CN115798184A - Multi-table combined self-checking method and system based on multi-module coordination - Google Patents
Multi-table combined self-checking method and system based on multi-module coordination Download PDFInfo
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Abstract
The invention discloses a multi-table combined self-checking method and a system based on multi-module coordination, which relate to the technical field of data processing, and the method comprises the following steps: respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through a data sampling module; interface conversion is carried out on heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through an interface conversion module; acquiring multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information; inputting multi-table data acquisition information into a multi-table combined self-checking model for analysis, and outputting a multi-table combined self-checking result; and carrying out display management on the multi-table combined self-inspection result based on the liquid crystal display module. The technical effects of improving the intellectualization and the visual self-inspection of the multi-meter combined self-inspection and further ensuring the accuracy of the multi-meter combined self-inspection result are achieved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a multi-table combined self-checking method and system based on multi-module coordination.
Background
With the gradual development of the power industry, equipment used in a power supply system is also developed towards more modernization, and an electric meter, a water meter and the like are common metering equipment to realize metering of consumed energy, information storage, real-time monitoring, automatic control and the like. The method has important practical significance for ensuring the accurate metering operation of each meter device and realizing accurate self-checking.
However, the self-checking intelligence degree of each meter device in the prior art is not high, so that the self-checking result is not accurate enough.
Disclosure of Invention
The multi-meter combined self-checking method and system based on multi-module coordination solve the technical problems that in the prior art, the self-checking intelligence degree of each meter device is low, and the self-checking result is not accurate enough, achieve the technical effects of performing combined self-checking on the multi-meters through multi-module coordination, reducing investment cost, improving intellectualization of multi-meter combined self-checking and visual self-checking, and further ensuring accuracy of the multi-meter combined self-checking result.
In view of the above problems, the present invention provides a multi-table combined self-checking method and system based on multi-module coordination.
In a first aspect, the present application provides a multi-table joint self-check method based on multi-module coordination, where the method includes: acquiring a special transformer acquisition terminal, wherein the special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module and an interface conversion module; respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through the data sampling module; interface conversion is carried out on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information through the interface conversion module, so that heat meter conversion data information, water meter conversion data information and gas meter conversion data information are obtained; acquiring multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information; calling and obtaining a multi-table combined self-checking model through the CPU module; inputting the multi-table data acquisition information into the multi-table joint self-checking model for analysis, and outputting a multi-table joint self-checking result; and carrying out display management on the multi-table combined self-inspection result based on the liquid crystal display module.
On the other hand, the application also provides a multi-table combined self-checking system based on multi-module coordination, and the system comprises: the special transformer acquisition terminal acquisition module is used for acquiring a special transformer acquisition terminal, and the special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module and an interface conversion module; the data sampling module is used for respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through the data sampling module; the interface conversion module is used for performing interface conversion on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information through the interface conversion module to obtain heat meter conversion data information, water meter conversion data information and gas meter conversion data information; the multi-meter data information acquisition module is used for acquiring multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information; the model calling module is used for calling the multi-table combined self-checking model through the CPU module; the model analysis output module is used for inputting the multi-table data acquisition information into the multi-table joint self-checking model for analysis and outputting a multi-table joint self-checking result; and the display management module is used for carrying out display management on the multi-table combined self-checking result based on the liquid crystal display module.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the data sampling module is adopted to respectively obtain electric meter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information, the interface conversion module is used for carrying out interface conversion on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information to obtain heat meter conversion data information, water meter conversion data information and gas meter conversion data information, and multi-meter data acquisition information is obtained according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information; calling a multi-table combined self-checking model through a CPU module, inputting the multi-table data acquisition information into the multi-table combined self-checking model for analysis, outputting a multi-table combined self-checking result, and carrying out display management on the multi-table combined self-checking result based on the liquid crystal display module. And then, the technical effects of carrying out combined self-checking on the multiple tables through multi-module coordination, reducing investment cost, improving intellectualization and visual self-checking of the multi-table combined self-checking and further ensuring the accuracy of the multi-table combined self-checking result are achieved.
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FIG. 1 is a schematic flowchart of a multi-table joint self-checking method based on multi-module coordination according to the present application;
fig. 2 is a schematic flow chart illustrating a multi-table combined self-inspection model obtained in the multi-table combined self-inspection method based on multi-module coordination according to the present application;
FIG. 3 is a schematic flow chart illustrating operation characteristics of a multi-table combined self-checking method based on multi-module coordination according to the present application;
FIG. 4 is a schematic structural diagram of a multi-table combined self-checking system based on multi-module coordination according to the present application;
description of reference numerals: the system comprises a special transformer acquisition terminal acquisition module 11, a data sampling module 12, an interface conversion module 13, a multi-table data information acquisition module 14, a model calling module 15, a model analysis output module 16 and a display management module 17.
