 r / min), and the normalized evaluation value is 0.8. The final comprehensive evaluation value of the eight feature parameters is 0.72, which is lower than the preset threshold 0.8, triggering the fault warning.

[0142] When the fault warning occurs, the system compares the fault feature database: the abnormal set corresponding to the "warping motor load too high" fault is "warping torque exceeds 8.5 N·m, motor current exceeds 5.5 A", and the real-time monitoring of the warp torque of No. 2 loom is 8.8 N·m and the current is 5.6 A, which has the highest matching degree with the fault, and is preliminarily determined as this fault. Based on the mechanical transmission relationship, a causal relationship network is constructed, and the associated motor set is selected; further calculation of the correlation degree is based on the 30 times of continuous data collected before the fault warning, combined with the fault feature abnormal value and the real-time running parameters of the motor, and the correlation degree is calculated as 0.85, and the No. 2 warp motor is determined as the fault associated motor.

[0143] The cloud platform constructs an optimization function with the goal of maximizing production efficiency (the current order is urgent, and the production weight is taken as 0.7) and minimizing motor energy consumption (the energy consumption weight is taken as 0.3), and initializes 10 groups of group control parameter combinations. Calculate the target value of each group, such as the production efficiency target function value of a certain group of parameters is 0.98, and the energy consumption target function value is 0.92. After selecting 3 groups of better parameter combinations, calculate the comprehensive closeness, and one of the closeness is 0.95, which is the highest, and is determined as the group control strategy: the rotating speed of the No. 2 warp motor is adjusted to 1260 r / min, the torque distribution is 0.9, and the start-stop timing is synchronized with other motors.

[0144] The cloud platform encrypts the group control instruction through an asymmetric encryption algorithm, splits it into 3 data fragments with check codes and serial numbers, and transmits it to each motor controller through 3 communication sub-links in parallel. The No. 2 motor controller receives and reorganizes according to the serial number, decrypts and executes after passing the verification, and returns the feedback information of "parameter adjusted" to the cloud platform; if the verification fails, a retransmission request is sent, and the cloud platform re-sends the corresponding fragment.

[0145] Finally, the cloud platform establishes a running state trace archive: a single archive layer stores the historical data, control instructions, and fault records of each loom according to time indexing, supporting retrieval by loom number, time, and fault type; the cluster archive layer is statistically summarized by day, with the single loom production of each of the three looms being 2000m, 1900m, and 2100m, respectively, the total cluster production being 6000m, and the running efficiency being 92%; the total energy consumption is 320kW・h, 330kW・h, and 310kW・h, respectively, with an average energy consumption of 40kW・h; the total number of faults is 2, and the fault occurrence rate is 8.3%; the archive update layer automatically updates data and archives every 12 hours and synchronously pushes to the administrator terminal.

[0146] In summary, the method in the above embodiments, in the execution process, combines time-sharing collection by multi-parameter sensors with synchronous calibration to ensure data accuracy, eliminates abnormal data and constructs a dynamic feature matrix to accurately extract loom running and motor load features, relies on an evaluation model to evaluate the state in real time and accurately warn of faults and associated motors, sets a group control strategy with the goal of maximizing production efficiency and minimizing energy consumption, optimizes motor operating parameters, combines encrypted and fragmented transmission to ensure safe and efficient transmission of control instructions, and establishes a hierarchical trace archive to achieve traceability of running data, which can adapt to multiple types of looms and multi-motor collaborative management, effectively improve loom running stability and production efficiency, reduce motor energy consumption, reduce the impact of faults, and provide reliable support for efficient operation and maintenance of loom clusters.

