Loom running state monitoring and motor group control method integrated on cloud platform

Abstract

The invention discloses a loom running state monitoring and motor group control method integrated on a cloud platform, and relates to the field of loom running monitoring, comprising the following steps: mounting multi-parameter sensors on a loom spindle, a let-off mechanism, a take-up mechanism and a motor driving end, and acquiring rotating speed, torque, vibration frequency and motor current and voltage data; the collected data are uploaded to the cloud platform in real time; according to the method, time-sharing acquisition and synchronous calibration of the multi-parameter sensor are combined, data accuracy is guaranteed, loom operation and motor load characteristics are accurately extracted through abnormal data elimination and dynamic characteristic matrix construction, the state is evaluated in real time through an evaluation model, and faults and associated motors are accurately pre-warned.

Description

Technical Field

[0001] This invention relates to the field of loom operation monitoring technology, specifically a method for monitoring the operation status of looms and controlling motor groups on a cloud platform. Background Technology

[0002] A loom is a mechanical device used to weave fabric. It interweaves warp and weft threads to form a fabric. It is the core equipment of the textile industry and has driven the development of clothing, home textiles and other fields.

[0003] Patent application No. 202411339618.0 discloses a monitoring system based on the operating status of a loom, including a loom operation data monitoring and acquisition module, an automatic data processing module, and a fault identification model establishment module. The output of the loom operation data monitoring and acquisition module is connected to the input of the automatic data processing module. The outputs of both the automatic data processing module and the fault identification model establishment module are connected to the input of a data detection module. The output of the data detection module is connected to the input of a fault early warning module. The output of the fault early warning module is connected to the input of a data receiving terminal. The output of the data receiving terminal is connected to the input of a data analysis module. The output of the data analysis module is connected to the input of a data feedback module. The data receiving terminal is one or more of a laptop computer and a mobile phone. This application aims to solve the problem that "existing monitoring systems can only detect and remove abnormal data from the received data, but do not trace and supplement the data, which leads to data inconsistency. At the same time, the system cannot provide solutions to the monitored problems, cannot quickly and substantially solve the problems, and the overall system application effect is poor."

[0004] However, existing technologies for controlling the operating parameters of looms or the control of a group of looms are often singular, making it difficult to achieve good and stable group control of looms.

[0005] To address this, we propose a method for monitoring the operating status of looms and controlling motor groups on a cloud platform. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for monitoring the operating status of a loom and controlling the motor group on a cloud platform, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses a method for monitoring the operating status of a loom and controlling multiple motors integrated into a cloud platform, comprising: Multi-parameter sensors are installed on the loom spindle, warp feed mechanism, take-up mechanism, and motor drive end to collect data on rotational speed, torque, vibration frequency, and motor current and voltage. This data is then uploaded to a cloud platform in real time. The cloud platform receives the data, removes abnormal data based on loom operation fluctuations, and simultaneously constructs a loom operation status feature matrix to extract characteristic parameters reflecting operational stability and motor load. The dimension of the feature matrix is ​​adjusted in real time according to the loom type and the number of motors. The cloud platform calls a preset loom operation status evaluation model, compares the characteristic parameters with standard state thresholds within the model, and generates real-time evaluation results. If any characteristic parameter does not match the corresponding standard state threshold, a fault warning mechanism is triggered to determine the fault type and associated motor. The system pushes early warning information to the management terminal. Based on the operational status assessment results and current production task requirements, the cloud platform aims to maximize loom production efficiency and minimize motor energy consumption. It combines the loom's fabric weaving density, unit time output target, and motor load balancing requirements to set a group control strategy, generating a group control strategy for the speed, start-stop sequence, load distribution, and collaborative operation parameters of the associated motors. The cloud platform converts the group control strategy into control commands and sends them to the motor controllers of each loom via a preset communication link. After receiving the commands, the motor controllers adjust the operating parameters in real time and send the execution feedback information back to the cloud platform. Based on the loom's historical operating data and motor control command execution records, the cloud platform establishes a traceability file for the operating status of individual looms and the cluster, and pushes it to the management terminal.

[0008] Furthermore, the multi-parameter sensor acquires data using a combination of time-division acquisition and synchronous calibration. Time-sharing data acquisition is divided into data acquisition periods according to the rotation cycle of the loom spindle. Within each data acquisition period, the parameters of the warp feeding mechanism, take-up mechanism and motor drive end are collected synchronously. Synchronous calibration uses a preset time synchronization protocol to unify the timestamps of data collected by each sensor to the time base of the cloud platform. Every preset calibration cycle, the acquisition accuracy of the sensors is verified. If the verification result exceeds the preset accuracy error range, a preset sensor parameter correction process is triggered.

[0009] Furthermore, when the cloud platform removes abnormal data from the original data, it follows the following rules: Based on the historical normal operation data of the loom, set the operating safety range for each type of parameter, and identify in real time whether the collected data is within the corresponding operating safety range. If it is not within the corresponding operating safety range, it is marked as preliminary abnormal data. The correlation between parameters is verified for the preliminary abnormal data. The correlation coefficient between the parameter corresponding to 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 to be real abnormal data and removed. If the correlation coefficient is higher than or equal to the preset correlation coefficient threshold, it is determined to be false abnormal data and retained. The formula for calculating the correlation coefficient between the parameters corresponding to the preliminary abnormal data and other related parameters is as follows: ; In the formula: The correlation coefficient between parameter X and associated parameter Y corresponding to the initial abnormal data; The number of times to collect data within a preset time window; , The values ​​of the abnormal parameters and the correlation parameters are at the a-th acquisition time. , This refers to the average value of abnormal parameters within a preset time window and the average value of related parameters within a preset time window.