Detailed Description
The multi-meter combined self-checking method and system based on multi-module coordination solve the technical problems that in the prior art, the self-checking intelligence degree of each meter device is low, and the self-checking result is not accurate enough, achieve the technical effects of performing combined self-checking on multiple meters through multi-module coordination, reducing investment cost, improving intellectualization of multi-meter combined self-checking and visual self-checking, and further ensuring accuracy of the multi-meter combined self-checking result.
Example one
As shown in fig. 1, the present application provides a multi-table combined self-test method based on multi-module coordination, where the method is applied to a multi-table combined self-test system, and the method includes:
step S100: acquiring a special transformer acquisition terminal, wherein the special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module and an interface conversion module;
specifically, with the gradual development of the power industry, the devices used in the power supply system are also developing towards more modernization, and the electricity meter, the water meter, and the like are commonly used metering devices to realize metering, information storage, real-time monitoring, automatic control, and the like of consumed energy. The method has important practical significance for ensuring the accurate metering operation of each meter device and realizing accurate self-checking.
The special transformer acquisition terminal is a client power utilization management terminal which is specially designed based on an embedded software and hardware development platform and is adapted to the requirement of power demand side management modernization, and is combined with years of design development and field operation experience in the power industry. The system adopts the most advanced technology at present such as microelectronic technology, computer chip technology, modern communication technology and the like, supports public wireless communication network technology such as GPRS/GSM/CDMA and the like, can realize functions of automatic meter reading, load monitoring and control on power users, prepayment management, power quality management, electricity larceny prevention, abnormal alarm and the like by being matched with a power load management master station system, is widely suitable for power utilization field service and power load management systems, and is a terminal product with higher practical value in a power marketing automation system. The special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module, an interface conversion module and the like, and all the modules are directly coordinated with each other to run.
Step S200: respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through the data sampling module;
specifically, the data sampling module is used for acquiring real-time consumption data of the electric meter, the heat meter, the water meter and the gas meter, and acquiring electric meter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information respectively, wherein the information comprises total electric quantity, peak electric quantity, valley electric quantity, total power, water flow accumulated flow, accumulated heat, heat power, heat flow, gas accumulated quantity and the like.
Step S300: interface conversion is carried out on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information through the interface conversion module, so that heat meter conversion data information, water meter conversion data information and gas meter conversion data information are obtained;
step S400: acquiring multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information;
specifically, interface conversion is performed on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information through the interface conversion module, so that multi-meter data integration is realized, and corresponding heat meter conversion data information, water meter conversion data information and gas meter conversion data information are obtained. And summarizing the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information to obtain multi-meter data acquisition information, and performing data-in-one acquisition, so that the investment cost is reduced, and multi-meter collection and reading are achieved.
Step S500: calling and obtaining a multi-table combined self-checking model through the CPU module;
as shown in fig. 2, further to obtain the multi-table combined self-test model, step S500 of the present application further includes:
step S510: acquiring historical ammeter data acquisition information, historical heat meter data acquisition information, historical water meter data acquisition information and historical gas meter data acquisition information;
step S520: respectively performing characteristic analysis on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information to obtain ammeter operation characteristics, heat meter operation characteristics, water meter operation characteristics and gas meter operation characteristics;
step S530: respectively carrying out self-checking model training according to the ammeter operation characteristics, the heat meter operation characteristics, the water meter operation characteristics and the gas meter operation characteristics to obtain an ammeter self-checking analysis model, a heat meter self-checking analysis model, a water meter self-checking analysis model and a gas meter self-checking analysis model;
step S540: extracting model parameters of the ammeter self-checking analysis model, the thermal meter self-checking analysis model, the water meter self-checking analysis model and the gas meter self-checking analysis model to obtain ammeter analysis model parameters, thermal meter analysis model parameters, water meter analysis model parameters and gas meter analysis model parameters;
step S550: and constructing the multi-meter combined self-checking model based on the electric meter analysis model parameters, the thermal meter analysis model parameters, the water meter analysis model parameters and the gas meter analysis model parameters.
