CN117833480B - Intelligent switch cabinet with online monitoring function - Google Patents
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Abstract
The invention relates to the electrical field, and particularly discloses an intelligent switch cabinet with an online monitoring function, which comprises a mode switching module, an operation interface module, a permission authentication module, a data monitoring module, a channel estimation module, an intelligent prediction module and an early warning processing module; according to the intelligent switch cabinet with the on-line monitoring function, the working mode is switched through the switch, the switch cabinet is directly operated in the background, working condition prediction is carried out through monitoring and collecting of working parameters, and working stability of the intelligent switch cabinet is improved.
Description
Technical Field
The invention relates to the electrical field, in particular to an intelligent switch cabinet with an on-line monitoring function.
Background
In modern electrical systems, a switchgear cabinet is a key component of the electrical equipment for controlling and protecting the electrical circuit. Conventional switchgear generally only provides basic switching and protection functions, and it is difficult to achieve comprehensive monitoring and intelligent management of a power system. Some switchgear systems have incorporated monitoring devices, but these devices are often limited to a single function, lacking comprehensive intelligent monitoring and predictive capabilities. Meanwhile, the existing intelligent switch cabinet system has some defects in network performance and fault early warning processing, so that the management efficiency of the power system is low and the maintenance is difficult, and therefore, an intelligent switch cabinet with an online monitoring function is urgently needed to improve the safety, reliability and intelligent level of the power system.
Disclosure of Invention
The invention aims to provide an intelligent switch cabinet with an on-line monitoring function, and aims to solve the problems in the background.
In order to achieve the above purpose, the invention provides an intelligent switch cabinet with an online monitoring function, which comprises a mode switching module, an operation interface module, a permission authentication module, a data monitoring module, a channel estimation module, an intelligent prediction module and an early warning processing module; the mode switching module switches the near-ground and remote operation modes through a knob switch; the operation interface module is used for providing an operation interface for the staff; the authority authentication module pops up an authority authentication window when a user operates the intelligent switch cabinet through the background, and executes operation after authentication; the data monitoring module is used for monitoring and collecting working data; the channel estimation module is used for estimating a channel through a channel estimation algorithm; the intelligent prediction module predicts the working parameters of the intelligent switch cabinet by establishing a working parameter prediction model; and the early warning processing module carries out fault prediction alarm and processing according to the working parameters predicted by the intelligent prediction module.
Further, the switching between the near-ground and the far-field operation modes is specifically: the switch is moved to a far place, the intelligent switch cabinet is operated at the background, the switch is moved to a near place, and the intelligent switch cabinet is operated at the cabinet surface.
Further, the operation interface module comprises a display screen installed on the intelligent switch cabinet, wherein the display screen is used for displaying the state of the intelligent switch cabinet and performing simple operation, and the operation interface module further comprises a background computer display interface, wherein the background computer display interface is used for displaying a circuit, and clicking each part in the circuit on the background computer display screen is used for operating the corresponding part in the intelligent switch cabinet.
Further, the authentication of the authority authentication module specifically includes: and (3) popping up an authentication window after clicking the operation, inputting a user name and a password for authentication, and executing the operation after authentication.
Further, the data monitoring module comprises a sensor installed in the intelligent switch cabinet, the sensor is used for monitoring the working condition of the intelligent switch cabinet, collecting working data and transmitting the collected working data to the background computer, and the working data comprise temperature and operation parameters.
Further, the channel estimation module performs channel estimation through a channel estimation algorithm.
