CN116483015A - Workshop equipment monitoring method, device, equipment and storage medium - Google Patents
Workshop equipment monitoring method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the field of data analysis, and discloses a workshop equipment monitoring method, device, equipment and storage medium, which are used for improving the flexibility and accuracy of workshop equipment monitoring. The method comprises the following steps: matching the configuration parameters of the target equipment, generating equipment configuration parameters, and carrying out parameter configuration on the target equipment through the equipment configuration parameters; establishing connection between the data acquisition tool and the IO collector, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment operation state data; performing analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate coding state data, and performing statistic feature extraction on the coding state data to generate state feature data; converting the state characteristic data matrix to generate a state characteristic vector; and inputting the state feature vector into an abnormal state analysis model for equipment abnormality analysis, generating a state analysis result, and carrying out coping strategy analysis through the state analysis result to obtain a target strategy.
Description
Technical Field
The present invention relates to the field of data analysis, and in particular, to a method, an apparatus, a device, and a storage medium for monitoring a plant device.
Background
Along with the continuous development of internet technology, a plurality of communication program methods exist, the realization logics are different, and the technologies are key technologies widely applied in the fields of intelligent manufacturing, internet of things and the like, so that equipment management and equipment maintenance can be more accurate, efficient and intelligent. The configuration and data acquisition of the target equipment are realized by connecting the equipment management system and the target equipment, and the operation state of the equipment is analyzed and optimized by utilizing the technologies of singular value decomposition, anomaly detection and the like, so that the operation efficiency and reliability of the equipment are improved, and the production efficiency and the competitiveness of enterprises are further improved.
However, the deficiencies of the prior art still exist. For example, in terms of data acquisition and transmission, the lack of perfect measures for data encryption and security protection is prone to problems such as hacking and data leakage; in terms of device state analysis, it is difficult to cope with complicated device states and abnormal situations, but only stays on basic statistical analysis and machine learning methods. Meanwhile, the processing logic of the equipment cannot be configured according to the actual scene, and some difficulties are encountered when the real-time data acquisition of the equipment is handled, and particularly development engineers are required to have a more skilled hardware foundation, so that the actual demands of customers can be known according to the equipment conditions.
Disclosure of Invention
The invention provides a workshop equipment monitoring method, a device, equipment and a storage medium, which are used for improving the flexibility and accuracy of workshop equipment monitoring.
The first aspect of the invention provides a workshop equipment monitoring method, which comprises the following steps: performing configuration parameter matching on target equipment based on a preset equipment management system, generating equipment configuration parameters, and performing parameter configuration on the target equipment through the equipment configuration parameters;
establishing connection between a data acquisition tool in the equipment management system and an IO collector in the target equipment, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment operation state data;
performing analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and performing statistic feature extraction on the coding state data to generate state feature data;
converting the state characteristic data matrix by a preset singular value decomposition algorithm to generate a state characteristic vector;
and inputting the state feature vector into a preset abnormal state analysis model for equipment abnormality analysis, generating a state analysis result, and carrying out coping strategy analysis through the state analysis result to obtain a target strategy.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the performing, by using the preset device management system, matching configuration parameters of a target device, generating device configuration parameters, and performing parameter configuration on the target device by using the device configuration parameters, includes:
performing equipment identification analysis on the target equipment to obtain equipment identification of the target equipment;
analyzing the equipment type of the target equipment through the equipment identifier to acquire the equipment type of the target equipment;
based on the equipment type, carrying out parameter corresponding relation matching on the target equipment through the equipment management system, and determining a corresponding parameter corresponding relation;
constructing a parameter mapping relation for the target equipment through the parameter corresponding relation, and generating a parameter mapping relation;
and carrying out configuration parameter mapping on the target equipment through the equipment management system based on the parameter mapping relation, generating equipment configuration parameters, and carrying out parameter configuration on the target equipment through the equipment configuration parameters.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the establishing a connection between a data collection tool in the device management system and an IO collector in the target device, and performing feedback data collection on the IO collector by using the data collection tool, to generate device operation state data includes:
Collecting a communication protocol of the IO collector to obtain the communication protocol of the target equipment;
carrying out protocol synchronous configuration on the data acquisition tool based on the communication protocol, and simultaneously establishing connection between the data acquisition tool and the IO collector;
and sending a data acquisition request to the IO collector through the data acquisition tool, receiving feedback data sent by the IO collector, and generating the equipment running state data through the feedback data.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the performing analog-to-digital conversion on the device operation state data and the device configuration parameter to generate corresponding coding state data, and performing statistical feature extraction on the coding state data to generate state feature data includes:
carrying out data combination on the equipment running state data and the equipment configuration parameter data to generate data to be processed;
floating point type data screening is carried out on the data to be processed to obtain floating point type data;
formatting the floating point data in the data to be processed to obtain formatted data;
analog-to-digital conversion is carried out on the formatted data through a rapid analog-to-digital conversion algorithm, and coding state data is generated;
And carrying out statistical feature extraction on the coding state data to generate state feature data.
With reference to the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect of the present invention, the performing statistical feature extraction on the encoded state data to generate state feature data includes:
performing numerical conversion on the coded state data to generate numerical state data;
performing sliding window processing on the numerical state data through a feature extraction algorithm to generate corresponding time sequence features;
and determining a feature data dimension through the time sequence feature, and generating state feature data through the feature data dimension.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the generating a state feature vector by performing matrix conversion on the state feature data by using a preset singular value decomposition algorithm includes:
analyzing the state characteristic data in a matrix form to determine a target matrix form;
generating a matrix of the state characteristic data based on the target matrix form to obtain a matrix to be processed;
performing singular value decomposition on the matrix to be processed through the singular value decomposition algorithm to obtain a decomposition data set, wherein the decomposition data set comprises: singular values, left singular vectors and right singular vectors;
Performing principal component analysis on the decomposed data set through a principal component analysis algorithm to determine corresponding target principal component data;
and performing dimension reduction processing on the matrix to be processed through the target principal component data to generate a state feature vector.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, inputting the state feature vector into a preset abnormal state analysis model to perform equipment abnormality analysis, generating a state analysis result, and performing coping strategy analysis according to the state analysis result, to obtain a target strategy, where the method includes:
inputting the state feature vector into the abnormal state analysis model for time series data segmentation to generate time series data of a plurality of intervals;
respectively carrying out event feature matching on the time sequence data of each interval, and determining a plurality of target event features;
and carrying out equipment anomaly analysis on each target event characteristic to generate a state analysis result, and carrying out coping strategy analysis through the state analysis result to obtain a target strategy.
