CN117611010A - Fuzzy comprehensive evaluation method for intelligent power distribution cabinet operation state based on twin model - Google Patents
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Abstract
The invention relates to a fuzzy comprehensive evaluation method of an intelligent power distribution cabinet running state based on a twin model, which belongs to the technical field of power transmission and transformation equipment monitoring and comprises the following steps: (1) acquiring operation state data of the intelligent power distribution cabinet; (2) performing virtual-real mapping on the acquired data by adopting a digital twin technology; (3) constructing a twin model of the running state of the power distribution cabinet; (4) solving the established twin model through an ant colony clustering algorithm, and gathering fault samples of the same type; (5) constructing an intelligent power distribution cabinet running state evaluation index system based on a twin model and weighting; (6) and outputting a comprehensive evaluation method of the intelligent power distribution cabinet running state based on the twin model. According to the fuzzy evaluation method, a fuzzy evaluation algorithm of the Hamacher operator is adopted, an intelligent power distribution cabinet operation state evaluation index system based on a twin model is established, the omission ratio and the monitoring response time of operation and maintenance of the intelligent power distribution cabinet are reduced, and the operation economy and reliability of a power distribution network are effectively improved.
Description
Technical Field
The invention belongs to the technical field of power transmission and transformation equipment monitoring, and particularly relates to a fuzzy comprehensive evaluation method for an intelligent power distribution cabinet running state based on a twin model.
Background
The power distribution cabinet is one of the most important equipment in the power distribution link of the power system, is more in configuration and wide in distribution in the power grid system, and is mainly responsible for switching on and off power lines, conveying power loads and cutting off fault lines and equipment. The running state of the power distribution network plays a great influence on the whole power system and the power distribution network. The power distribution cabinet works in a high-voltage and high-current state for a long time, and is easy to fail due to the self-sealing structure. In the running state of the power distribution cabinet, the factors influencing the stability and the safety of the power distribution cabinet are more, and the running state of the power distribution cabinet needs to be monitored in real time. Because the internal structure of the power distribution cabinet is complex, the number and the variety of the electrical elements are more, and the influence factors are complex, the monitoring has higher difficulty. The existing monitoring method has higher omission ratio in practical application, longer monitoring response time and can not meet the practical requirements.
The evaluation method for researching the operation state of the intelligent power distribution cabinet is an important link of reliable operation of the power distribution network, and due to the lack of a reasonable and effective operation state evaluation means, the maintenance working efficiency is low, the omission ratio is high, the quick and effective evaluation of the operation state of the intelligent power distribution cabinet can not be realized, and the reliability and the economy of power supply are difficult to realize. How to realize balance and coordination of economy and reliability and obtain the optimal comprehensive evaluation scheme of the running state of the intelligent power distribution cabinet is an important problem to be solved in the current running state monitoring of the intelligent power distribution cabinet.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a fuzzy comprehensive evaluation method for the operation state of an intelligent power distribution cabinet based on a twin model, which carries out virtual-real mapping on the operation state of the intelligent power distribution cabinet through a digital twin technology and constructs the twin model of the operation state of the intelligent power distribution cabinet comprehensively considering the randomness and diversity of faults of the intelligent power distribution cabinet; the ant colony algorithm is adopted to solve the model, the fuzzy evaluation algorithm of the Hamacher operator is adopted to evaluate the running state of the intelligent power distribution cabinet, the real-time evaluation of the health state of the intelligent power distribution cabinet is realized, and the omission ratio of fault monitoring is reduced.
In order to achieve the purpose, the invention adopts the following technical scheme that the method comprises the steps of carrying out virtual-real mapping on the operation state of the intelligent power distribution cabinet by a digital twin technology, and constructing a twin model of the operation state of the intelligent power distribution cabinet; solving the model by adopting an ant colony algorithm; and evaluating the operation state of the intelligent power distribution cabinet through a fuzzy evaluation algorithm of the Hamacher operator to obtain a fuzzy comprehensive evaluation method of the operation state of the intelligent power distribution cabinet based on the twin model.
The method comprises the following steps:
step 1) acquiring operation state data of the intelligent power distribution cabinet.