[0147] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A loom running state monitoring and motor group control method integrated in a cloud platform, characterized in that, The application relates to a weaving machine group control method and system. The application comprises: A plurality of parameter sensors are installed on a main shaft, a let-off mechanism, a take-up mechanism and a motor driving end of a weaving machine to collect rotation speed, torque, vibration frequency and motor current and voltage data, and the collected data is uploaded to a cloud platform in real time; The cloud platform receives the collected data, removes abnormal data in the original data according to weaving machine operation data fluctuation, synchronously constructs a weaving machine operation state feature matrix to extract characteristic parameters reflecting operation stability and motor load, and adjusts the feature matrix dimension in real time according to the weaving machine type and the number of motors; The cloud platform calls a preset weaving machine operation state evaluation model, compares the characteristic parameters with standard state thresholds in the evaluation model to generate real-time evaluation results, triggers a fault early warning mechanism to determine the fault type and the associated motor when any characteristic parameter does not match the corresponding standard state threshold, and pushes the early warning information to a management personnel terminal; According to the operation state evaluation results and the current production task demand, the cloud platform sets a group control strategy to maximize the weaving machine production efficiency and minimize the motor energy consumption, combines the weaving machine fabric weaving density, the unit time output target and the motor load balance demand, sets the group control strategy, and generates the group control strategy of the rotation speed, the start-stop time sequence, the load distribution and the cooperative operation parameters of the associated motor; The group control strategy is subject to the following conditions when being set: ; wherein: , is a production efficiency objective function, an energy consumption objective function; is a production weight coefficient; is a real-time unit time production of the loom; is a unit time production target; is a normal operation time of the loom; is a total operation time; is a real-time energy consumption weight coefficient; is a total number of motors; is a real-time power of the kth motor; is an operation time of the kth motor; is a power fluctuation value of the kth motor; An optimization objective function is constructed with the optimization objectives of maximizing the weaving machine production efficiency and minimizing the motor energy consumption: An initial optimal set is formed by initializing a preset number of group control strategy parameter combinations containing motor rotation speed adjustment coefficients, torque distribution ratios and start-stop time sequence parameters, calculating the production efficiency and energy consumption target values corresponding to each parameter combination, and selecting the current optimal parameter combination according to the logic that if a parameter combination is not lower than another combination in production efficiency and not higher than the combination in energy consumption, the parameter combination is determined to be more optimal; In the initial optimal set, the parameter combination with the highest comprehensive closeness degree is selected as the group control strategy parameter by calculating the comprehensive closeness degree of each parameter combination to the production efficiency target value and the energy consumption target value; The cloud platform converts the group control strategy into control instructions, which are sent to each weaving machine motor controller through a preset communication link, the motor controller adjusts the operation parameters in real time after receiving the instructions, and the execution feedback information is fed back to the cloud platform; 2. The loom running state monitoring and motor group control method integrated with the cloud platform according to claim 1, characterized in that, The cloud platform establishes a weaving machine single machine and cluster operation state trace file based on the historical operation data of the weaving machine and the motor control instruction execution record, and pushes the file to the management personnel terminal. When the plurality of parameter sensors collect data, a time-sharing collection and synchronous calibration combined mode is adopted: Time-sharing collection divides the collection period according to the rotation period of the main shaft of the weaving machine, and the parameters of the let-off mechanism, the take-up mechanism and the motor driving end are synchronously collected in each collection period; Synchronous calibration unifies the time stamps of the data collected by the sensors to the time reference of the cloud platform through a preset time synchronization protocol, and the collection accuracy of the sensors is checked every preset calibration period, and if the checking result exceeds the preset accuracy error interval, a preset sensor parameter correction process is triggered; 3. The loom running state monitoring and motor group control method integrated with the cloud platform according to claim 1, characterized in that, Each collection period corresponds to a collection period. According to the loom historical normal operation data, the running safety interval of each type parameter is set, and whether the collected data is in the corresponding running safety interval is identified in real time. If not, it is marked as preliminary abnormal data; The preliminary abnormal data is verified for parameter correlation, the correlation coefficient of the preliminary abnormal data corresponding parameter and other related parameters is calculated, if the correlation coefficient is lower than the preset correlation coefficient threshold, it is determined as true abnormal data and eliminated, if the correlation coefficient is higher than or equal to the preset correlation coefficient threshold, it is determined as false abnormal data and retained; The correlation coefficient calculation formula of the preliminary abnormal data corresponding parameter and other related parameters is: ; wherein: is a correlation coefficient of the parameter X corresponding to the preliminary abnormal data and the associated parameter Y; is the number of collection times within the preset time window; , is a value of the abnormal parameter at the a-th collection time, and a value of the associated parameter at the a-th collection time; , is an average value of the abnormal parameter within the preset time window, and an average value of the associated parameter within the preset time window.

4. The loom running state monitoring and motor group control method integrated with the cloud platform according to claim 2, characterized in that, When the loom running state feature matrix is constructed, it is subject to: The first layer is the basic parameter layer, the collected speed, torque, vibration frequency and motor current and voltage data are taken as the basic feature vector; The second layer is the derived feature layer, the loom running stability coefficient and motor load coefficient are calculated based on the basic feature vector: ; wherein: is a loom running stability coefficient; is the number of times of collecting the rotation speed data in the collection period; is the rotation speed data collected at the i-th time of collection; is the average value of the rotation speed data in the collection period; is a preset stability adjustment coefficient; is a motor load coefficient; is the number of times of collecting the current and voltage data in the collection period; is the motor current data collected at the j-th time of collection; is the motor voltage data collected at the j-th time of collection; is the motor power factor; is the motor rated power; The third layer is the fusion feature layer, the basic feature vector and the stability coefficient and load coefficient of the derived feature layer are fused to form the loom running state feature matrix, and the dimension of the feature matrix is increased or decreased according to the parameter number difference and the motor number change of the corresponding loom type.