[0010] Furthermore, the loom operating state feature matrix, when constructed, follows the following rules: The first layer is the basic parameter layer, which uses the collected speed, torque, vibration frequency and motor current and voltage data as the basic feature vector; The second layer is the derived feature layer, which calculates the loom's operational stability coefficient and the motor load coefficient based on the basic feature vectors. ; In the formula: The stability coefficient of the loom operation; This refers to the number of times the rotational speed data is collected within the collection period; This represents the rotational speed data collected during the i-th acquisition. This represents the average value of the rotational speed data within the acquisition period. This is the preset stability adjustment coefficient; This is the motor load factor; The number of times current and voltage data are collected within the acquisition period; This refers to the motor current data collected during the j-th acquisition. This refers to the motor voltage data collected during the j-th data acquisition. The motor power factor; This refers to the rated power of the motor. The third layer is the fusion feature layer, which integrates the basic feature vector with the stability coefficient and load coefficient of the derived feature layer to form a loom operating status feature matrix. The dimension of the feature matrix is ​​adjusted according to the difference in the number of parameters corresponding to the loom type and the change in the number of motors.

[0011] Furthermore, the loom operating status evaluation model is as follows: 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; Calculate the standardized evaluation values ​​of each characteristic parameter. , This represents the standardized evaluation value of the i-th feature parameter. This represents the real-time acquired value of the i-th feature parameter. Represents the standard state upper and lower threshold values ​​of the i-th feature parameter; The comprehensive evaluation value of the loom's operating status is , This represents the total amount of characteristic parameters, if If the value falls below the preset comprehensive evaluation threshold, the loom is determined to be in an abnormal operating state, triggering a fault warning mechanism.

[0012] Furthermore, when determining the fault type and associated motor based on the fault early warning mechanism, the following applies: Establish a fault feature database, which stores the abnormal set of feature parameters and the abnormal threshold range of parameters corresponding to various fault types. The set of abnormal feature parameters is a group of associated parameters that accompany the abnormality when the corresponding fault type occurs, and the threshold range of abnormal parameters is the range of values ​​in which each abnormal parameter deviates from the standard threshold under the corresponding fault type. 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 feature parameter set and abnormal parameter threshold range of various faults in the fault feature database to determine the fault type with the highest matching degree, which is used as the preliminary judgment result of the fault type. Based on the fault types in the preliminary judgment results, the correlation between the fault type and each motor is calculated. , This represents the correlation between the i-th type of fault and the j-th motor. This indicates the total number of consecutive data collection moments before the fault warning is triggered. This represents the characteristic outlier value of the i-th type of fault at the t-th data collection time, calculated as the difference between the outlier parameter at the corresponding time and the standard threshold. This represents the operating parameter value of the j-th motor at the t-th data acquisition time. 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.

[0013] Furthermore, the group control strategy is configured to obey the following: With the optimization objectives of maximizing loom production efficiency and minimizing motor energy consumption, the following optimization objective function is constructed: ; In the formula: , The objective functions are production efficiency and energy consumption. This is the production weighting coefficient; This refers to the real-time output per unit time of the loom. The target output per unit time; This refers to the normal operating time of the loom; Total running time; This is the real-time energy consumption weighting coefficient; This represents the total number of motors. Let be the real-time power of the k-th motor; Let be the running time of the k-th motor; Let be the power fluctuation value of the k-th motor; Initialize and generate a preset number of group control strategy parameter combinations, including motor speed adjustment coefficient, torque distribution ratio and start-stop timing parameters. Calculate the production efficiency and energy consumption target values ​​corresponding to each parameter combination. According to the logic that if a parameter combination is not lower than another combination in terms of production efficiency and not higher than that combination in terms of energy consumption, it is determined to be better. Select the current better parameter combination to form the initial optimal set. In the initial optimal set, the parameter combination with the highest comprehensive closeness to the production efficiency target value and energy consumption target value is selected as the group control strategy parameter by calculating the comprehensive closeness of each parameter combination.

[0014] Furthermore, the preset communication link employs a combination of encrypted transmission and data fragmentation transmission when performing transmission tasks; Encrypted transmission uses a preset asymmetric encryption algorithm to encrypt control commands, generating encrypted command data packets; Data fragmentation transmission divides the encrypted instruction data packet into multiple data fragments according to a preset fragment size. Each data fragment is given a check code and a fragment number. Each data fragment is transmitted to the motor controller in parallel through different communication sub-links. After receiving the data fragments, the motor controller reassembles the data packets according to the fragment sequence number and verifies the integrity of the data packets using a checksum. If the verification passes, the data packets are decrypted to obtain control commands. If the verification fails, a retransmission request is sent to the cloud platform, which then retransmits the corresponding data fragments based on the retransmission request.