Further, the channel estimation algorithm comprises the following detailed procedures:
Defining a data symbol vector at time k during transmission Expressed asWhereinRespectively representing the 1 st, 2 nd, i th and n th values of the data symbol vector, defining a vector of modulation valuesExpressed asWhereinRespectively representing the 1 st, 2 nd, i th and n th values of the vector of the modulation values, and defining a noise vector when noise is generated in the transmission processExpressed asWhereinThe 1 st, 2 nd, i th and n th noise values are respectively expressed as noise vectors, demodulation and equalization operations are carried out on the data symbols based on the channel estimation result, the relation between the data symbols and the channel estimation is obtained, the channel estimation value with the smallest relation is found as the k moment channel estimation value, and the relation is as follows:
，
Wherein, The channel estimate at time k is indicated,Represents the channel estimation value corresponding to the ith number value at the k moment,Represents the 2 norm, for the pilot subcarriers at this timeFor the known quantity, the actual environment causes errors to influence the communication quality, so that the influence of demodulation errors and channel estimation caused by channel weakness is avoided, the influence of actual environment noise on communication is weakened, the average operation is performed on the basis of a channel frequency domain and a time domain, and the average operation on the frequency domain is as follows:
，
Wherein, The channel estimate at time k is indicated,The channel weights are represented as such,Representing the channel estimate at time k-i,The channel value at the moment of k-i is represented, n represents the number of subcarriers participating in averaging under the frequency domain, the frequency domain averaging is completed, the time domain averaging operation is carried out, the channel value is updated, and the updating formula is as follows:
，
Wherein the method comprises the steps of Representing the update parameters of the device,And the channel estimation value at the time of k-1 is represented, and the updating and the adjustment of the subsequent data symbols are carried out according to the historical channel estimation value. The channel estimation algorithm provided by the invention demodulates and equalizes the data symbols based on the historical channel result, and averages the channel based on the frequency domain and the time domain, thereby improving the network performance of the system, reducing the packet loss rate, reducing the noise data in the data transmission process and improving the data transmission quality.
Furthermore, the intelligent prediction module provides a working parameter prediction model based on an artificial intelligent algorithm, and performs model training through a training set formed by working data of the intelligent switch cabinet collected by the data monitoring module so as to monitor working parameters of the intelligent switch cabinet in real time.
Further, the working parameter prediction model comprises the following detailed procedures:
based on an improved decision tree algorithm, constructing a weak regression tree, combining the weak regression trees, and defining a weak regression tree output vector for each weak regression tree output value Expressed asWhereinThe output values respectively expressed as the 1 st weak regression tree, the 2 nd weak regression tree, the i th weak regression tree and the m th weak regression tree are accumulated, and the formulas are as follows:
，
Wherein the method comprises the steps of Representing the cumulative sum of the k-phases,Respectively representing the effective rates of the 1 st weak regression tree, the 2 nd weak regression tree and the m th weak regression tree, searching in a negative gradient direction, wherein the weak regression trees form leaf node areasThe loss function is constructed as follows:
，
wherein y represents the input data, Indicating predicted data, c is a set constant,Representing the loss function, updating the model, optimizing parameters in the prediction model, and regarding the prediction dataCorresponding real data is collected at the next momentBased on the error between the real data and the predicted data, the effective rate of the weak regression tree is updated, and the formula is as follows:
，
Wherein, Indicating that the updated weak regression number is effective,Indicating the efficiency of the update,Representing an indicative function, taking 1 in the range, otherwise taking 0,Indicating the degree of offset of the setting. According to the working parameter prediction model, based on an improved decision tree algorithm, a weak regression tree is constructed, the weak regression tree is combined, a loss function under a tolerant condition is constructed, searching is conducted in a negative gradient direction, the effective rate of the weak regression tree is optimized based on a prediction error through an artificial intelligent algorithm, and the prediction effect of the working parameter prediction model is improved.
Further, the fault prediction alarm and the fault prediction processing of the early warning processing module are specifically as follows: and when the predicted data exceeds a set critical value, the intelligent switch cabinet automatically performs preset operation.