5. The loom running state monitoring and motor group control method integrated with the cloud platform according to claim 1, characterized in that, The loom running state evaluation model is: The weight coefficient of each feature parameter is set ， a weight coefficient of the i-th characteristic parameter, a safety weight distribution coefficient, an efficiency weight distribution coefficient, a standardized value of the priority of the i-th characteristic parameter on the loom operation safety, a standardized value of the contribution degree of the i-th characteristic parameter on the production efficiency; The standardized evaluation value of each feature parameter is calculated , represents the normalized evaluation value of the i-th characteristic parameter, represents the real-time acquisition value of the i-th characteristic parameter, represents the upper and lower threshold values of the i-th characteristic parameter in the standard state. The loom running state comprehensive evaluation value is ， represents the total amount of characteristic parameters, if if the overall evaluation is below a preset threshold, it is determined that the loom operating state is abnormal, triggering a fault warning mechanism.

6. The loom running state monitoring and motor group control method integrated with the cloud platform according to claim 5, characterized in that, When determining the fault type and related motor based on the fault early warning mechanism, it is subject to: A fault feature database is established, which stores the feature parameter abnormal set and parameter abnormal threshold interval corresponding to each fault type; The feature parameter abnormal set is a group of associated parameters that are abnormal when the corresponding fault type occurs, and the parameter abnormal threshold interval is the value interval of each abnormal parameter deviating from the standard threshold under the corresponding fault type; The abnormal parameter set obtained by real-time monitoring and the actual deviation value of each abnormal parameter are matched and analyzed with the feature parameter abnormal set and parameter abnormal threshold interval of each fault in the fault feature database to determine the fault type with the highest matching degree as the preliminary determination result of the fault type; Based on the fault type in the preliminary determination result, the correlation degree of each motor with the fault type is calculated ， represents the correlation degree of the ith fault and the jth motor, represents the total number of continuous collection time points before the fault warning trigger, represents the feature anomaly value of the ith fault at the tth collection time point, which is the difference between the corresponding time point anomaly parameter and the standard threshold value, represents the running parameter value of the jth motor at the tth collection time point; If the correlation of a certain motor If the correlation degree is higher than the preset correlation degree threshold, the motor is determined to be an associated motor of the fault type; if there is a correlation degree of a non-unique motor, the motor is considered an associated motor of the fault type. If all values ​​are higher than the preset correlation threshold, then the primary and secondary associated motors are determined in descending order of correlation values.

7. The loom running state monitoring and motor group control method integrated with the cloud platform according to claim 1, characterized in that, When performing the transmission task, the preset communication link adopts the combination of encrypted transmission and data fragmentation transmission; The encrypted transmission encrypts the control instruction by a preset asymmetric encryption algorithm to generate an encrypted instruction data packet; The data fragmentation transmission splits the encrypted instruction data packet into multiple data fragments according to a preset fragmentation size, adds a check code and a fragment sequence number to each data fragment, and transmits each data fragment in parallel to the motor controller through different communication sub-links; After receiving the data fragments, the motor controller reassembles the data packet according to the fragment sequence number, verifies the data packet integrity through the check code, decrypts the data packet to obtain the control instruction if the verification is passed, or sends a retransmission request to the cloud platform if the verification fails, and the cloud platform retransmits the corresponding data fragments according to the retransmission request.

8. The loom running state monitoring and motor group control method integrated with the cloud platform according to claim 1, characterized in that, When the loom single machine and cluster running state trace archives are established, data storage is performed through a hierarchical storage architecture: Single loom archive layer: stores historical running data, motor control instruction execution records, fault warning records, and fault disposal records of each loom, indexes are established according to time dimension, and data retrieval is supported according to loom number, time interval, and fault type; Cluster running archive layer: based on the historical running data of all looms in the single loom archive layer, the following calculations are performed on the running parameters of single looms according to the predetermined statistical period to generate cluster characteristic indexes: Single statistics are performed on the unit time output, energy consumption data, and fault occurrence frequency of each loom to obtain the unit time output value, single total energy consumption value, and single fault frequency of single looms; The unit time output values of all looms are accumulated to obtain the cluster unit time total output, and the cluster unit time total output is compared with the cluster theoretical maximum total output in the predetermined statistical period to obtain the cluster running efficiency; The single total energy consumption values of all looms are accumulated to obtain the cluster total energy consumption, and the cluster total energy consumption is compared with the cluster total running time in the predetermined statistical period to obtain the average energy consumption; The single fault frequencies of all looms are accumulated to obtain the cluster total fault frequency, and the cluster total fault frequency is compared with the cluster total running time in the predetermined statistical period to obtain the fault occurrence rate; All intermediate data and final calculation results of the above calculations are associated with the original data identifiers of the corresponding single looms; Archive update layer: real-time monitoring of loom running data and control instruction execution state, when the data meet the preset update conditions, automatically update the data of single loom archive layer and cluster running archive layer, and archive storage of historical data; The preset update conditions include: data volume reaching the preset threshold, time interval reaching the preset period.

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