[0015] Furthermore, when establishing the operational status traceability files for individual and cluster looms, data is stored using a hierarchical storage architecture: Single loom archive layer: Stores historical operating data, motor control command execution records, fault warning records and fault handling records for each loom. It is indexed by time dimension and supports data retrieval by loom number, time interval and fault type. Cluster Operation Archive Layer: Based on the historical operation data of all looms in the individual loom archive layer, the following calculations are performed on the operation parameters of the individual looms according to a predetermined statistical period to generate cluster characteristic indicators: The unit time output, energy consumption data, and number of failures of each loom are statistically analyzed separately to obtain the unit time output value, total energy consumption value, and number of failures of a single loom. The output per unit time of each loom is summed up to obtain the total output per unit time of the cluster. The total output per unit time of the cluster is compared with the theoretical maximum total output of the cluster within a predetermined statistical period and recorded as the cluster operating efficiency. The total energy consumption of each loom is summed to obtain the total energy consumption of the cluster. The total energy consumption of the cluster is then compared with the total running time of the cluster within a predetermined statistical period and recorded as the average energy consumption. The number of individual failures of all looms is summed up to obtain the total number of cluster failures. The total number of cluster failures is then compared with the total cluster runtime within a predetermined statistical period and recorded as the failure rate. All intermediate data and final calculation results above are associated with the original data identifier of the corresponding single loom; Archive update layer: Real-time monitoring of loom operation data and control command execution status. When the data meets the preset update conditions, it automatically updates the data in the archive layer of a single loom and the archive layer of the cluster operation, and archives and stores historical data. The preset update conditions include: the amount of data reaches a preset threshold, and the time interval reaches a preset period.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention provides a method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform. During the execution of this method, the accuracy of the data is ensured by combining time-sharing acquisition and synchronous calibration of multi-parameter sensors. After abnormal data removal and construction of a dynamic feature matrix, the operating characteristics of the loom and the load characteristics of the motors are accurately extracted. The status is evaluated in real time based on the evaluation model, and the faults and associated motors are accurately warned. With the goal of maximizing production efficiency and minimizing energy consumption, a group control strategy is set up to optimize motor operating parameters. Encryption and segmented transmission are combined to ensure the safe and efficient transmission of control commands. At the same time, a hierarchical traceability file is established to enable the traceability of operating data. It can adapt to the collaborative management of multiple types of looms and multiple motors, effectively improve the stability and production efficiency of loom operation, reduce motor energy consumption, reduce the impact of failures, and provide reliable support for the efficient operation and maintenance of loom clusters. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example: This embodiment presents a method for monitoring the operating status of a loom and controlling multiple motors integrated into a cloud platform, such as... Figure 1 Shown, including: Multi-parameter sensors are installed on the main shaft of the loom, the warp feeding mechanism, the take-up mechanism and the motor drive end to collect data on rotation speed, torque, vibration frequency and motor current and voltage, and the collected data is uploaded to the cloud platform in real time. When acquiring data from multi-parameter sensors, a combination of time-division acquisition and synchronous calibration is used. Time-sharing data acquisition is divided into data acquisition periods according to the rotation cycle of the loom spindle. Within each data acquisition period, the parameters of the warp feeding mechanism, take-up mechanism and motor drive end are collected synchronously. Synchronous calibration uses a preset time synchronization protocol to unify the timestamps of data collected by each sensor to the time base of the cloud platform. Every preset calibration cycle, the sensor's acquisition accuracy is verified. If the verification result exceeds the preset accuracy error range, a preset sensor parameter correction process is triggered. The preset sensor parameter correction process includes: calling the preset parameter compensation algorithm to calculate the corresponding compensation coefficient based on the accuracy error value; writing the compensation coefficient into the sensor's parameter configuration module to correct the acquisition deviation; and synchronously recording the correction time, compensation coefficient, and accuracy verification values ​​before and after correction to the sensor calibration file on the cloud platform. The cloud platform receives the collected data, removes abnormal data from the original data based on the fluctuation of the loom operation data, and simultaneously constructs a loom operation status feature matrix to extract feature parameters that reflect the operation stability and motor load. The dimension of the feature matrix is ​​adjusted in real time according to the type of loom and the number of motors. When the cloud platform removes abnormal data from the original data, it follows the following rules: Based on the historical normal operation data of the loom, set the operating safety range for each type of parameter, and identify in real time whether the collected data is within the corresponding operating safety range. If it is not within the corresponding operating safety range, it is marked as preliminary abnormal data. The correlation between parameters is verified for the preliminary abnormal data. The correlation coefficient between the parameter corresponding to 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 to be real abnormal data and removed. If the correlation coefficient is higher than or equal to the preset correlation coefficient threshold, it is determined to be false abnormal data and retained. The formula for calculating the correlation coefficient between the parameters corresponding to the preliminary abnormal data and other related parameters is as follows: ; In the formula: The correlation coefficient between parameter X and associated parameter Y corresponding to the initial abnormal data; The number of times to collect data within a preset time window; , The values ​​of the abnormal parameters and the correlation parameters are at the a-th acquisition time. , The average value of the abnormal parameters within a preset time window, and the average value of the related parameters within a preset time window; The above formula introduces the number of collections within a preset time window, and calculates the difference between the values ​​of the parameters corresponding to the preliminary abnormal data and the associated parameters at each collection time and their respective average values ​​within the time window, multiplies them and sums them, and then divides them by the square root product of the sum of the squares of the deviations of the two parameters. This quantifies the degree of linear correlation between the two, effectively distinguishes between real and false anomalies, avoids misjudging abnormal data due to accidental fluctuations, ensures the accuracy of data removal, and provides a reliable data foundation for subsequent status assessment. Among them, the historical normal operation data of the loom is always the historical normal operation data of the loom within the previous preset time interval with reference to the current timestamp. The operation safety interval of each type of parameter is updated in real time based on the iteration of the historical normal operation data of the loom. Each operation safety interval is composed of the maximum and minimum values ​​of the corresponding data type in the historical normal operation data of the loom. The associated parameters of the parameters corresponding to the preliminary abnormal data are determined based on the preset basic association map. The loom operating state feature matrix, when constructed, follows the following rules: The first layer is the basic parameter layer, which uses the collected speed, torque, vibration frequency and motor current and voltage data as the basic feature vector; The second layer is the derived feature layer, which calculates the loom's operational stability coefficient and the motor load coefficient based on the basic feature vectors. ; In the formula: The stability coefficient of the loom operation; This refers to the number of times the rotational speed data is collected within the collection period; This represents the rotational speed data collected during the i-th acquisition. This represents the average value of the rotational speed data within the acquisition period. This is the preset stability adjustment coefficient; This is the motor load factor; The number of times current and voltage data are collected within the acquisition period; This refers to the motor current data collected during the j-th acquisition. This refers to the motor voltage data collected during the j-th data acquisition. The motor power factor; This refers to the rated power of the motor. The above formula is based on the number of times the rotation speed data is collected within the collection cycle. By calculating the sum of squares of the difference between each rotation speed data and the average rotation speed within the cycle, and then multiplying it by the preset stability adjustment coefficient, the loom operation stability coefficient can accurately reflect the operation stability of the loom under different fabric weaving scenarios, and provide characteristic parameters that fit actual needs for condition assessment. By utilizing the number of current and voltage data acquisitions within the acquisition cycle, the motor current and voltage data acquired each time are multiplied, then multiplied by the motor power factor, summed, and finally divided by the product of the motor's rated power and the number of acquisitions, the motor load coefficient is obtained. This accurately measures the actual load of the motor, providing an important basis for load allocation in the subsequent group control strategy formulation, and achieving precise control of the motor load. The third layer is the fusion feature layer, which merges the basic feature vector with the stability coefficient and load coefficient of the derived feature layer to form a loom operating status feature matrix. The dimension of the feature matrix is ​​adjusted according to the difference in the number of parameters corresponding to the loom type and the change in the number of motors. The dimension of the feature matrix is ​​increased or decreased by adding or removing the number of feature columns in the following ways: First, the type of loom is identified by the model identification information of the loom. Based on the preset loom type-parameter mapping table, the monitoring parameters unique to this type of loom are determined, such as the rapier head displacement parameters of the rapier loom and the airflow pressure parameters of the air-jet loom. These unique parameters are then used as the basis for adding new feature columns. 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; 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. 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. 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. 