And 2) carrying out virtual-real mapping on the acquired data by adopting a digital twin technology.
And 3) constructing a twin model of the running state of the power distribution cabinet.
(1) And establishing a model according to the geometric data of the intelligent power distribution cabinet.
(2) And adding the attribute value corresponding to the intelligent power distribution cabinet to the geometric model.
(3) And establishing the connection between the intelligent power distribution cabinet operation state data signal and the twin model.
And 4) solving the established twin model through an ant colony clustering algorithm, and gathering fault samples of the same type.
And 5) constructing an intelligent power distribution cabinet running state evaluation index system based on the twin model and giving weight.
(1) And establishing an intelligent power distribution cabinet running state evaluation model based on the twin model.
(2) And determining a membership function to form a membership matrix.
(3) And determining the weights of all indexes by adopting a principal component analysis method, and forming a weight matrix.
(4) And carrying out fuzzy comprehensive evaluation on the operation state of the intelligent power distribution cabinet by adopting a Hamacher operator.
(5) And outputting an evaluation result.
And 6) outputting a comprehensive evaluation method of the operation state of the intelligent power distribution cabinet based on the twin model.
Further, the intelligent power distribution cabinet operation state data refer to temperature, humidity, voltage, current, abnormal time setting, equipment locking, parameter errors, opening and closing states of a circuit breaker and the like.
Further, the digital twin technology refers to that by establishing an entity mapping from the objective world to the virtual world, the whole life cycle of the corresponding entity in the target environment is reflected. The whole-course sensing, self-diagnosis and future prediction of the state of the physical entity object can be completed by means of simulation, emulation, data analysis and other processes, and the implementation control and comprehensive optimization of the related behavior of the entity object are always realized through the self-evolution and self-learning of the cross-reference propulsion model between the digital model and the entity model.
Further, the data interface refers to a transmission channel for the actual data of the operation state of the power distribution cabinet and the twin model.
Further, the ant colony clustering algorithm refers to that collected fault samples can be regarded as ant nodes in an ant colony, the samples are firstly divided into a plurality of classes, a clustering center is determined, deviation of the ant from each clustering center is calculated according to European lengths experienced by ants in the food searching process, the class of the sample is adjusted by the smallest deviation, the clustering center is updated finally, and the fault samples of the same type are gathered together.
Further, the intelligent power distribution cabinet operation state evaluation model based on the twin model is built, and the built process is as follows:
let the set of intelligent power distribution cabinet operation state information evaluation factors be u= { U1, U2, …, um }, where ui (i=1, 2, …, m) is data participating in equipment evaluation, and can be specifically classified into discrete type and continuous type. The discrete data refers to equipment status information such as equipment locking, abnormal time setting, parameter errors and the like. The continuous data refer to the temperature and humidity of the device, the load factor of the CPU, the working voltage, the working current and the like. The interval type standardization is adopted to convert the data into an evaluation index, and the specific formula is as follows:
wherein x is the actual value of the data, [ x ] n ，x m ]For the optimal running interval of the data, [ x ] min ，x max ]An interval is required for the operation of the data. Evaluation knot provided with intelligent power distribution cabinetThe result set is v= { V1, V2, …, vn }, where vi (i=1, 2, …, n) is the operation state of the device, and specifically, the set may be divided into 4 states of normal, attention, abnormality, and failure, and V1, V2, V3, and V4 respectively.
Further, the membership function refers to a function for reflecting the influence degree of the operation data of the intelligent power distribution cabinet on the operation state of the intelligent power distribution cabinet. For discrete state data, the value range is {0,1}, and the discrete state data has definite equipment running states and corresponds to the discrete state data, so that the membership function can be simplified to be a function other than 0, namely 1. For continuous data, because the data are numerous in variety, and direct influence of each data on the running state of the equipment is difficult to visually see, the running parameters of the equipment are obtained according to expert experience, manufacturer opinion and equipment statistics in extreme cases.
The membership matrix refers to a membership set obtained by carrying M groups of data into corresponding membership functions and then calculating, and the membership set is used as a column, so that a membership matrix R is obtained, wherein the membership matrix R is shown in the formula:wherein r is nm The result obtained for the membership function.