A second aspect of the present invention provides a plant monitoring apparatus, including:
The matching module is used for matching the configuration parameters of the target equipment based on a preset equipment management system, generating equipment configuration parameters and carrying out parameter configuration on the target equipment through the equipment configuration parameters;
the connection module is used for establishing connection between a data acquisition tool in the equipment management system and an IO collector in the target equipment, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment operation state data;
the extraction module is used for carrying out analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and carrying out statistic feature extraction on the coding state data to generate state feature data;
the conversion module is used for carrying out matrix conversion on the state characteristic data through a preset singular value decomposition algorithm to generate a state characteristic vector;
the analysis module is used for inputting the state feature vector into a preset abnormal state analysis model to perform equipment abnormality analysis, generating a state analysis result, and performing coping strategy analysis through the state analysis result to obtain a target strategy.
A third aspect of the present invention provides a plant monitoring device comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the plant monitoring device to perform the plant monitoring method described above.
A fourth aspect of the present invention provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the plant monitoring method described above.
In the embodiment of the invention, the configuration parameters of the target equipment are matched based on a preset equipment management system, equipment configuration parameters are generated, and the parameter configuration is carried out on the target equipment through the equipment configuration parameters; establishing connection between a data acquisition tool in the equipment management system and an IO collector in the target equipment, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment operation state data; performing analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and performing statistic feature extraction on the coding state data to generate state feature data; converting the state characteristic data matrix by a preset singular value decomposition algorithm to generate a state characteristic vector; the state feature vector is input into a preset abnormal state analysis model to perform equipment abnormality analysis, a state analysis result is generated, and coping strategy analysis is performed through the state analysis result to obtain a target strategy. The operation efficiency and the production quality of the equipment can be improved. By analyzing and optimizing the state feature vector of the equipment, the operation control strategy of the equipment can be improved in a targeted manner, and the operation efficiency and the production quality of the equipment are improved. Through realizing intelligent control and the management of equipment, can improve holistic production efficiency of enterprise and product quality, and then promote the competitiveness of enterprise, through configurable and the automation characteristic of system, can bring convenience and simplification for the configuration and the maintenance process of equipment, reduce the work load and the cost of enterprise.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a monitoring method for plant equipment in an embodiment of the present invention;
FIG. 2 is a flow chart of feedback data acquisition of an IO collector by a data acquisition tool in an embodiment of the present invention;
FIG. 3 is a flowchart of performing analog-to-digital conversion on equipment operation status data and equipment configuration parameters according to an embodiment of the present invention;
FIG. 4 is a flowchart of the statistical feature extraction of the encoded status data according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a plant monitoring apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an embodiment of a plant monitoring device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a workshop equipment monitoring method, a workshop equipment monitoring device, workshop equipment monitoring equipment and a storage medium, which are used for aaaa. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below with reference to fig. 1, where an embodiment of a monitoring method for a plant device in an embodiment of the present invention includes:
s101, performing configuration parameter matching on target equipment based on a preset equipment management system, generating equipment configuration parameters, and performing parameter configuration on the target equipment through the equipment configuration parameters;
it is to be understood that the execution body of the present invention may be a plant equipment monitoring device, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
The device management system is a professional software system for managing and monitoring devices, and can uniformly manage and monitor all devices in factories or enterprises. In the workshop equipment monitoring method, the function of the equipment management system is very important because the equipment management system can carry out effective parameter configuration on target equipment through a preset parameter corresponding relation and complete equipment monitoring and management. When the server configures the device, the device identification analysis needs to be performed to acquire the device identification of the target device. The device identifier is an important mark used by the device management system to distinguish different devices, and the device can be quickly identified and parameters can be configured through the device identifier. For example, a press in a factory has a unique equipment identity in an equipment management system. And the server analyzes the equipment type of the target equipment through the equipment identifier to acquire the equipment type of the target equipment. The parameter configuration modes of different types of equipment are different, so that corresponding parameter configuration is required according to the equipment type. For example, in different workshops in a factory, the type of machine tool used may be different, and the corresponding parameter settings may be different for different machine tool types. And the server performs parameter corresponding relation matching in the equipment management system according to different equipment types and parameter corresponding relations. This process looks at the mapping between specific device types and device parameters in the factory. For example, the types of parameters to be set in a machine tool may be many, but since not all parameters are required for different types of machine tools, the corresponding parameter settings need to be determined according to the parameter correspondence. After the corresponding relation of the parameters is determined, the server constructs the corresponding parameter mapping relation through the parameter mapping relation and generates equipment configuration parameters. In the process, the device management system completes parameter setting of the target device through a preset parameter configuration template and generates corresponding device configuration parameters. For example, the operation mode of the compressor may be controlled with a pressure setting, and thus it is necessary to match the pressure parameter or the like with the equipment configuration parameter of the compressor through a parameter map. After the device configuration parameters are completed, the server applies the generated device configuration parameters to the target device to complete the parameter configuration of the target device. For example, according to intelligent equipment configuration parameters generated by the equipment management system, the parameters of the injection molding machine are configured so as to control the opening and closing time of the injection molding machine. By configuration and monitoring of the device management system, the use efficiency and the production efficiency of the device can be greatly improved.