The invention provides an intelligent switch cabinet with an online monitoring function, which comprises a mode switching module, an operation interface module, a permission authentication module, a data monitoring module, a channel estimation module, an intelligent prediction module and an early warning processing module; the mode switching module switches between a near-ground operation mode and a remote operation mode through a knob switch, the near-ground operation model directly operates on the intelligent switch cabinet, and the remote operation mode remotely operates the intelligent switch cabinet through a background computer; the operation interface module provides an intelligent switch cabinet operation interface for staff, and a user operates the intelligent switch cabinet at the operation interface; the authority authentication module pops up an authority authentication window when a user operates the intelligent switch cabinet through the background, and the intelligent switch cabinet executes operation after authentication; the data monitoring module is used for installing a sensor in the intelligent switch cabinet, monitoring the work of the intelligent switch cabinet and collecting work data; the channel estimation module provides a channel estimation algorithm, demodulates and equalizes the data symbols based on the historical channel result to obtain the relation between the data symbols and the channel estimation, and then carries out average operation on the channel based on the frequency domain and the time domain, so that the influence of demodulation errors and further on the channel estimation caused by channel weakness is avoided, and the performance of the network system is improved; the intelligent prediction module provides a working parameter prediction model, predicts working data of the intelligent switch cabinet, builds a weak regression tree based on an improved decision tree algorithm, combines the weak regression tree, builds a loss function under a tolerant condition, searches in a negative gradient direction, optimizes the effective rate of the weak regression tree based on a prediction error through an artificial intelligent algorithm, and obtains a good parameter prediction effect; and the early warning processing module carries out fault prediction alarm and processing according to the working data predicted by the intelligent prediction module. The intelligent switch cabinet with the on-line monitoring function provided by the invention can monitor key data in real time, including parameters such as current, voltage, temperature and the like. The method is beneficial to timely finding potential problems and improving the fault detection and elimination capability of the power system; the switch cabinet can conduct intelligent prediction based on historical and real-time data, and potential faults or abnormal conditions can be identified in advance. Through the early warning processing module, the system can take corresponding measures, the risk of serious faults of the power system is reduced, and the stability and reliability of the system are improved. The intelligent switch cabinet provided by the invention provides an advanced and comprehensive solution for the power system, so that the power equipment is safer, more reliable and more intelligent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a schematic diagram of the structure of the present invention;
fig. 2 is a flow chart of channel estimation of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides an intelligent switch cabinet with an online monitoring function, which comprises a mode switching module, an operation interface module, a permission authentication module, a data monitoring module, a channel estimation module, an intelligent prediction module and an early warning processing module; the mode switching module switches the near-ground and remote operation modes through a knob switch; the operation interface module is used for providing an operation interface for the staff; the authority authentication module pops up an authority authentication window when a user operates the intelligent switch cabinet through the background, and executes operation after authentication; the data monitoring module is used for monitoring and collecting working data; the channel estimation module is used for estimating a channel through a channel estimation algorithm; the intelligent prediction module predicts the working parameters of the intelligent switch cabinet by establishing a working parameter prediction model; and the early warning processing module carries out fault prediction alarm and processing according to the working parameters predicted by the intelligent prediction module.
Specifically, the switching between the near-ground and the far-field operation modes is specifically: the switch is moved to a far place, the intelligent switch cabinet is operated at the background, the switch is moved to a near place, and the intelligent switch cabinet is operated at the cabinet surface.
Specifically, the operation interface module comprises a display screen installed on the intelligent switch cabinet, wherein the display screen is used for displaying the state of the intelligent switch cabinet and performing simple operation, and the operation interface module further comprises a background computer display interface, wherein the background computer display interface is used for displaying a circuit, and clicking each part in the circuit on the background computer display screen is used for operating the corresponding part in the intelligent switch cabinet.
Specifically, the authentication of the authority authentication module specifically includes: and (5) popping up an authentication window after clicking operation, and inputting a user name and a password for authentication.
Specifically, the data monitoring module comprises a sensor installed in the intelligent switch cabinet, wherein the sensor is used for monitoring the working condition of the intelligent switch cabinet, collecting working data and transmitting the collected working data to a background computer.
Specifically, the channel estimation module performs channel estimation through a channel estimation algorithm.