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. The loom operating status assessment model is as follows: 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; 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. Calculate the standardized evaluation values ​​of each characteristic parameter. , This represents the standardized evaluation value of the i-th feature parameter. This represents the real-time acquired value of the i-th feature parameter. Represents the standard state upper and lower threshold values ​​of the i-th feature parameter; The comprehensive evaluation value of the loom's operating status is , This represents the total amount of characteristic parameters, if If the value is below the preset comprehensive evaluation threshold, the loom is determined to be in an abnormal operating state, triggering the fault warning mechanism. Among them, the safety weight allocation coefficient and the efficiency weight allocation coefficient The values ​​are all greater than zero and their sum is 1, and they follow the following rules: It increases as the safety requirements for loom operation increase and decreases as they decrease. , The values ​​are all within the range of 0 to 1, and The greater the threat to the safe operation of the loom when the characteristic parameter is abnormal, the larger the value; conversely, the smaller the value. When optimizing feature parameters, the more significant the improvement in loom production efficiency, the larger the value; conversely, the smaller the value. When determining the fault type and associated motors based on the fault early warning mechanism, the following applies: Establish a fault feature database, which stores the abnormal set of characteristic parameters and the abnormal threshold range of parameters corresponding to each type of fault. The feature parameter anomaly set is the associated parameter group that accompanies the anomaly when the corresponding fault type occurs, and the parameter anomaly threshold range is the range of values ​​of each abnormal parameter deviating from the standard threshold under the corresponding fault type. 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 parameter abnormal threshold range of various faults in the fault feature database to determine the fault type with the highest matching degree, which serves as the preliminary judgment result of the fault type. Based on the fault type identified in the preliminary assessment, the correlation between that fault type and each motor is calculated. , This represents the correlation between the i-th type of fault and the j-th motor. This indicates the total number of consecutive data collection moments before the fault warning is triggered. This represents the characteristic outlier value of the i-th type of fault at the t-th data collection time, calculated as the difference between the outlier parameter at the corresponding time and the standard threshold. This represents the operating parameter value of the j-th motor at the t-th data acquisition time. The above formula is based on the total number of continuous acquisition times before the fault warning is triggered. It calculates the sum of the products of the characteristic abnormal values ​​of the i-th type of fault at each acquisition time and the corresponding operating parameter values ​​of the j-th motor at the same time, and obtains the correlation between the two. It can accurately locate the fault-related motors, and when multiple motors are associated, it can sort and determine the primary and secondary associated motors, providing a clear direction for fault handling. If the correlation of a certain motor If the correlation score exceeds the preset correlation threshold, the motor is determined to be a associated motor of the fault type; if there is a correlation score for a non-unique motor... 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. Before calculating the correlation, a causal relationship network between faults and motors is constructed based on the mechanical transmission connection between the loom spindle, warp feed mechanism, take-up mechanism and each motor (e.g., the spindle drives the warp feed motor and take-up motor to operate synchronously through a gear set, and the warp feed mechanism and the warp feed motor are directly connected through a coupling) and electrical control correlation logic (e.g., the spindle speed signal is used as the speed adjustment reference signal for the warp feed motor and take-up motor, and the motor current feedback signal is used to control the load output of the corresponding mechanism). This network pre-stores the correlation rules of different mechanism faults transmitted to the corresponding motors through mechanical transmission or electrical control paths. Based on the causal relationship network, a set of motors with correlation is selected, and then the correlation between the fault type in the preliminary judgment result and each motor in the set is calculated. Based on the operational status assessment results and current production task requirements, the cloud platform aims to maximize the production efficiency of the loom and minimize the energy consumption of the motor. It combines the weaving density of the loom fabric, the output target per unit time, and the motor load balancing requirements to set up a group control strategy and generate a group control strategy for the speed, start-stop sequence, load distribution, and collaborative operation parameters of the associated motors. The group control strategy follows the following rules when configured: With the optimization objectives of maximizing loom production efficiency and minimizing motor energy consumption, the following optimization objective function is constructed: ; In the formula: , The objective functions are production efficiency and energy consumption. This is the production weighting coefficient; This refers to the real-time output per unit time of the loom. The target output per unit time; This refers to the normal operating time of the loom; Total running time; This is the real-time energy consumption weighting coefficient; This represents the total number of motors. Let be the real-time power of the k-th motor; Let be the running time of the k-th motor; Let be the power fluctuation value of the k-th motor; The above formula is the production efficiency objective function formed by the product of the production weight coefficient and the product of the product. It comprehensively considers the actual production target and the equipment operation reliability. By highlighting the importance of the production target through the weight coefficient, it can quantify the production efficiency achievement and provide a clear direction for production efficiency optimization for the group control strategy, thereby maximizing production efficiency. The energy consumption objective function integrates the real-time power, running time and power fluctuation of the motor to comprehensively measure the energy consumption level of the motor. It emphasizes the importance of energy consumption control through weighting coefficients, provides a clear basis for energy consumption optimization for the formulation of group control strategies, and minimizes the energy consumption of the motor. Initialize and generate a preset number of group control strategy parameter combinations, including motor speed adjustment coefficient, torque distribution ratio and start-stop timing parameters. Calculate the production efficiency and energy consumption target values ​​corresponding to each parameter combination. According to the logic that if a parameter combination is not lower than another combination in terms of production efficiency and not higher than that combination in terms of energy consumption, it is determined to be better. Select the current better parameter combination to form the initial optimal set. In the initial optimal set, the parameter combination with the highest comprehensive closeness to the production efficiency target value and energy consumption target value is selected as the group control strategy parameter by calculating the comprehensive closeness between each parameter combination and the target value of production efficiency and energy consumption. Among them, the production weighting coefficient ∈ (0,1), the value is larger when the current production task of the loom is urgent and the unit time output target needs to be achieved first, and smaller when the unit time output target of the loom is close to being achieved or the motor energy consumption control needs to be taken into account first. Real-time energy consumption weighting coefficient ∈ (0,1), when it is necessary to prioritize reducing the total energy consumption and power fluctuation of the motor, such as when energy supply is tight or energy consumption assessment indicators are strict, the value is larger, and vice versa. The overall proximity score is based on preset weights to normalize and integrate the production efficiency achievement rate and energy consumption reduction rate to achieve quantification; The cloud platform converts the group control strategy into control commands, which are then sent to the motor controllers of each loom via a preset communication link. After receiving the commands, the motor controllers adjust the operating parameters in real time and send the execution feedback information back to the cloud platform. The pre-defined communication link uses a combination of encrypted transmission and data fragmentation transmission when performing transmission tasks; Encrypted transmission uses a preset asymmetric encryption algorithm to encrypt control commands, generating encrypted command data packets; Data fragmentation transmission divides the encrypted instruction data packet into multiple data fragments according to a preset fragment size. Each data fragment is given a check code and a fragment number. Each data fragment is transmitted to the motor controller in parallel through different communication sub-links. After receiving the data fragments, the motor controller reassembles the data packets according to the fragment sequence number and verifies the integrity of the data packets using the check code. If the verification passes, the data packets are decrypted to obtain control commands. If the verification fails, a retransmission request is sent to the cloud platform, and the cloud platform retransmits the corresponding data fragments according to the retransmission request. Based on historical operating data of the loom and motor control command execution records, the cloud platform establishes a traceability file of the operating status of a single loom and a cluster, and pushes it to the management personnel terminal; When establishing traceability files for the operational status of individual looms and clusters, data is stored using a hierarchical storage architecture: Single loom archive layer: Stores historical operating data, motor control command execution records, fault warning records and fault handling records for each loom. It is indexed by time dimension and supports data retrieval by loom number, time interval and fault type. Cluster Operation Archive Layer: Based on the historical operation data of all looms in the individual loom archive layer, the following calculations are performed on the operation parameters of the individual looms according to a predetermined statistical period to generate cluster characteristic indicators: The unit time output, energy consumption data, and number of failures of each loom are statistically analyzed separately to obtain the unit time output value, total energy consumption value, and number of failures of a single loom. The output per unit time of each loom is summed up to obtain the total output per unit time of the cluster. The total output per unit time of the cluster is compared with the theoretical maximum total output of the cluster within a predetermined statistical period and recorded as the cluster operating efficiency. The total energy consumption of each loom is summed to obtain the total energy consumption of the cluster. The total energy consumption of the cluster is then compared with the total running time of the cluster within a predetermined statistical period and recorded as the average energy consumption. The number of individual failures of all looms is summed up to obtain the total number of cluster failures. The total number of cluster failures is then compared with the total cluster runtime within a predetermined statistical period and recorded as the failure rate. All intermediate data and final calculation results above are associated with the original data identifier of the corresponding single loom; Archive update layer: Real-time monitoring of loom operation data and control command execution status. When the data meets the preset update conditions, it automatically updates the data in the archive layer of a single loom and the archive layer of the cluster operation, and archives and stores historical data. The preset update conditions include: the amount of data reaches a preset threshold, and the time interval reaches a preset period. Among them, the cloud platform is a dedicated cloud platform for loom operation status monitoring and motor group control. It has functions such as data reception, processing, operation status evaluation, motor group control strategy generation, control command issuance, and operation file storage and retrieval. It can be adapted to the collaborative management of multiple types of looms and multiple motors.