S102, a data acquisition tool in the equipment management system is connected with an IO collector in the target equipment, and feedback data acquisition is carried out on the IO collector through the data acquisition tool to generate equipment running state data;
specifically, the server collects the communication protocol of the IO collector to obtain the communication protocol of the target equipment. The communication protocol is a communication rule and standard for information transmission and data exchange among devices, and the communication protocol is determined to be the first step of data acquisition, and the data acquisition modes caused by different communication protocols are different. For example, during the production process of a factory, each machine may run different programs with different operating parameters and modes, so that different types of IO collectors need to be subjected to communication protocol collection. The server carries out protocol synchronous configuration on the data acquisition tool according to the communication protocol, and meanwhile establishes connection between the data acquisition tool and the IO collector. The device management system can preset various communication protocols and corresponding data acquisition tools, and can select an adaptive data acquisition mode according to the communication protocol of the target device. The server sends a data acquisition request to the IO collector through the data acquisition tool, and receives feedback data sent by the IO collector. The acquisition request can be aimed at each interface of the equipment, and the data information and the running state of each component of the equipment can be obtained through feedback data. For example, in a plant equipment monitoring system, the data acquisition request for the press may include temperature data, pressure data, and the like. Through data acquisition and processing, the running state of the equipment can be obtained, the performance and abnormal conditions of the equipment are analyzed, and the possible problems are found and solved in time. The server generates device operating state data by the feedback data. The equipment operation state data is important supporting data for monitoring and analyzing the equipment operation state, can reflect the performance, stability and abnormal conditions of the equipment, and helps to realize complete monitoring and management of the equipment and the production process. For example, on a production line of a factory, the condition of each machine can be monitored and checked by collecting operation state data, and the production mode can be timely adjusted, so that the production efficiency and the production quality are maintained.
S103, performing analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and performing statistic feature extraction on the coding state data to generate state feature data;
specifically, the server performs data combination on the equipment running state data and the equipment configuration parameter data to generate data to be processed. The equipment operation state data and the equipment configuration parameter data are important data for monitoring the equipment operation state, and are required to be effectively integrated and processed to generate state data to be processed. For example, in monitoring a press, it is necessary to integrate and process data such as temperature and pressure to generate state data to be processed. And the server performs floating point type data screening on the data to be processed to obtain floating point type data. Floating point data refers to values containing decimal numbers that are of great significance for device state analysis. For example, in an injection molding manufacturing plant, for analysis of injection molded cruise control module data, it is desirable to select floating point type data associated with a particular performance in order to better characterize the associated state. And the server performs formatting processing on floating point type data in the data to be processed to obtain formatted data. The formatting process is to convert floating point type data into data of a specific format so as to perform processing and analog-to-digital conversion of the data. For example, in the device monitoring, the analog data generated by the device sensor needs to be formatted and converted, and integrated into a preset data model, so as to facilitate the subsequent analog-digital conversion process. The server performs analog-to-digital conversion on the formatted data through a fast analog-to-digital conversion algorithm to generate encoded state data. Analog-to-digital conversion refers to converting formatted data into integer data within a particular range by calculation. For example, in press monitoring, it is necessary to convert successive digital pressure values into corresponding integer representations, mapping the range of pressure values into a range of 0 to a preselected integer. And the server performs statistic feature extraction on the encoded state data to generate state feature data. In the statistical feature extraction process, corresponding state feature data is required to be extracted according to a preset model so as to perform anomaly detection and state analysis. For example, in the monitoring of an injection molding cruise control module, statistical analysis and feature extraction of data are required to extract state feature data related to engine load, rotational speed, etc. in order to achieve the adaptive performance of the cruise control module.
S104, converting the state characteristic data matrix through a preset singular value decomposition algorithm to generate a state characteristic vector;
specifically, the server analyzes the state characteristic data in a matrix form and determines a target matrix form. The target matrix may reflect the correlation and change rule of the device state, and different target matrix forms may reflect different state characteristic data. For example, in machine equipment monitoring at a factory, data from all the equipment can be collected into a matrix to form a target matrix with correlation and change rules. And the server generates a matrix for the state characteristic data based on the target matrix form to acquire a matrix to be processed. The matrix to be processed refers to a matrix reflecting the running state data of the device, and the data can be split into a plurality of feature vectors in the matrix to be processed. For example, in monitoring a press, state characteristic data such as pressure and temperature generated by a plurality of sensors are combined and combined to generate a matrix to be processed. The server carries out singular value decomposition on the matrix to be processed through a singular value decomposition algorithm to obtain a decomposition data set, wherein the decomposition data set comprises singular values, left singular vectors and right singular vectors. Singular value decomposition is a very commonly used mathematical method that can split a matrix into parts, where the singular values reflect the importance of the matrix and the left and right singular vectors reflect the internal structure of the matrix. For example, in machine equipment monitoring, a matrix to be processed may be decomposed into a combination of a plurality of singular values, left singular vectors, and right singular vectors by a singular value decomposition method. And the server performs principal component analysis on the decomposed data set through a principal component analysis algorithm to determine corresponding target principal component data. Principal component analysis is a method of identifying correlations between variables by transforming coordinate axes, which maximizes the variance of data and regression analysis results. Only the first N main components with the maximum variance are reserved, so that the information of the data can be reserved to the greatest extent. For example, after principal component analysis is performed on the singular value, the left singular vector, and the right singular vector of the matrix to be processed, target principal component data can be obtained. And the server performs dimension reduction processing on the matrix to be processed through the target principal component data to generate a state feature vector. The dimension reduction processing is to process the high-dimension data into low-dimension data, so that the data can be conveniently further processed and analyzed. For example, in injection molding manufacturing plant machine monitoring, the matrix to be processed may be reduced in dimension by a principal component analysis method to obtain a desired state feature vector.