As shown in fig. 3, further, the obtaining of the operation characteristics of the electric meter, the operation characteristics of the heat meter, the operation characteristics of the water meter, and the operation characteristics of the gas meter further includes:
step S521: carrying out unsupervised learning classification on the historical electric meter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information respectively to generate a continuous meter data set;
step S522: constructing a table data change curve set according to the continuity table data set;
step S523: carrying out curvature analysis on the table data change curve to obtain a table data curvature change result set;
step S524: and inputting the set of the curvature change results of the meter data into a meter data characteristic evaluation model to respectively obtain the running characteristics of the electric meter, the heat meter, the water meter and the gas meter.
Further, in the generating the continuity table data set, step S521 in the present application further includes:
step S5211: traversing access processing is respectively carried out on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information to generate a uniformity meter data set;
step S5212: defining the data in the uniformity table data set as P clusters;
step S5213: carrying out average calculation on pairwise distances of data points in the P clusters to obtain an average distance data set;
step S5214: obtaining a class table data set according to the average distance data set, wherein the class table data set comprises a classification set with the minimum distance average value;
step S5215: according to the class table data set, carrying out layer-by-layer recursive clustering on the average distance data set until a table data clustering tree of the uniformity table data set is generated;
step S5216: and performing learning classification based on the table data clustering tree to generate the continuity table data set.
Specifically, the CPU module is a core of the special transformer acquisition terminal, controls all modules to perform coordination work, and calls the CPU module to obtain the multi-meter combined self-checking model, wherein the multi-meter combined self-checking model is a neural network model and is used for performing data combined self-checking on all the meters. The model training construction process comprises the steps of firstly acquiring historical ammeter data acquisition information, historical heat meter data acquisition information, historical water meter data acquisition information and historical gas meter data acquisition information through big data or a system history module. And respectively performing characteristic analysis on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information, wherein the analysis process is to respectively perform unsupervised learning classification on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information, and the unsupervised learning is to solve the problem in pattern recognition according to training samples with unknown classes, namely no data label and only data.
And generating a continuity meter data set by unsupervised learning classification, and firstly, respectively performing traversal access processing on all data information in the historical electric meter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information to generate a uniformity meter data set. The data in the uniformity table data set is then defined as P clusters, where the clusters refer to the process of grouping together similar things, while classifying dissimilar things into different categories. And further measuring and calculating the distances between every two data points in the P clusters, and then carrying out average value calculation to obtain the average distance between every two data points in the P clusters, namely an average distance data set, wherein the average distance data set comprises P average value data which are respectively in one-to-one correspondence with the P clusters.
And further obtaining P pieces of clustering average table data information, namely a class table data set, according to the average distance data set, wherein the class table data set comprises a cluster set with the minimum distance average value. The class table data set includes a class set that is the smallest from the mean. And performing layer-by-layer recursive clustering on the average distance data set according to the class table data set until a table data clustering tree of the uniformity table data set is generated, wherein the layer-by-layer recursive clustering refers to merging the data with the maximum or minimum average distance data into a large class according to the size of the average distance data and the sequence from large to small or from small to large. And finally, learning and classifying the table data based on the table data clustering tree to generate a continuity table data set when the table data are continuous. The continuity data in the continuity table data set shows the monitored table data information trend, classification of the table data information is realized through unsupervised learning, and the technical effect that intelligent calculation is more accurate in learning and classifying the table data is achieved.
And constructing a table data change curve set according to the continuity table data set, wherein the table data change curve set is a continuous data change curve obtained by monitoring each table data in real time. And carrying out curvature analysis on the table data change curves to obtain corresponding table data curvature change result sets, wherein the curvature analysis shows that the curve has a numerical value of the bending degree at a certain point, and the larger the curvature is, the larger the bending degree of the curve is. And inputting the set of the table data curvature change results into a table data characteristic evaluation model, wherein the table data characteristic evaluation model is used for carrying out operation characteristic analysis on the table data curvature change and can be obtained through historical data training, so that the corresponding electric meter operation characteristic, heat meter operation characteristic, water meter operation characteristic and gas meter operation characteristic are respectively output and obtained, and the electric meter operation characteristic, the heat meter operation characteristic, the water meter operation characteristic and the gas meter operation characteristic are working characteristics of each table operation.