[0022] In this embodiment, the above method can accurately collect the operating data of key parts of the loom and the motor, ensure data reliability through synchronous calibration and anomaly rejection, accurately assess the operating status of the loom, provide timely warning of faults and locate related motors, formulate motor group control strategies with the goal of high efficiency and low consumption, optimize operating parameters, and establish a complete operation traceability file to assist management, significantly improve the stability of loom operation and production efficiency, and reduce motor energy consumption.

[0023] The above embodiments illustrate an application example of a technical solution: A textile factory deployed a cluster of three air-jet looms and implemented intelligent management using a loom monitoring and motor group control method integrated into a cloud platform. The specific application process is as follows: Multi-parameter sensors are installed on the main shaft, warp feed mechanism, take-up mechanism, and motor drive end of each loom to collect data on rotational speed, torque, vibration frequency, motor current and voltage, and airflow pressure specific to air-jet looms. Data collection is divided into time periods based on the loom main shaft rotation cycle (each cycle is 0.5 seconds). Within each time period, parameters from the warp feed mechanism, take-up mechanism, and motor drive end are collected synchronously. Simultaneously, the timestamps of the data from each sensor are unified to the cloud platform's time base via a time synchronization protocol, and the data collection accuracy is verified every 2 hours. During one verification, the error of the motor current sensor on loom No. 1 exceeded the preset range. The system immediately called the parameter compensation algorithm to calculate a compensation coefficient of 0.03, wrote it into the sensor configuration module to correct the deviation, and recorded the correction time, coefficient, and accuracy values ​​before and after correction in the cloud platform's sensor calibration file.