The principal component analysis method utilizes the relation among indexes to fuse a plurality of indexes into a few indexes, and replaces the original indexes with a few comprehensive indexes under the condition of keeping most of information, so that the interrelationship among the original indexes can be eliminated, and the workload of the weight giving of the indexes is reduced.
Further, the weight matrix refers to: calculating weights by adopting a 1-9 scale analytic hierarchy process, comparing the influence degree of the operation data of the power distribution cabinet on a certain operation state vk in pairs, and if the operation data u is i And u j To a considerable extent on the operating state, z ij =1, if data u i Ratio u j To the greatest extent, z ij =1/9, zji=9, thereby yielding a judgment matrix Z, i.e.:and then carrying out normalization processing on the judgment matrix Z to obtain a weight matrix:wherein a is nm Refers to the magnitude of the impact of each data on different operating states.
Further, the fuzzy comprehensive evaluation means that when the weight matrix a and the membership matrix R are known, fuzzy comprehensive processing can be performed according to the formula b=a° R, where ° is a Hamacher operator, and b= { B1, B2, …, bn } is a fuzzy comprehensive evaluation set.
Hamacher operator refers to a fuzzy synthesis operator containing parameters, and the expression is as follows:
wherein, gamma is a value in the range of (0, ++ infinity) range is used for the control of the temperature of the liquid crystal display device, and x is 0,1 and y is 0, 1.
Compared with the prior art, the invention has the beneficial effects.
1. The invention discloses a fuzzy comprehensive evaluation method for the running state of an intelligent power distribution cabinet based on a twin model, which can realize the optimization of the randomness and diversity of faults of the intelligent power distribution cabinet. The traditional intelligent power distribution cabinet operation state evaluation method is high in omission rate due to the lack of reasonable and effective operation state evaluation means, and long in monitoring response time, so that effective evaluation of intelligent power distribution on the cabinet operation state cannot be realized quickly and effectively, and reliability and economy of power supply are difficult to realize. According to the invention, by constructing the intelligent power distribution cabinet operation state evaluation index system based on the twin model, the omission ratio and the monitoring response time of the intelligent power distribution cabinet operation monitoring are reduced, and the reliability and the economy of the power distribution network operation are improved.
2. The invention is easy to implement. The intelligent power distribution cabinet operation state twin model comprehensively considering the randomness and the diversity of faults of the intelligent power distribution cabinet is constructed on the basis of the traditional power distribution cabinet operation state monitoring, so that the omission ratio of the intelligent power distribution cabinet operation monitoring is lower, and the response time is shorter. Is easy to be controlled.
3. The invention is convenient for commercialized development. With the increase of the application of intelligent power distribution cabinets in the power distribution network, the development of the fuzzy comprehensive evaluation method of the running state inevitably has larger requirements, and the method has better commercial development prospect.
Drawings
The invention is further described below with reference to the drawings and the detailed description. The scope of the present invention is not limited to the following description.
FIG. 1 is a flow chart of a fuzzy comprehensive evaluation method of the operation state of an intelligent power distribution cabinet based on a twin model.
Fig. 2 is a flow chart for fuzzy evaluation of the operation state of the power distribution cabinet.
Fig. 3 is a flow chart of operational status monitoring.
Detailed Description
As shown in fig. 1, fig. 1 is a general flow chart, and the invention performs virtual-real mapping on the operation state of an intelligent power distribution cabinet by a digital twin technology to construct a twin model of the operation state of the intelligent power distribution cabinet; the randomness and diversity of the faults of the intelligent power distribution cabinet are considered to realize fuzzy comprehensive evaluation of the running state of the intelligent power distribution cabinet, which is basically different from other methods.
The specific technical scheme is as follows:
step 1) acquiring operation state data of the intelligent power distribution cabinet.
And 2) carrying out virtual-real mapping on the acquired data by adopting a digital twin technology.
And 3) constructing a twin model of the running state of the power distribution cabinet.
(1) And establishing a model according to the geometric data of the intelligent power distribution cabinet.
(2) And adding the attribute value corresponding to the intelligent power distribution cabinet to the geometric model.
(3) And establishing the connection between the intelligent power distribution cabinet operation state data signal and the twin model.