And respectively carrying out self-checking model training according to the ammeter operation characteristics, the heat meter operation characteristics, the water meter operation characteristics and the gas meter operation characteristics, carrying out neural network model training through historical data, and when the output information of the neural network model reaches a preset accuracy rate or reaches a convergence state, finishing the supervision and learning process to obtain an ammeter self-checking analysis model, a heat meter self-checking analysis model, a water meter self-checking analysis model and a gas meter self-checking analysis model corresponding to corresponding characteristic data, wherein the ammeter self-checking analysis model, the heat meter self-checking analysis model, the water meter self-checking analysis model and the gas meter self-checking analysis model are used for carrying out self-checking analysis on the working states of the ammeter, the heat meter, the water meter and the gas meter. And extracting model parameters of the ammeter self-checking analysis model, the heat meter self-checking analysis model, the water meter self-checking analysis model and the gas meter self-checking analysis model to obtain ammeter analysis model parameters, heat meter analysis model parameters, water meter analysis model parameters and gas meter analysis model parameters, wherein the ammeter analysis model parameters comprise model weight, characteristic parameters, identification parameters and the like.
And constructing the multi-meter combined self-checking model obtained by performing combined training on each model parameter based on the electric meter analysis model parameter, the thermal meter analysis model parameter, the water meter analysis model parameter and the gas meter analysis model parameter, wherein the multi-meter combined self-checking model is used for performing data combined self-checking on each meter and self-checking the operation state and the fault type of each meter. The self-checking model is constructed through joint training of the parameters of the table models, the accuracy of model training output is more accurate, and the intellectualization of multi-table joint self-checking and the high efficiency of self-checking are improved.
Step S600: inputting the multi-table data acquisition information into the multi-table joint self-checking model for analysis, and outputting a multi-table joint self-checking result;
further, the step S600 of outputting the multi-table combined self-test result further includes:
step S610: the multi-table combined self-checking model comprises an input layer, a characteristic analysis layer, a self-checking analysis layer and an output layer;
step S620: inputting the multi-meter data acquisition information serving as an input layer into the characteristic analysis layer to obtain meter data operation characteristics;
step S630: inputting the table data operation characteristics into the self-checking analysis layer to obtain a multi-table combined self-checking result;
step 640: and outputting the multi-table combined self-checking result as a model output result through the output layer.
Specifically, the multi-table data acquisition information is input into the multi-table combined self-checking model for analysis, and the multi-table combined self-checking model comprises an input layer, a characteristic analysis layer, a self-checking analysis layer and an output layer. Firstly, the multi-meter data acquisition information is used as an input layer and input into the characteristic analysis layer, the characteristic analysis layer is used for analyzing the running characteristics of each meter, and the analysis process is the meter data characteristic analysis, so that meter data running characteristics, such as running stability, running faults and the like, are obtained. And inputting the meter data operation characteristics into the self-checking analysis layer, wherein the self-checking analysis layer can be obtained through historical data training and is used for carrying out fault identification analysis on the operation characteristics and outputting a multi-meter combined self-checking result, and the multi-meter combined self-checking result comprises a self-checking safety operation result, a self-checking fault type and the like. And the multi-table combined self-checking result is output as a model output result through the output layer, and the self-checking result is output by constructing a model multi-logic layer, so that the accuracy of the multi-table combined self-checking result is improved.
Step S700: and carrying out display management on the multi-table combined self-inspection result based on the liquid crystal display module.
Further, the step S700 of performing display management on the multi-table combined self-test result based on the liquid crystal display module further includes:
step S710: obtaining a table fault type according to the multi-table combined self-checking result;
step S720: performing fault grade evaluation on the multi-table combined self-detection result to obtain a table fault grade coefficient;
step S730: formulating a table fault solution based on the table fault type and the table fault grade coefficient;
step S740: and performing display management and control on the meter fault type, the meter fault grade coefficient and the meter fault solution through the liquid crystal display module.
Specifically, the multi-table joint self-test result is displayed and managed based on the liquid crystal display module, and specifically, the table fault types including the table metering fault, the table operation fault and the like are determined according to the multi-table joint self-test result. And performing fault grade evaluation on each fault type of the multi-table combined self-checking result, and determining a corresponding table fault grade coefficient, wherein the larger the coefficient is, the larger the fault grade is. And formulating a meter fault solution based on the meter fault type and the meter fault grade coefficient, wherein the meter fault solution is a solution of each meter fault type, such as measures of calibrating and clearing the electric meter. The liquid crystal display module is used for displaying, managing and controlling the meter fault type, the meter fault grade coefficient and the meter fault solution, and performing combined self-checking on the multiple meters through multi-module coordination, so that the investment cost is reduced, and the intellectualization and the visual self-checking of the combined self-checking of the multiple meters are improved.