[0024] After receiving the data, the cloud platform first sets safe ranges for each parameter based on the historical normal operation data of the looms over the previous 7 days, such as a safe range for rotational speed of 1200-1300 r / min. When the rotational speed of loom No. 2 is detected to be 1150 r / min, it is marked as preliminary abnormal data. The correlation coefficient between this rotational speed and the associated parameters is further calculated. If the result is 0.2 (lower than the preset threshold of 0.5), it is determined to be real abnormal data and removed. If the correlation coefficient is 0.6 (higher than the threshold), it is determined to be false abnormal data and retained.

[0025] Subsequently, a feature matrix of the loom's operating status was constructed: the basic parameter layer included the collected rotational 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 calculated the stability coefficient and load coefficient—the stability coefficient was based on 20 rotational speed data collections, and the sum of the squares of the deviations of each rotational speed from the average value was multiplied by 0.2 (due to the high stability requirements of weaving high-precision cotton fabric, the adjustment coefficient was set to a smaller value), resulting in 0.75; the load coefficient was based on 20 current and voltage data collections, and each data point was multiplied by the power factor of 0.85, summed, and then divided by the rated power of the motor 7.5 kW, resulting in 0.6; the fusion feature layer merged the basic parameters with the two coefficients. Since each motor of the three looms has independent monitoring parameters, the feature matrix added three columns of independent motor parameters and removed duplicate vibration parameter columns to form a feature matrix adapted to the current cluster.

[0026] The cloud platform invokes the operational status assessment model, setting a safety weight of 0.6 and an efficiency weight of 0.4 (factory safety is prioritized). Weight coefficients are assigned to each characteristic parameter; for example, the safety impact priority of rotational speed is 0.8, and its efficiency contribution is 0.7, resulting in a weight coefficient of 0.76. Then, standardized evaluation values ​​for each parameter are calculated; for example, a real-time rotational speed of 1230 r / min (standard upper and lower limits 1200-1300 r / min) has a standardized evaluation value of 0.8. The final comprehensive evaluation value of the eight characteristic parameters is 0.72, which is lower than the preset threshold of 0.8, triggering a fault warning.

[0027] When a fault warning is issued, the system compares the fault feature database: the anomaly set corresponding to the "overload of the warp feed motor" fault is "warp feed torque exceeding 8.5 N·m, motor current exceeding 5.5 A". Real-time monitoring shows that the warp feed torque of loom No. 2 is 8.8 N·m and the current is 5.6 A, which has the highest match degree with this fault, and it is initially determined to be this fault. A causal relationship network is constructed based on the mechanical transmission relationship to screen out the set of associated motors; further, the correlation degree is calculated. Based on the 30 consecutive data collected before the fault warning, combined with the fault feature anomaly value and the real-time operating parameters of the motor, the correlation degree is calculated to be 0.85, and the warp feed motor No. 2 is determined to be the associated motor of the fault.

[0028] The cloud platform constructs an optimization function with the objectives of maximizing production efficiency (current orders are urgent, so output weight is 0.7) and minimizing motor energy consumption (energy consumption weight is 0.3), initializing 10 sets of group control parameter combinations. The target values ​​for each set are calculated; for example, the objective function value for production efficiency for a certain set of parameters is 0.98, and the objective function value for energy consumption is 0.92. After selecting 3 better parameter combinations, the overall proximity is calculated. One set with the highest proximity of 0.95 is determined as the group control strategy: the speed of the No. 2 conveyor motor is adjusted to 1260 r / min, the torque distribution is 0.9, and the start / stop sequence is synchronized with other motors.

[0029] The cloud platform encrypts the group control commands using an asymmetric encryption algorithm, splits them into three data fragments with checksums and sequence numbers, and transmits them in parallel to each motor controller via three communication sub-links. Motor controller #2 receives the data, reassembles it according to the sequence number, decrypts and executes it after successful verification, and sends a "parameters adjusted" feedback message back to the cloud platform. If verification fails, a retransmission request is sent, and the cloud platform retransmits the corresponding fragment.