S105, inputting the state feature vector into a preset abnormal state analysis model to perform equipment abnormality analysis, generating a state analysis result, and performing coping strategy analysis through the state analysis result to obtain a target strategy.
Specifically, the server inputs the state feature vector into the abnormal state analysis model to perform time-series data division, and generates time-series data of a plurality of sections. The time series data segmentation is to decompose continuous time series data into a plurality of relatively independent state data, so as to facilitate the checking of the change trend and abnormal condition of the equipment state. For example, in a machine equipment monitoring system, state characteristic data of a plurality of equipment can be input into an abnormal state analysis model for segmentation, so that a plurality of independent time series data are generated, and analysis and comparison of the equipment are facilitated. The server performs event feature matching on the time series data of each interval, and determines a plurality of target event features. Event feature matching is to more accurately analyze and describe device state feature data. By identifying and matching event features of the time series data, state change trends and abnormal conditions of the equipment and relevant feature data of the abnormal conditions can be obtained. For example, in the differential equipment monitoring of an injection molding production line, by performing event feature matching on time series data of equipment, data features related to the operation of the production line can be obtained, so that abnormal conditions of the equipment can be judged. The server performs equipment anomaly analysis on each target event feature, generates a state analysis result, and performs coping strategy analysis through the state analysis result to obtain a target strategy. The equipment abnormality analysis refers to finding out the problem by carrying out deep analysis on the abnormal condition after confirming that the equipment has the abnormal condition so as to help to formulate a more effective coping strategy. According to the data and the analysis method provided by the abnormal state analysis model, the abnormal condition of the equipment can be judged and the solution strategy can be identified by analyzing the state characteristic data, the characteristics of the abnormal event and the corresponding context information. For example, in the analysis of abnormal states of a production line of an injection molding manufacturing plant, event feature matching may be performed on time series data of equipment, and an abnormal analysis may be performed on each target event feature to identify a cause of failure of the equipment and a corresponding resolution policy.
In the embodiment of the invention, the configuration parameters of the target equipment are matched based on a preset equipment management system, equipment configuration parameters are generated, and the parameter configuration is carried out on the target equipment through the equipment configuration parameters; establishing connection between a data acquisition tool in the equipment management system and an IO collector in the target equipment, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment running state data; performing analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and performing statistic feature extraction on the coding state data to generate state feature data; converting the state characteristic data matrix by a preset singular value decomposition algorithm to generate a state characteristic vector; the method comprises the steps of inputting a state feature vector into a preset abnormal state analysis model to perform equipment abnormality analysis, generating a state analysis result, and performing coping strategy analysis through the state analysis result to obtain a target strategy. The operation efficiency and the production quality of the equipment can be improved. By analyzing and optimizing the state feature vector of the equipment, the operation control strategy of the equipment can be improved in a targeted manner, and the operation efficiency and the production quality of the equipment are improved. Through realizing intelligent control and the management of equipment, can improve holistic production efficiency of enterprise and product quality, and then promote the competitiveness of enterprise, through configurable and the automation characteristic of system, can bring convenience and simplification for the configuration and the maintenance process of equipment, reduce the work load and the cost of enterprise.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Performing equipment identification analysis on the target equipment to obtain the equipment identification of the target equipment;
(2) Analyzing the equipment type of the target equipment through the equipment identifier to acquire the equipment type of the target equipment;
(3) Based on the equipment type, carrying out parameter corresponding relation matching on the target equipment through an equipment management system, and determining a corresponding parameter corresponding relation;
(4) Constructing a parameter mapping relation for the target equipment through the parameter corresponding relation, and generating the parameter mapping relation;
(5) Based on the parameter mapping relation, performing configuration parameter mapping on the target equipment through the equipment management system, generating equipment configuration parameters, and performing parameter configuration on the target equipment through the equipment configuration parameters.
Specifically, the server performs device identification analysis on the target device to obtain the device identification of the target device. The device identification is an identifier for distinguishing different devices, and can be determined by various forms such as a device name, a model number, a serial number, and the like. For example, in an injection molding manufacturing plant, the device identification may be determined based on the names and model numbers of the machine components and sensors. And the server analyzes the equipment type of the target equipment through the equipment identifier to acquire the equipment type of the target equipment. The device type refers to a collection of devices with similar characteristics and capabilities, and can be classified according to device structure, function, production efficiency, and specific requirements. For example, in the above example, the device type of the corresponding device may be determined according to the information of the device identification. And the server performs parameter corresponding relation matching on the target equipment through the equipment management system based on the equipment type, and determines the corresponding parameter corresponding relation. The parameter correspondence refers to correspondence between various parameters of the device. By analyzing and comparing parameters of different devices, the parameter configuration can be more accurate and efficient. For example, after the device type of the corresponding device is defined, the corresponding relation of parameters suitable for the device type can be searched through a database of the device management system. And the server builds the parameter mapping relation of the target equipment through the parameter corresponding relation to generate the parameter mapping relation. The parameter mapping relationship refers to a corresponding relationship and a mapping relationship between various parameters of the device, for example, an output voltage of the temperature sensor corresponds to a certain temperature value. The data acquisition and transmission can be conveniently carried out by defining and standardizing the mapping relation of the parameters. For example, after the parameter correspondence relation of the corresponding device type is determined, the parameter mapping relation can be constructed in the device management system. The server carries out configuration parameter mapping on the target equipment based on the parameter mapping relation and the equipment management system, generates equipment configuration parameters, and carries out parameter configuration on the target equipment through the equipment configuration parameters. The device configuration parameters are key parameters set by the management system for device parameter configuration. For example, in an injection molding process, a particular machine may be parameter configured via equipment configuration parameters, thereby allowing the equipment to operate in a more efficient and low-cost state.