Further, step S524 in the present application further includes:
step S5241: obtaining a discrete table data set according to the complement of the continuity table data set;
step S5242: obtaining a discrete persistence table data set exceeding a predetermined time threshold in the discrete table data sets;
step S5243: carrying out variance calculation on the numerical values in the discrete persistence table data set, and taking the variance calculation result as the table data deviation degree;
step S5244: and if the deviation degree of the meter data exceeds a preset deviation degree, carrying out self-checking early warning on the meter data.
Specifically, a discrete table data set is determined according to a complement of the continuity table data set, namely a residual data complement of the continuity table data set, and discrete data in the discrete table data set are discrete results caused by influences of various factors, such as table equipment signal interference factors, external pressure factors, environment temperature factors and the like, so that the discrete data cannot be used as actual values of the table data and other monitoring data are analyzed after being eliminated. The discrete persistence table data set is data in the discrete table data set, wherein the data exceeds a preset time threshold, and the preset time threshold is a preset discrete table data duration range, which indicates that the data discrete result needs to be analyzed.
And calculating the variance of the values in the discrete persistence table data set, and taking the variance calculation result as the deviation degree of the table data, wherein the variance indicates the discrete degree of the table data, and the larger the variance is, the higher the discrete degree of the monitoring data is, and the worse the running condition of the table data is. If the deviation degree of the meter data exceeds the preset deviation degree, the meter data is subjected to self-checking early warning if the meter data is subjected to self-checking early warning, and metering faults are caused because the meter working quality does not meet the standard due to external factors such as environment and the like. By considering the influence of factors such as meter working environment, meter equipment and the like on meter metering work, the accuracy of a self-checking result is improved, the meter fault is timely warned, and the technical effect of the meter working metering accuracy is further ensured.
In summary, the multi-table joint self-checking method and system based on multi-module coordination provided by the present application have the following technical effects:
the data sampling module is adopted to respectively obtain electric meter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information, the interface conversion module is used for carrying out interface conversion on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information to obtain heat meter conversion data information, water meter conversion data information and gas meter conversion data information, and multi-meter data acquisition information is obtained according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information; calling a multi-table combined self-checking model through a CPU module, inputting the multi-table data acquisition information into the multi-table combined self-checking model for analysis, outputting a multi-table combined self-checking result, and carrying out display management on the multi-table combined self-checking result based on the liquid crystal display module. And then, the technical effects of carrying out combined self-checking on the multiple tables through multi-module coordination, reducing investment cost, improving intellectualization and visual self-checking of the multi-table combined self-checking and further ensuring the accuracy of the multi-table combined self-checking result are achieved.
Example two
Based on the same inventive concept as the multi-table joint self-checking method based on multi-module coordination in the foregoing embodiment, the present invention further provides a multi-table joint self-checking system based on multi-module coordination, as shown in fig. 4, the system includes:
the special transformer acquisition terminal acquisition module 11 is used for acquiring a special transformer acquisition terminal, and the special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module and an interface conversion module;
the data sampling module 12 is used for respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through the data sampling module;
the interface conversion module 13 is configured to perform interface conversion on the heat meter data acquisition information, the water meter data acquisition information, and the gas meter data acquisition information through the interface conversion module to obtain heat meter conversion data information, water meter conversion data information, and gas meter conversion data information;
a multi-meter data information obtaining module 14, configured to obtain multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information, and the gas meter conversion data information;
the model calling module 15 is used for calling the multi-table combined self-checking model through the CPU module;
the model analysis output module 16 is used for inputting the multi-table data acquisition information into the multi-table joint self-checking model for analysis and outputting a multi-table joint self-checking result;
and the display management module 17 is configured to perform display management on the multi-table combined self-inspection result based on the liquid crystal display module.
Further, the model calling module further includes:
the historical data information acquisition unit is used for acquiring historical ammeter data acquisition information, historical heat meter data acquisition information, historical water meter data acquisition information and historical gas meter data acquisition information;
the characteristic analysis unit is used for respectively carrying out characteristic analysis on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information to obtain ammeter operation characteristics, heat meter operation characteristics, water meter operation characteristics and gas meter operation characteristics;
the model training unit is used for respectively carrying out self-checking model training according to the electric meter operation characteristics, the heat meter operation characteristics, the water meter operation characteristics and the gas meter operation characteristics to obtain an electric meter self-checking analysis model, a heat meter self-checking analysis model, a water meter self-checking analysis model and a gas meter self-checking analysis model;
the model parameter extraction unit is used for extracting model parameters of the ammeter self-checking analysis model, the thermal meter self-checking analysis model, the water meter self-checking analysis model and the gas meter self-checking analysis model to obtain ammeter analysis model parameters, thermal meter analysis model parameters, water meter analysis model parameters and gas meter analysis model parameters;
and the multi-meter combined self-checking model building unit is used for building the multi-meter combined self-checking model based on the electric meter analysis model parameters, the heat meter analysis model parameters, the water meter analysis model parameters and the gas meter analysis model parameters.