Specifically, the channel estimation algorithm comprises the following detailed procedures:
Defining a data symbol vector at time k during transmission Expressed asWhereinRespectively representing the 1 st, 2 nd, i th and n th values of the data symbol vector, defining a vector of modulation valuesExpressed asWhereinRespectively representing the 1 st, 2 nd, i th and n th values of the vector of the modulation values, and defining a noise vector when noise is generated in the transmission processExpressed asWhereinThe 1 st, 2 nd, i th and n th noise values are respectively expressed as noise vectors, demodulation and equalization operations are carried out on the data symbols based on the channel estimation result, the relation between the data symbols and the channel estimation is obtained, the channel estimation value with the smallest relation is found as the k moment channel estimation value, and the relation is as follows:
，
Wherein, The channel estimate at time k is indicated,Represents the channel estimation value corresponding to the ith number value at the k moment,Represents the 2 norm, for the pilot subcarriers at this timeFor the known quantity, the actual environment causes errors to influence the communication quality, so that the influence of demodulation errors and channel estimation caused by channel weakness is avoided, the influence of actual environment noise on communication is weakened, the average operation is performed on the basis of a channel frequency domain and a time domain, and the average operation on the frequency domain is as follows:
，
Wherein, The channel estimate at time k is indicated,The channel weights are represented as such,Representing the channel estimate at time k-i,The channel value at the moment of k-i is represented, n represents the number of subcarriers participating in averaging under the frequency domain, the frequency domain averaging is completed, the time domain averaging operation is carried out, the channel value is updated, and the updating formula is as follows:
，
Wherein, Representing the update parameters of the device,And the channel estimation value at the time of k-1 is represented, and the updating and the adjustment of the subsequent data symbols are carried out according to the historical channel estimation value. The channel estimation algorithm provided by the invention demodulates and equalizes the data symbols based on the historical channel result, and averages the channel based on the frequency domain and the time domain, thereby improving the network performance of the system, reducing the packet loss rate, reducing the noise data in the data transmission process and improving the data transmission quality.
Specifically, the intelligent prediction module provides a working parameter prediction model based on an artificial intelligent algorithm, and performs model training through a training set formed by working data of the intelligent switch cabinet collected by the data monitoring module to monitor working parameters of the intelligent switch cabinet in real time.
Specifically, the working parameter prediction model comprises the following detailed processes:
based on an improved decision tree algorithm, constructing a weak regression tree, combining the weak regression trees, and defining a weak regression tree output vector for each weak regression tree output value Expressed asWhereinThe output values respectively expressed as the 1 st weak regression tree, the 2 nd weak regression tree, the i th weak regression tree and the m th weak regression tree are accumulated, and the formulas are as follows:
，
Wherein the method comprises the steps of Representing the cumulative sum of the k-phases,Respectively representing the effective rates of the 1 st weak regression tree, the 2 nd weak regression tree and the m th weak regression tree, searching in a negative gradient direction, wherein the weak regression trees form leaf node areasThe loss function is constructed as follows:
，
Where y represents the input data and where, Indicating predicted data, c is a set constant,Representing the loss function, updating the model, optimizing parameters in the prediction model, and regarding the prediction dataCorresponding real data is collected at the next momentBased on the error between the real data and the predicted data, the effective rate of the weak regression tree is updated, and the formula is as follows:
，
indicating that the updated weak regression number is effective, Indicating the efficiency of the update,Representing an indicative function, taking 1 in the range, otherwise taking 0,Indicating the degree of offset of the setting. According to the working parameter prediction model, based on an improved decision tree algorithm, a weak regression tree is constructed, the weak regression tree is combined, a loss function under a tolerant condition is constructed, searching is conducted in a negative gradient direction, the effective rate of the weak regression tree is optimized based on a prediction error through an artificial intelligent algorithm, and the prediction effect of the working parameter prediction model is improved.