[0030] Finally, the cloud platform establishes an operational status traceability archive: the single-machine archive layer stores historical data, control commands, and fault records for each loom by time index, supporting retrieval by loom number, time, and fault type; the cluster archive layer provides daily statistics, with the individual outputs of the three looms being 2000m, 1900m, and 2100m respectively, and the total cluster output being 6000m, with an operating efficiency of 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, with a fault occurrence rate of 8.3%; the archive update layer automatically updates and archives data every 12 hours, and pushes it synchronously to the administrator terminal.

[0031] In summary, the methods described in the above embodiments, during execution, ensure data accuracy by combining time-sharing data acquisition and synchronous calibration using multi-parameter sensors. Through abnormal data removal and dynamic feature matrix construction, they accurately extract the characteristics of loom operation and motor load. Relying on the evaluation model, they assess the status in real time and accurately warn of faults and associated motors. With the goal of maximizing production efficiency and minimizing energy consumption, they set group control strategies, optimize motor operating parameters, and combine encryption and segmented transmission to ensure the safe and efficient transmission of control commands. At the same time, they establish hierarchical traceability files to achieve traceability of operating data. This approach can adapt to the collaborative management of multiple types of looms and multiple motors, effectively improving the stability and production efficiency of loom operation, reducing motor energy consumption, minimizing the impact of faults, and providing reliable support for the efficient operation and maintenance of loom clusters.

[0032] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 invention.

Claims

1. A method for monitoring the operating status of a loom and controlling multiple motors integrated into a cloud platform, characterized in that, include: Multi-parameter sensors are installed on the main shaft of the loom, the warp feeding mechanism, the take-up mechanism and the motor drive end to collect data on rotation speed, torque, vibration frequency and motor current and voltage, and the collected data is uploaded to the cloud platform in real time. The cloud platform receives the collected data, removes abnormal data from the original data based on the fluctuation of the loom operation data, and simultaneously constructs a loom operation status feature matrix to extract feature parameters that reflect the operation stability and motor load. The dimension of the feature matrix is ​​adjusted in real time according to the type of loom and the number of motors. 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. Based on the operational status assessment results and current production task requirements, the cloud platform aims to maximize the production efficiency of the loom and minimize the energy consumption of the motor. It combines the weaving density of the loom fabric, the output target per unit time, and the motor load balancing requirements to set up a group control strategy and generate a group control strategy for the speed, start-stop sequence, load distribution, and collaborative operation parameters of the associated motors. The cloud platform converts the group control strategy into control commands, which are then sent to the motor controllers of each loom via a preset communication link. After receiving the commands, the motor controllers adjust the operating parameters in real time and send the execution feedback information back to the cloud platform. Based on historical operating data of the looms and motor control command execution records, the cloud platform establishes traceability files for the operating status of individual looms and clusters, and pushes them to the management personnel's terminals.

2. The method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform according to claim 1, characterized in that, When the multi-parameter sensor collects data, it adopts a combination of time-division acquisition and synchronous calibration: Time-sharing data acquisition is divided into data acquisition periods according to the rotation cycle of the loom spindle. Within each data acquisition period, the parameters of the warp feeding mechanism, take-up mechanism and motor drive end are collected synchronously. Synchronous calibration uses a preset time synchronization protocol to unify the timestamps of the data collected by each sensor to the time base of the cloud platform. Every preset calibration cycle, the acquisition accuracy of the sensor is verified. If the verification result exceeds the preset accuracy error range, the preset sensor parameter correction process is triggered. Each data collection period corresponds to a data collection cycle.

3. The method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform according to claim 1, characterized in that, The cloud platform follows the following rules when removing abnormal data from the original data: Based on the historical normal operation data of the loom, set the operating safety range for each type of parameter, and identify in real time whether the collected data is within the corresponding operating safety range. If it is not within the corresponding operating safety range, it is marked as preliminary abnormal data. The correlation between parameters is verified for the preliminary abnormal data. The correlation coefficient between the parameter corresponding to 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 to be real abnormal data and removed. If the correlation coefficient is higher than or equal to the preset correlation coefficient threshold, it is determined to be false abnormal data and retained. The formula for calculating the correlation coefficient between the parameters corresponding to the preliminary abnormal data and other related parameters is as follows: ； Where: The correlation coefficient between parameter X and associated parameter Y corresponding to the initial abnormal data; The number of times to collect data within a preset time window; , The values ​​of the abnormal parameters and the correlation parameters are at the a-th acquisition time. , This refers to the average value of abnormal parameters within a preset time window and the average value of related parameters within a preset time window.

4. The method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform according to claim 2, characterized in that, The loom operating state feature matrix, when constructed, follows the following rules: The first layer is the basic parameter layer, which uses the collected speed, torque, vibration frequency and motor current and voltage data as the basic feature vector; The second layer is the derived feature layer, which calculates the loom's operational stability coefficient and the motor load coefficient based on the basic feature vectors. ； Where: The stability coefficient of the loom operation; This refers to the number of times the rotational speed data is collected within the collection period; This represents the rotational speed data collected during the i-th acquisition. This represents the average value of the rotational speed data within the acquisition period. This is the preset stability adjustment coefficient; This is the motor load factor; The number of times current and voltage data are collected within the acquisition period; This represents the motor current data collected during the j-th data acquisition. This refers to the motor voltage data collected during the j-th acquisition. The motor power factor; This refers to the rated power of the motor. The third layer is the fusion feature layer, which integrates the basic feature vector with the stability coefficient and load coefficient of the derived feature layer to form a loom operating status feature matrix. The dimension of the feature matrix is ​​adjusted according to the difference in the number of parameters corresponding to the loom type and the change in the number of motors.