And 4) solving the established twin model through an ant colony clustering algorithm, and gathering fault samples of the same type.
And 5) constructing an intelligent power distribution cabinet running state evaluation index system based on the twin model and giving weight.
(1) And establishing an intelligent power distribution cabinet running state evaluation model based on the twin model.
(2) And determining a membership function to form a membership matrix.
(3) And determining the weights of all indexes by adopting a principal component analysis method, and forming a weight matrix.
(4) And carrying out fuzzy comprehensive evaluation on the operation state of the intelligent power distribution cabinet by adopting a Hamacher operator.
(5) And outputting an evaluation result.
And 6) outputting a comprehensive evaluation method of the operation state of the intelligent power distribution cabinet based on the twin model.
Preferably, the intelligent power distribution cabinet operation state data refer to temperature, humidity, voltage, current, time synchronization abnormality, equipment locking, parameter error, breaker opening and closing state and the like.
Preferably, the digital twin technique refers to reflecting the whole life cycle of the corresponding entity in the target environment by establishing an entity mapping of the objective world to the virtual world. The whole-course sensing, self-diagnosis and future prediction of the state of the physical entity object can be completed by means of simulation, emulation, data analysis and other processes, and the implementation control and comprehensive optimization of the related behavior of the entity object are always realized through the self-evolution and self-learning of the cross-reference propulsion model between the digital model and the entity model.
Preferably, the data interface refers to a transmission channel for the actual data of the operation state of the power distribution cabinet associated with the twin model.
Preferably, the ant colony clustering algorithm refers to that the collected fault samples can be regarded as ant nodes in the ant colony, the samples are firstly divided into a plurality of classes, a clustering center is determined, then the deviation of the ant from each clustering center is calculated according to European lengths experienced by ants in the food searching process, the class of the sample is adjusted by selecting the smallest deviation, the clustering center is updated finally, and the fault samples of the same type are gathered together.
Preferably, the intelligent power distribution cabinet operation state evaluation model based on the twin model is built, and the built process is as follows:
let the set of intelligent power distribution cabinet operation state information evaluation factors be u= { U1, U2, …, um }, where ui (i=1, 2, …, m) is data participating in equipment evaluation, and can be specifically classified into discrete type and continuous type. The discrete data refers to equipment status information such as equipment locking, abnormal time setting, parameter errors and the like. The continuous data refer to the temperature and humidity of the device, the load factor of the CPU, the working voltage, the working current and the like. The interval type standardization is adopted to convert the data into an evaluation index, and the specific formula is as follows:
wherein x is the actual value of the data, [ x ] n ，x m ]For the optimal running interval of the data, [ x ] min ，x max ]An interval is required for the operation of the data. Let the evaluation result set of the intelligent power distribution cabinet be v= { V1, V2, …, vn }, where vi (i=1, 2, …, n) is the operation state of the device, and specifically, the evaluation result set can be divided into 4 states of normal, attention, abnormal and fault, and V1, V2, V3 and V4 are respectively corresponding to each other.
The membership function refers to a function used for reflecting the influence degree of the operation data of the intelligent power distribution cabinet on the operation state of the intelligent power distribution cabinet. For discrete state data, the value range is {0,1}, and the discrete state data has definite equipment running states and corresponds to the discrete state data, so that the membership function can be simplified to be a function other than 0, namely 1. For continuous data, because the data are numerous in variety, and direct influence of each data on the running state of the equipment is difficult to visually see, the running parameters of the equipment are obtained according to expert experience, manufacturer opinion and equipment statistics in extreme cases.
The membership matrix refers to a membership set obtained by carrying M groups of data into corresponding membership functions and then calculating, and the membership set is used as a column, so that a membership matrix R is obtained, wherein the membership matrix R is shown in the formula:wherein r is nm The result obtained for the membership function.
The principal component analysis method utilizes the relation among indexes to fuse a plurality of indexes into a few indexes, and replaces the original indexes with a few comprehensive indexes under the condition of keeping most of information, so that the interrelationship among the original indexes can be eliminated, and the workload of the weight giving of the indexes is reduced.