In a specific embodiment, as shown in fig. 2, the process of executing step S102 may specifically include the following steps:
s201, collecting a communication protocol of the IO collector to obtain the communication protocol of the target equipment;
s202, carrying out protocol synchronous configuration on a data acquisition tool based on a communication protocol, and simultaneously, establishing connection between the data acquisition tool and an IO collector;
s203, sending a data acquisition request to the IO collector through a data acquisition tool, receiving feedback data sent by the IO collector, and generating equipment running state data through the feedback data.
Specifically, the server collects the communication protocol of the IO collector to obtain the communication protocol of the target equipment. The communication protocol refers to the specification followed by the device when communicating data. The communication protocols used by different devices may vary, such as Mod Bus, CAN, profibus, etc. By collecting the communication protocol of the IO collector, the correct communication protocol adopted by the target equipment can be obtained, and subsequent data collection and transmission are facilitated. The server carries out protocol synchronous configuration on the data acquisition tool based on the communication protocol, and meanwhile, establishes connection between the data acquisition tool and the IO collector. The data acquisition tool is a software tool for realizing data acquisition of the device. After setting up the data acquisition tool according to the communication protocol of the device, the acquisition tool needs to be connected with the IO collector so as to ensure the stability and accuracy of data acquisition. For example, after the communication protocol of the device is acquired, corresponding parameters may be set in the data acquisition tool, and after the parameters are set, connection is established with the IO collector. The server sends a data acquisition request to the IO collector through the data acquisition tool, receives feedback data sent by the IO collector, and generates equipment running state data through the feedback data. The data acquisition request refers to a data acquisition request initiated to the IO collector by the data acquisition tool. The data acquisition request may include information such as an indicator and a time interval that the device needs to feed back. And the IO collector correspondingly initiates collection after receiving the request and returns corresponding collection data. The data acquisition tool receives the acquired data, processes the acquired data, stores the acquired data and converts the acquired data into equipment operation state data. For example, during the process of collection on the production line, the data collection tool may collect aspects such as equipment temperature, vibration, sound, etc., and generate corresponding equipment time series data through the data collection tool, so as to perform equipment state analysis and abnormality diagnosis.
In a specific embodiment, as shown in fig. 3, the process of executing step S103 may specifically include the following steps:
s301, carrying out data combination on equipment operation state data and equipment configuration parameter data to generate data to be processed;
s302, floating point type data screening is carried out on data to be processed to obtain floating point type data;
s303, formatting floating point type data in the data to be processed to obtain formatted data;
s304, carrying out analog-to-digital conversion on the formatted data through a rapid analog-to-digital conversion algorithm to generate encoded state data;
s305, carrying out statistical feature extraction on the coding state data to generate state feature data.
Specifically, the server performs data combination on the equipment running state data and the equipment configuration parameter data to generate data to be processed. The device operating state data is the primary data source for device state monitoring and may be obtained by a data collection tool, while the device configuration parameter data is the parameter information required during device configuration. The two data can be combined to reflect the state and performance characteristics of the equipment more comprehensively and accurately, and a sufficient data basis is provided for subsequent state analysis. And the server performs floating point type data screening on the data to be processed to obtain floating point type data. Floating point data refers to numbers having a fractional portion. In data screening, floating point data may be further screened by some characteristic attribute to determine which values may be used for subsequent data processing and state analysis. For example, during device monitoring, floating point data screening can be performed on device state data according to preset thresholds and rules, so as to distinguish which values belong to a normal operating state of the device and which values belong to an abnormal state. And the server performs formatting processing on floating point type data in the data to be processed to obtain formatted data. During formatting, the elements such as the decimal part, the leading zero, the sign bit and the like in the data need to be subjected to unified normalization processing so as to ensure that the subsequent calculation and analysis processes can be normally performed. For example, in data processing, output decimal places of the device sensors may be formatted to ensure accuracy and validity of the data. The server performs analog-to-digital conversion on the formatted data through a fast analog-to-digital conversion algorithm to generate encoded state data. The fast analog-to-digital conversion algorithm is an efficient method for converting continuous floating-point type data into discretized data. In performing analog-to-digital conversion, the level of conversion employed and the specific algorithm need to be determined. For example, in device monitoring, a tree-based fast analog-to-digital conversion algorithm may be employed to convert device state data into 0 and 1 sequences for subsequent analysis and processing. And the server performs statistic feature extraction on the encoded state data to generate state feature data. In the process of statistical feature extraction, representative feature data such as mean, variance, peak value and the like can be extracted from the statistical feature attributes of the state data, so that important references are provided for subsequent state analysis and anomaly detection. For example, in device monitoring and management, the health of a device may be identified and an abnormal situation may be quickly responded to by performing statistical feature extraction on the device operational status data.
In a specific embodiment, as shown in fig. 4, the process of executing step S305 may specifically include the following steps:
s401, performing numerical conversion on the coded state data to generate numerical state data;
s402, carrying out sliding window processing on the numerical type state data through a feature extraction algorithm to generate corresponding time sequence features;
s403, determining feature data dimension through time sequence features, and generating state feature data through the feature data dimension.
Specifically, the server performs a numerical conversion on the encoded state data to generate numerical state data. Numerical state data refers to a data type that converts encoded state data into real data. In data conversion, the coding state data needs to be subjected to proper conversion operation according to actual data requirements and problems so as to be convenient for subsequent data processing and analysis. For example, in device monitoring, a particular type of device state characteristic, such as temperature, pressure, current, etc., may be selected based on the subject and goal to convert the encoded state data into a corresponding real number value. And the server performs sliding window processing on the numerical type state data through a feature extraction algorithm to generate corresponding time sequence features. The time series data refers to a data type formed by arranging data acquired by a certain object according to time sequence, and can be used for describing the change trend in the time evolution process. A sliding window is a data processing method, which can divide data within a period of time in a sliding window manner to obtain corresponding time sequence features. For example, in device monitoring, a sliding window method may be used to divide device state data over a period of time into windows, and then feature extraction is performed on the data within each window to obtain time series features. The server determines feature data dimensions from the time series features and generates state feature data from the feature data dimensions. The feature data dimension refers to the dimension of representative feature data obtained by extracting and selecting the features of a certain object through a specific method. When determining the feature data dimension, reasonable feature extraction and screening are required according to feature selection strategies and data processing requirements. For example, in device monitoring, an appropriate feature selection strategy may be selected according to different monitoring metrics and objectives, and state feature data may be generated through feature data dimensions to describe the operating state and characteristics of the device.