5. The method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform according to claim 1, characterized in that, The loom operating status evaluation model is as follows: 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; Calculate the standardized evaluation values ​​of each characteristic parameter. , This represents the standardized evaluation value of the i-th feature parameter. This represents the real-time acquired value of the i-th feature parameter. Represents the standard state upper and lower threshold values ​​of the i-th feature parameter; The comprehensive evaluation value of the loom's operating status is , This represents the total amount of characteristic parameters, if If the value falls below the preset comprehensive evaluation threshold, the loom's operating status is determined to be abnormal, triggering the fault warning mechanism.

6. The method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform according to claim 5, characterized in that, When determining the fault type and associated motors based on the fault early warning mechanism, the following applies: Establish a fault feature database, which stores the abnormal set of feature parameters and the abnormal threshold range of parameters corresponding to various fault types. The set of abnormal feature parameters is a group of associated parameters that accompany the abnormality when the corresponding fault type occurs, and the threshold range of abnormal parameters is the range of values ​​in which each abnormal parameter deviates from the standard threshold under the corresponding fault type. 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 feature parameter set and abnormal parameter threshold range of various faults in the fault feature database to determine the fault type with the highest matching degree, which is used as the preliminary judgment result of the fault type. Based on the fault types in the preliminary judgment results, the correlation between the fault type and each motor is calculated. , This represents the correlation between the i-th type of fault and the j-th motor. This indicates the total number of consecutive data collection moments before the fault warning is triggered. This represents the characteristic outlier value of the i-th type of fault at the t-th data collection time, calculated as the difference between the outlier parameter at the corresponding time and the standard threshold. This represents the operating parameter value of the j-th motor at the t-th data acquisition time. 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 method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform according to claim 1, characterized in that, The group control strategy is configured to follow the following: With the optimization objectives of maximizing loom production efficiency and minimizing motor energy consumption, the following optimization objective function is constructed: ； Where: , The objective functions are production efficiency and energy consumption. This is the production weighting coefficient; This refers to the real-time output per unit time of the loom. The target output per unit time; This refers to the normal operating time of the loom; Total running time; This is the real-time energy consumption weighting coefficient; This represents the total number of motors. Let be the real-time power of the k-th motor; Let be the running time of the k-th motor; Let be the power fluctuation value of the k-th motor; Initialize and generate a preset number of group control strategy parameter combinations, including motor speed adjustment coefficient, torque distribution ratio and start-stop timing parameters. Calculate the production efficiency and energy consumption target values ​​corresponding to each parameter combination. According to the logic that if a parameter combination is not lower than another combination in terms of production efficiency and not higher than that combination in terms of energy consumption, it is determined to be better. Select the current better parameter combination to form the initial optimal set. In the initial optimal set, the parameter combination with the highest comprehensive closeness to the production efficiency target value and energy consumption target value is selected as the group control strategy parameter by calculating the comprehensive closeness of each parameter combination.

8. The method for monitoring the operating status of a loom and controlling a group of motors integrated into a cloud platform according to claim 1, characterized in that, When performing transmission tasks, the preset communication link adopts a combination of encrypted transmission and data fragmentation transmission. Encrypted transmission uses a preset asymmetric encryption algorithm to encrypt control commands, generating encrypted command data packets; Data fragmentation transmission divides the encrypted instruction data packet into multiple data fragments according to a preset fragment size. Each data fragment is given a check code and a fragment number. Each data fragment is transmitted to the motor controller in parallel through different communication sub-links. After receiving the data fragments, the motor controller reassembles the data packets according to the fragment sequence number and verifies the integrity of the data packets using a checksum. If the verification passes, the data packets are decrypted to obtain control commands. If the verification fails, a retransmission request is sent to the cloud platform, which then retransmits the corresponding data fragments based on the retransmission request.

9. A method for monitoring the operating status of a loom and controlling multiple motors integrated into a cloud platform according to claim 1, characterized in that, When establishing the operational status traceability files for individual looms and clusters, data is stored using a hierarchical storage architecture: Single loom archive layer: Stores historical operating data, motor control command execution records, fault warning records and fault handling records for each loom. It is indexed by time dimension and supports data retrieval by loom number, time interval and fault type. Cluster Operation Archive Layer: Based on the historical operation data of all looms in the individual loom archive layer, the following calculations are performed on the operation parameters of the individual looms according to a predetermined statistical period to generate cluster characteristic indicators: The unit time output, energy consumption data, and number of failures of each loom are statistically analyzed separately to obtain the unit time output value, total energy consumption value, and number of failures of a single loom. The output per unit time of each loom is summed up to obtain the total output per unit time of the cluster. The total output per unit time of the cluster is compared with the theoretical maximum total output of the cluster within a predetermined statistical period and recorded as the cluster operating efficiency. The total energy consumption of each loom is summed to obtain the total energy consumption of the cluster. The total energy consumption of the cluster is then compared with the total running time of the cluster within a predetermined statistical period and recorded as the average energy consumption. The number of individual failures of all looms is summed up to obtain the total number of cluster failures. The total number of cluster failures is then compared with the total cluster runtime within a predetermined statistical period and recorded as the failure rate. All intermediate data and final calculation results above are associated with the original data identifier of the corresponding single loom; Archive update layer: Real-time monitoring of loom operation data and control command execution status. When the data meets the preset update conditions, it automatically updates the data in the archive layer of a single loom and the archive layer of the cluster operation, and archives and stores historical data. The preset update conditions include: the amount of data reaches a preset threshold, and the time interval reaches a preset period.

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