The weight matrix refers to: calculating weights by adopting a 1-9 scale analytic hierarchy process, comparing the influence degree of the operation data of the power distribution cabinet on a certain operation state vk in pairs, and if the operation data u is i And u j To a considerable extent on the operating state, z ij =1, if data u i Ratio u j To the greatest extent, z ij =1/9, zji=9, thereby yielding a judgment matrix Z, i.e.:and then carrying out normalization processing on the judgment matrix Z to obtain a weight matrix:wherein a is nm Refers to the magnitude of the impact of each data on different operating states.
The fuzzy comprehensive evaluation refers to that when the weight matrix a and the membership matrix R are known, fuzzy comprehensive processing can be performed according to the formula b=a° R, where ° is a Hamacher operator, and b= { B1, B2, …, bn } is a fuzzy comprehensive evaluation set.
Hamacher operator refers to a fuzzy synthesis operator containing parameters, and the expression is as follows:
wherein, gamma is a value in the range of (0, ++ infinity) range is used for the control of the temperature of the liquid crystal display device, and x is 0,1 and y is 0, 1.
As shown in FIG. 2, firstly, an intelligent power distribution cabinet fuzzy evaluation model is established, a membership matrix is obtained according to expert experience and manufacturer suggestions, a 1-9 scale analytic hierarchy process is adopted to calculate a weight matrix, and a Hamacher operator is used for fuzzy comprehensive processing of the membership matrix and the weight matrix, so that fuzzy comprehensive evaluation of the operation state of the intelligent power distribution cabinet is realized.
The invention provides a reliable and effective fuzzy evaluation method for the running state of an intelligent power distribution cabinet. The fuzzy evaluation method provided by the invention considers the randomness and diversity of faults, can greatly reduce the omission ratio of fault monitoring and improves the working efficiency of monitoring. Technical basis and practical method are provided for realizing the accuracy and rapidness of the evaluation of the running state of the power distribution cabinet.
It should be understood that the foregoing detailed description of the present invention is provided for illustration only and is not limited to the technical solutions described in the embodiments of the present invention, and those skilled in the art should understand that the present invention may be modified or substituted for the same technical effects; as long as the use requirement is met, the invention is within the protection scope of the invention.
Claims (10)
1. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model is characterized by comprising the following steps of:
acquiring operation state data of the intelligent power distribution cabinet;
performing virtual-real mapping on the acquired running state data by adopting a digital twin technology;
constructing a twin model of the running state of the power distribution cabinet;
solving the established twin model through an ant colony clustering algorithm, and gathering fault samples of the same type;
and constructing an intelligent power distribution cabinet running state evaluation index system based on the twin model and weighting.
2. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 1, wherein the method comprises the following steps:
the intelligent power distribution cabinet operation state data comprise temperature, humidity, voltage, current, time synchronization abnormality, equipment locking, parameter error and breaker opening and closing states.
3. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 1, wherein the method comprises the following steps:
the digital twinning technique includes: establishing entity mapping from the objective world to the virtual world, so as to reflect the whole life cycle of the corresponding entity in the target environment; the whole-course sensing, self-diagnosis and future prediction of the state of the physical entity object can be completed by means of simulation, emulation, data analysis and other processes, and the implementation control and comprehensive optimization of the related behavior of the entity object are realized by means of self-evolution and self-learning of a cross-reference propulsion model between a digital model and the entity model.
4. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 1, wherein the method comprises the following steps:
the construction of the power distribution cabinet running state twin model comprises the following steps:
establishing a model according to the geometric data of the intelligent power distribution cabinet;
adding the attribute value corresponding to the intelligent power distribution cabinet to the geometric model;
establishing the connection between the operation state data of the intelligent power distribution cabinet and the twin model; the data interface is used as a transmission channel for the actual data of the running state of the power distribution cabinet and the twin model.
5. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 1, wherein the method comprises the following steps:
the ant colony algorithm comprises: the collected fault samples are regarded as ant nodes in the ant colony, the samples are firstly divided into a plurality of classes, a clustering center is determined, then the deviation of the ant from each clustering center is calculated according to the European length experienced by the ant in the food searching process, the class of the sample is adjusted by selecting the smallest deviation, the clustering center is updated, and the fault samples of the same type are gathered together.
6. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 1, wherein the method comprises the following steps:
the construction of the intelligent power distribution cabinet running state evaluation index system based on the twin model and the weighting thereof comprises the following steps:
establishing an intelligent power distribution cabinet running state evaluation model based on a twin model;
determining a membership function to form a membership matrix;
determining the weights of all indexes by adopting a principal component analysis method, and forming a weight matrix;
carrying out fuzzy comprehensive evaluation on the operation state of the intelligent power distribution cabinet by adopting a Hamacher operator;
and outputting an evaluation result.
7. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 6, wherein the method comprises the following steps:
the establishment of the intelligent power distribution cabinet operation state evaluation model based on the twin model comprises the following steps:
let the set of intelligent power distribution cabinet running state information evaluation factors be U= { U 1 ，u 2 ，…，u m -wherein ui (i=1, 2, …, m) is data participating in the evaluation of the device, divided into discrete and continuous types; wherein the discrete data refers to equipment status information such as equipment locking, abnormal time setting, parameter error and the like; the continuous data refer to the temperature and humidity of the device, the load rate of the CPU, the working voltage, the working current and the like;
the interval type standardization is adopted to convert the data into an evaluation index, and the specific formula is as follows:
wherein x is the actual value of the data, [ x ] n ，x m ]For the data[ x ] the optimum operation interval of (1) min ，x max ]An operation request interval for the data;
let the evaluation result set of the intelligent power distribution cabinet be V= { V 1 ，v 2 ，…，v n }, v is i (i=1, 2, …, n) is the running state of the device, and is divided into 4 states of normal, attention, abnormal and fault, corresponding to v respectively 1 ，v 2 ，v 3 ，v 4 。
8. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 6, wherein the method comprises the following steps:
the membership function is a function for reflecting the influence degree of the operation data of the intelligent power distribution cabinet on the operation state of the intelligent power distribution cabinet;
for discrete state data, the value range is {0,1}, and the discrete state data has definite equipment running states to correspond, so that the membership function can be simplified into a function which is not 0, namely 1;
for continuous status data, the running parameters of equipment statistics are obtained according to expert experience, manufacturer opinion and extreme conditions.
9. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 6, wherein the method comprises the following steps:
the membership matrix is calculated after M groups of data are brought into corresponding membership functions, a membership set can be obtained, the membership set is used as a column, and a membership matrix R is obtained, wherein the membership matrix R is shown in the formula:wherein r is nm The result obtained for the membership function.
10. The fuzzy comprehensive evaluation method for the operation state of the intelligent power distribution cabinet based on the twin model according to claim 6, wherein the method comprises the following steps:
the principal component analysis method utilizes the relation among indexes to fuse a plurality of indexes into a few indexes, and replaces the original indexes with a few comprehensive indexes under the condition of keeping most of information;
the weight matrix is as follows: the weight is calculated by adopting a 1-9 scale analytic hierarchy process, and the operation data of the power distribution cabinet is applied to a certain operation state v k The influence degree of (2) is compared pairwise, if the operation data u i And u j To a considerable extent on the operating state, z ij ＝1；
If data u i Ratio u j To the greatest extent, z ij ＝1/9，z ji ＝9；
Thereby, a judgment matrix Z is obtained, namely:and then carrying out normalization processing on the judgment matrix Z to obtain a weight matrix: />Wherein a is nm Refers to the influence of each data on different running states;
the fuzzy comprehensive evaluation is that when the weight matrix A and the membership matrix R are known, the fuzzy comprehensive evaluation can be based on the formula
Performing fuzzy synthesis processing, wherein->Is a Hamacher operator, b= { B 1 ，b 2 ，…，b n -a fuzzy comprehensive evaluation set;
the Hamacher operator refers to a fuzzy synthesis operator containing parameters, and the expression is as follows:
wherein, gamma is a value in the range of (0, ++ infinity) range is used for the control of the temperature of the liquid crystal display device, and x is 0,1 and y is 0, 1.
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| CN117937768B (en) * | 2024-03-21 | 2024-05-28 | 无锡志恒自动化系统有限公司 | Intelligent electrical cabinet remote monitoring system |
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