In a specific embodiment, the process of executing step S104 may specifically include the following steps:
(1) Analyzing the state characteristic data in a matrix form to determine a target matrix form;
(2) Generating a matrix based on the state characteristic data in a target matrix form to obtain a matrix to be processed;
(3) Performing singular value decomposition on a matrix to be processed through a singular value decomposition algorithm to obtain a decomposition data set, wherein the decomposition data set comprises: singular values, left singular vectors and right singular vectors;
(4) Performing principal component analysis on the decomposed data set through a principal component analysis algorithm to determine corresponding target principal component data;
(5) And performing dimension reduction processing on the matrix to be processed through the target principal component data to generate a state feature vector.
Specifically, the server analyzes the state characteristic data in a matrix form and determines a target matrix form. Matrix form analysis is an effective method for analyzing and processing data by a matrix method, and a large amount of data can be represented in a matrix form in a centralized manner so as to facilitate subsequent processing and analysis. For example, in device monitoring, device status data may be converted into a matrix form to more intuitively and efficiently describe the status characteristics of the device. And the server generates a matrix for the state characteristic data based on the target matrix form to acquire a matrix to be processed. For the differences of the target matrix form and the data requirements, different generating methods and matrix operations can be adopted to acquire the matrix to be processed. For example, in device monitoring, device state data may be matrix combined and split in time series and in the dimension of the monitoring index to obtain corresponding target matrix form and matrix data to be processed. The server carries out singular value decomposition on the matrix to be processed through a singular value decomposition algorithm to obtain a decomposition data set, wherein the decomposition data set comprises: singular values, left singular vectors and right singular vectors. Singular value decomposition is a commonly used linear algebraic method, and a matrix can be decomposed into products of three matrices to obtain characteristic information of the matrix. For example, in the device monitoring, a singular value decomposition method may be used to decompose a matrix to be processed into three matrix products of singular values, left singular vectors and right singular vectors, so as to obtain feature information of the device state data. And the server performs principal component analysis on the decomposed data set through a principal component analysis algorithm to determine corresponding target principal component data. The principal component analysis is a common multivariate statistical analysis method, and converts a plurality of related variables into independent principal component variables by a linear transformation mode so as to perform dimension reduction processing and feature extraction on the original data. For example, in equipment monitoring, a principal component analysis method may be used to analyze a decomposition data set obtained by singular value decomposition to obtain principal component data of equipment state data. And the server performs dimension reduction processing on the matrix to be processed through the target principal component data to generate a state feature vector. The dimension reduction processing is an effective method for converting high-dimension data into low-dimension data, and can effectively reduce the calculation and storage cost and improve the data processing and analysis efficiency. In the equipment monitoring, the dimension reduction processing can be carried out on the matrix to be processed through the target principal component data, and the state feature vector is generated so as to obtain a finer and effective equipment state data description result.
In a specific embodiment, the process of executing step S105 may specifically include the following steps:
(1) Inputting the state feature vector into an abnormal state analysis model for time series data segmentation to generate time series data of a plurality of intervals;
(2) Respectively carrying out event feature matching on the time sequence data of each interval, and determining a plurality of target event features;
(3) And carrying out equipment anomaly analysis on each target event feature, generating a state analysis result, and carrying out coping strategy analysis through the state analysis result to obtain a target strategy.
Specifically, the server inputs the state feature vector into the abnormal state analysis model to perform time-series data division, and generates time-series data of a plurality of sections. The time series data segmentation is to decompose continuous time series data into a plurality of relatively independent state data, so as to facilitate the checking of the change trend and abnormal condition of the equipment state. For example, in a machine equipment monitoring system, state characteristic data of a plurality of equipment can be input into an abnormal state analysis model for segmentation, and a plurality of individual time series data can be generated so as to analyze the change trend and abnormal situation of the equipment state. The server performs event feature matching on the time series data of each interval, and determines a plurality of target event features. Event feature matching is to more accurately analyze and describe device state feature data. By identifying and matching event features of the time series data, state change trends and abnormal conditions of the equipment and relevant feature data of the abnormal conditions can be obtained. For example, in the monitoring of equipment in an injection molding production line, by performing event feature matching on time-series data of the equipment, data features related to the production process can be obtained, thereby judging abnormal conditions of the equipment. The server performs equipment anomaly analysis on each target event feature, generates a state analysis result, and performs coping strategy analysis through the state analysis result to obtain a target strategy. The equipment abnormality analysis refers to finding out the problem by carrying out deep analysis on the abnormal condition after confirming that the equipment has the abnormal condition so as to help to formulate a more effective coping strategy. According to the data and the analysis method provided by the abnormal state analysis model, the abnormal condition of the equipment can be judged and the solution strategy can be identified by analyzing the state characteristic data, the characteristics of the abnormal event and the corresponding context information. For example, in monitoring equipment in an injection molding manufacturing plant, event feature matching may be performed on time series data of the equipment, and anomaly analysis may be performed on each target event feature to identify a cause of failure of the equipment and a corresponding resolution strategy.