Specifically, the fault prediction alarm and the fault prediction treatment of the early warning treatment module are specifically as follows: and when the predicted data exceeds a set critical value, the intelligent switch cabinet automatically performs preset operation.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (8)
1. The intelligent switch cabinet with the online monitoring function is characterized by comprising a mode switching module, an operation interface module, a permission authentication module, a data monitoring module, a channel estimation module, an intelligent prediction module and an early warning processing module; the mode switching module switches the near-ground and remote operation modes through a knob switch; the operation interface module is used for providing an operation interface for the staff; the authority authentication module pops up an authority authentication window when a user operates the intelligent switch cabinet through the background, and executes operation after authentication; the data monitoring module is used for monitoring and collecting working data; the channel estimation module is used for estimating a channel through a channel estimation algorithm; the intelligent prediction module predicts the working parameters of the intelligent switch cabinet by establishing a working parameter prediction model; the early warning processing module carries out fault prediction alarm and processing according to the working parameters predicted by the intelligent prediction module;
The intelligent prediction module provides a working parameter prediction model based on an artificial intelligent algorithm, performs model training through a training set formed by working data of the intelligent switch cabinet collected by the data monitoring module, and monitors working parameters of the intelligent switch cabinet in real time;
the working parameter prediction model comprises the following detailed processes:
Based on the improved decision tree algorithm, the weak regression trees are combined, and for the output value of each weak regression tree, the weak regression tree output vector is defined Accumulating all the weak regression tree output values according to the following formula:
Wherein f 1、f2、fi、fm is represented as the output value of the 1 st weak regression tree, the 2 nd weak regression tree, the i th weak regression tree, the m th weak regression tree, respectively, wherein The cumulative sum of the k stage is represented, beta 1、β2、βm represents the effective rate of the 1 st weak regression tree, the 2 nd weak regression tree and the m th weak regression tree, the searching is carried out in the negative gradient direction, the weak regression tree forms a leaf node area U m,j, and a loss function is constructed as follows:
Wherein y represents input data, h m represents predicted data, c is a set constant, L (·) represents a loss function, the model is updated, for predicted data h m, corresponding real data h' m is collected at the next moment, and the effective rate of the weak regression tree is updated based on the error between the real data and the predicted data, with the following formula:
beta' m represents the effective rate of the updated weak regression number, delta represents the updating rate, I {. Cndot. } represents the indirection function, 1 is taken in the range, otherwise 0 is taken, and theta represents the set offset.
2. An intelligent switch cabinet with on-line monitoring function according to claim 1, characterized in that said switching between the near-ground and the remote operation modes is in particular: the switch is moved to a far place, the intelligent switch cabinet is operated at the background, the switch is moved to a near place, and the intelligent switch cabinet is operated at the cabinet surface.
3. The intelligent switch cabinet with the on-line monitoring function according to claim 1, wherein the operation interface module comprises a display screen installed on the intelligent switch cabinet, the display screen is used for displaying the state of the intelligent switch cabinet and performing simple operation, and the intelligent switch cabinet further comprises a background computer display interface, the background computer display interface is used for displaying a circuit, and clicking each component in the circuit on the background computer display screen is used for operating the corresponding component in the intelligent switch cabinet.
4. The intelligent switch cabinet with the on-line monitoring function according to claim 1, wherein the authentication of the authority authentication module is specifically: and (5) popping up an authentication window after clicking operation, and inputting a user name and a password for authentication.
5. An intelligent switch cabinet with an on-line monitoring function according to claim 1, wherein the data monitoring module comprises a sensor installed in the intelligent switch cabinet, the sensor is used for monitoring the working condition of the intelligent switch cabinet, collecting working data and transmitting the collected working data to a background computer.
6. The intelligent switch cabinet with on-line monitoring function according to claim 1, wherein the channel estimation module performs channel estimation by a channel estimation algorithm.
7. The intelligent switch cabinet with the on-line monitoring function according to claim 6, wherein the channel estimation algorithm comprises the following detailed procedures:
Based on the channel estimation result, performing demodulation equalization operation on the data symbol to obtain the relation between the data symbol and the channel estimation, performing demodulation equalization operation on the data symbol, and finding the channel estimation value with the smallest relation as the k-moment channel estimation value, wherein the relation is as follows:
Wherein H k represents a channel estimation value at k time, Y k,i represents an i-th value of the data symbol vector Y k, H k,i represents a channel estimation value corresponding to the i-th value at k time, X k,i represents an i-th value of the modulation value vector X k, i·i 2 represents a 2-norm, and the average operation of the channel frequency domain and the time domain is performed, and the average operation of the frequency domain is as follows:
Wherein, gamma i represents channel weight, H k-i represents k-i time channel estimation value, H' k-i represents k-i time channel value, n represents the number of subcarriers participating in averaging in the frequency domain, the frequency domain averaging is completed, the time domain averaging operation is performed, and the channel value is updated, wherein the updating formula is as follows:
Wherein, alpha k represents an update parameter, H k-1 represents a channel estimation value at k-1 time, and the subsequent data symbol is updated and mediated according to the channel estimation value at the historical time.
8. The intelligent switch cabinet with the on-line monitoring function according to claim 1, wherein the fault prediction alarm and the fault prediction process of the early warning processing module are specifically as follows: and when the predicted data exceeds a set critical value, the intelligent switch cabinet automatically performs preset operation.
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