The method for monitoring the plant in the embodiment of the present invention is described above, and the plant monitoring device in the embodiment of the present invention is described below, referring to fig. 5, where an embodiment of the plant monitoring device in the embodiment of the present invention includes:
the matching module 501 is configured to match configuration parameters of a target device based on a preset device management system, generate device configuration parameters, and perform parameter configuration on the target device through the device configuration parameters;
the connection module 502 is configured to connect a data acquisition tool in the device management system with an IO collector in the target device, and perform feedback data acquisition on the IO collector through the data acquisition tool, so as to generate device operation state data;
an extracting module 503, configured to perform analog-to-digital conversion on the device running state data and the device configuration parameters, generate corresponding encoded state data, and perform statistical feature extraction on the encoded state data to generate state feature data;
the conversion module 504 is configured to perform matrix conversion on the state feature data by using a preset singular value decomposition algorithm, so as to generate a state feature vector;
The analysis module 505 is configured to input the state feature vector into a preset abnormal state analysis model to perform equipment abnormality analysis, generate a state analysis result, and perform coping strategy analysis according to the state analysis result to obtain a target strategy.
Matching configuration parameters of target equipment based on a preset equipment management system through cooperation of the components to generate equipment configuration parameters, and carrying out parameter configuration on the target equipment through the equipment configuration parameters; establishing connection between a data acquisition tool in the equipment management system and an IO collector in the target equipment, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment operation state data; performing analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and performing statistic feature extraction on the coding state data to generate state feature data; converting the state characteristic data matrix by a preset singular value decomposition algorithm to generate a state characteristic vector; the state feature vector is input into a preset abnormal state analysis model to perform equipment abnormality analysis, a state analysis result is generated, and coping strategy analysis is performed through the state analysis result to obtain a target strategy. The operation efficiency and the production quality of the equipment can be improved. By analyzing and optimizing the state feature vector of the equipment, the operation control strategy of the equipment can be improved in a targeted manner, and the operation efficiency and the production quality of the equipment are improved. Through realizing intelligent control and the management of equipment, can improve holistic production efficiency of enterprise and product quality, and then promote the competitiveness of enterprise, through configurable and the automation characteristic of system, can bring convenience and simplification for the configuration and the maintenance process of equipment, reduce the work load and the cost of enterprise.
The plant monitoring device in the embodiment of the present invention is described in detail above in terms of the modularized functional entity in fig. 5, and the plant monitoring device in the embodiment of the present invention is described in detail below in terms of hardware processing.
Fig. 6 is a schematic structural diagram of a plant monitoring device 600 according to an embodiment of the present invention, where the plant monitoring device 600 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. Wherein the memory 620 and the storage medium 630 may be transitory or persistent storage. The program stored on the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations for the plant monitoring device 600. Still further, the processor 610 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the shop floor device monitoring device 600.
The plant monitoring device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input/output interfaces 660, and/or one or more operating systems 631, such as Windows Serve, mac OS X, unix, linux, freeBSD, etc. It will be appreciated by those skilled in the art that the plant monitoring device configuration shown in fig. 6 is not limiting of the plant monitoring device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The invention also provides a workshop equipment monitoring device, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the workshop equipment monitoring method in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, in which instructions are stored which, when executed on a computer, cause the computer to perform the steps of the plant monitoring method.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random acceS memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The workshop equipment monitoring method is characterized by comprising the following steps of:
performing configuration parameter matching on target equipment based on a preset equipment management system, generating equipment configuration parameters, and performing parameter configuration on the target equipment through the equipment configuration parameters;
establishing connection between a data acquisition tool in the equipment management system and an IO collector in the target equipment, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment operation state data;
performing analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and performing statistic feature extraction on the coding state data to generate state feature data;
Converting the state characteristic data matrix by a preset singular value decomposition algorithm to generate a state characteristic vector;
and inputting the state feature vector into a preset abnormal state analysis model for equipment abnormality analysis, generating a state analysis result, and carrying out coping strategy analysis through the state analysis result to obtain a target strategy.
2. The plant equipment monitoring method according to claim 1, wherein the performing, based on a preset equipment management system, configuration parameter matching on a target equipment, generating equipment configuration parameters, and performing parameter configuration on the target equipment through the equipment configuration parameters includes:
performing equipment identification analysis on the target equipment to obtain equipment identification of the target equipment;
analyzing the equipment type of the target equipment through the equipment identifier to acquire the equipment type of the target equipment;
based on the equipment type, carrying out parameter corresponding relation matching on the target equipment through the equipment management system, and determining a corresponding parameter corresponding relation;
constructing a parameter mapping relation for the target equipment through the parameter corresponding relation, and generating a parameter mapping relation;
And carrying out configuration parameter mapping on the target equipment through the equipment management system based on the parameter mapping relation, generating equipment configuration parameters, and carrying out parameter configuration on the target equipment through the equipment configuration parameters.
3. The method for monitoring plant equipment according to claim 1, wherein the step of establishing a connection between a data collection tool in the equipment management system and an IO collector in the target equipment, and performing feedback data collection on the IO collector through the data collection tool, and generating equipment operation state data includes:
collecting a communication protocol of the IO collector to obtain the communication protocol of the target equipment;
carrying out protocol synchronous configuration on the data acquisition tool based on the communication protocol, and simultaneously establishing connection between the data acquisition tool and the IO collector;
and sending a data acquisition request to the IO collector through the data acquisition tool, receiving feedback data sent by the IO collector, and generating the equipment running state data through the feedback data.
4. The plant equipment monitoring method according to claim 1, wherein the performing analog-to-digital conversion on the equipment operation status data and the equipment configuration parameters to generate corresponding encoded status data, and performing statistical feature extraction on the encoded status data to generate status feature data includes:
Carrying out data combination on the equipment running state data and the equipment configuration parameter data to generate data to be processed;
floating point type data screening is carried out on the data to be processed to obtain floating point type data;
formatting the floating point data in the data to be processed to obtain formatted data;
analog-to-digital conversion is carried out on the formatted data through a rapid analog-to-digital conversion algorithm, and coding state data is generated;
and carrying out statistical feature extraction on the coding state data to generate state feature data.
5. The plant monitoring method according to claim 4, wherein the performing statistical feature extraction on the encoded status data to generate status feature data includes:
performing numerical conversion on the coded state data to generate numerical state data;
performing sliding window processing on the numerical state data through a feature extraction algorithm to generate corresponding time sequence features;
and determining a feature data dimension through the time sequence feature, and generating state feature data through the feature data dimension.
6. The plant monitoring method according to claim 1, wherein the generating a state feature vector by matrix-transforming the state feature data by a preset singular value decomposition algorithm includes:
Analyzing the state characteristic data in a matrix form to determine a target matrix form;
generating a matrix of the state characteristic data based on the target matrix form to obtain a matrix to be processed;
performing singular value decomposition on the matrix to be processed through the singular value decomposition algorithm to obtain a decomposition data set, wherein the decomposition data set comprises: singular values, left singular vectors and right singular vectors;
performing principal component analysis on the decomposed data set through a principal component analysis algorithm to determine corresponding target principal component data;
and performing dimension reduction processing on the matrix to be processed through the target principal component data to generate a state feature vector.
7. The plant equipment monitoring method according to claim 1, wherein the inputting the state feature vector into a preset abnormal state analysis model for equipment abnormality analysis, generating a state analysis result, and performing coping strategy analysis through the state analysis result to obtain a target strategy, includes:
inputting the state feature vector into the abnormal state analysis model for time series data segmentation to generate time series data of a plurality of intervals;
Respectively carrying out event feature matching on the time sequence data of each interval, and determining a plurality of target event features;
and carrying out equipment anomaly analysis on each target event characteristic to generate a state analysis result, and carrying out coping strategy analysis through the state analysis result to obtain a target strategy.
8. A plant monitoring device, characterized in that the plant monitoring device comprises:
the matching module is used for matching the configuration parameters of the target equipment based on a preset equipment management system, generating equipment configuration parameters and carrying out parameter configuration on the target equipment through the equipment configuration parameters;
the connection module is used for establishing connection between a data acquisition tool in the equipment management system and an IO collector in the target equipment, and carrying out feedback data acquisition on the IO collector through the data acquisition tool to generate equipment operation state data;
the extraction module is used for carrying out analog-to-digital conversion on the equipment running state data and the equipment configuration parameters to generate corresponding coding state data, and carrying out statistic feature extraction on the coding state data to generate state feature data;
The conversion module is used for carrying out matrix conversion on the state characteristic data through a preset singular value decomposition algorithm to generate a state characteristic vector;
the analysis module is used for inputting the state feature vector into a preset abnormal state analysis model to perform equipment abnormality analysis, generating a state analysis result, and performing coping strategy analysis through the state analysis result to obtain a target strategy.
9. A plant monitoring device, characterized in that the plant monitoring device comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invoking the instructions in the memory to cause the plant monitoring device to perform the plant monitoring method of any of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the shop equipment monitoring method of any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117151314A (en) * | 2023-11-01 | 2023-12-01 | 深圳市普朗医疗科技发展有限公司 | Production management method and related device of sodium hyaluronate |
| CN117527859A (en) * | 2024-01-04 | 2024-02-06 | 深圳市联特微电脑信息技术开发有限公司 | Equipment monitoring method and system based on industrial Internet |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111723850A (en) * | 2020-05-28 | 2020-09-29 | 国网河北省电力有限公司电力科学研究院 | Automatic verification equipment state evaluation method based on intelligent inspection system |
| CN113561656A (en) * | 2021-07-22 | 2021-10-29 | 江阴市欧莱特彩印有限公司 | Method, system and equipment for monitoring and tracing production process quality of printing workshop |
| CN115145505A (en) * | 2022-05-20 | 2022-10-04 | 深圳劲嘉集团股份有限公司 | Data intelligent acquisition and processing method, system and medium for printing equipment |
| CN115423289A (en) * | 2022-08-29 | 2022-12-02 | 广东鑫光智能系统有限公司 | Intelligent plate processing workshop data processing method and terminal |
| CN115576284A (en) * | 2022-10-20 | 2023-01-06 | 浙江链捷数字科技有限公司 | Clothing workshop intelligent management method and system |
-
2023
- 2023-06-21 CN CN202310737823.1A patent/CN116483015B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111723850A (en) * | 2020-05-28 | 2020-09-29 | 国网河北省电力有限公司电力科学研究院 | Automatic verification equipment state evaluation method based on intelligent inspection system |
| CN113561656A (en) * | 2021-07-22 | 2021-10-29 | 江阴市欧莱特彩印有限公司 | Method, system and equipment for monitoring and tracing production process quality of printing workshop |
| CN115145505A (en) * | 2022-05-20 | 2022-10-04 | 深圳劲嘉集团股份有限公司 | Data intelligent acquisition and processing method, system and medium for printing equipment |
| CN115423289A (en) * | 2022-08-29 | 2022-12-02 | 广东鑫光智能系统有限公司 | Intelligent plate processing workshop data processing method and terminal |
| CN115576284A (en) * | 2022-10-20 | 2023-01-06 | 浙江链捷数字科技有限公司 | Clothing workshop intelligent management method and system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117151314A (en) * | 2023-11-01 | 2023-12-01 | 深圳市普朗医疗科技发展有限公司 | Production management method and related device of sodium hyaluronate |
| CN117151314B (en) * | 2023-11-01 | 2024-04-05 | 深圳市普朗医疗科技发展有限公司 | Production management method and related device of sodium hyaluronate |
| CN117527859A (en) * | 2024-01-04 | 2024-02-06 | 深圳市联特微电脑信息技术开发有限公司 | Equipment monitoring method and system based on industrial Internet |
| CN117527859B (en) * | 2024-01-04 | 2024-03-19 | 深圳市联特微电脑信息技术开发有限公司 | Equipment monitoring method and system based on industrial Internet |
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