Further, the feature analysis unit further includes:
the unsupervised learning classification unit is used for performing unsupervised learning classification on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information respectively to generate a continuity meter data set;
the change curve set construction unit is used for constructing a table data change curve set according to the continuity table data set;
the curvature analysis unit is used for carrying out curvature analysis on the table data change curve to obtain a table data curvature change result set;
and the operation characteristic obtaining unit is used for inputting the table data curvature change result set into a table data characteristic evaluation model to respectively obtain the electric meter operation characteristic, the heat meter operation characteristic, the water meter operation characteristic and the gas meter operation characteristic.
Further, the unsupervised learning classification unit further comprises:
the traversal access processing unit is used for performing traversal access processing on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information respectively to generate a uniformity meter data set;
the data definition unit is used for defining the data in the uniformity table data set into P clusters;
the distance average value calculation unit is used for carrying out average value calculation on pairwise distances of data points in the P clusters to obtain an average distance data set;
the class table data set obtaining unit is used for obtaining a class table data set according to the average distance data set, and the class table data set comprises a classification set with the minimum distance average value;
the recursive clustering unit is used for performing recursive clustering on the average distance data set layer by layer according to the class table data set until a table data clustering tree of the uniformity table data set is generated;
and the clustering tree classification unit is used for performing learning classification on the basis of the table data clustering tree to generate the continuity table data set.
Further, the system further comprises:
a discreteness table data set obtaining unit, configured to obtain a discreteness table data set according to a complement of the continuity table data set;
a discrete persistence table data obtaining unit configured to obtain a discrete persistence table data set that exceeds a predetermined time threshold from among the discrete table data sets;
the variance calculation unit is used for performing variance calculation on the numerical values in the discrete persistence table data set, and taking the variance calculation result as the degree of deviation of the table data;
and the self-checking early warning unit is used for carrying out self-checking early warning on the meter data if the deviation degree of the meter data exceeds a preset deviation degree.
Further, the model analysis output module further includes:
the model construction unit is used for the multi-table combined self-checking model and comprises an input layer, a characteristic analysis layer, a self-checking analysis layer and an output layer;
the model input unit is used for inputting the multi-meter data acquisition information serving as an input layer into the characteristic analysis layer to obtain meter data operation characteristics;
the self-checking analysis unit is used for inputting the table data operation characteristics into the self-checking analysis layer to obtain a multi-table combined self-checking result;
and the model output unit is used for outputting the multi-table combined self-checking result as a model output result through the output layer.
Further, the display management module further includes:
a fault type obtaining unit, configured to obtain a table fault type according to the multi-table joint self-checking result;
the fault grade evaluation unit is used for carrying out fault grade evaluation on the multi-table combined self-detection result to obtain a table fault grade coefficient;
a fault solution formulation unit for formulating a table fault solution based on the table fault type and the table fault grade coefficient;
and the display control unit is used for performing display control on the meter fault type, the meter fault grade coefficient and the meter fault solution through the liquid crystal display module.
The application provides a multi-table combined self-checking method based on multi-module coordination, which comprises the following steps: acquiring a special transformer acquisition terminal, wherein the special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module and an interface conversion module; respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through the data sampling module; interface conversion is carried out on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information through the interface conversion module, so that heat meter conversion data information, water meter conversion data information and gas meter conversion data information are obtained; acquiring multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information; calling and obtaining a multi-table combined self-checking model through the CPU module; inputting the multi-table data acquisition information into the multi-table joint self-checking model for analysis, and outputting a multi-table joint self-checking result; and carrying out display management on the multi-table combined self-inspection result based on the liquid crystal display module. The technical problem that in the prior art, the self-checking intelligence degree of each meter device is not high, and the self-checking result is not accurate enough is solved. The technical effects of performing combined self-checking on the multi-meter through multi-module coordination, reducing investment cost, improving intellectualization of combined self-checking of the multi-meter and visual self-checking, and further ensuring accuracy of a multi-meter combined self-checking result are achieved.
The specification and drawings are merely illustrative of the present application, and it is intended that the present invention cover modifications and variations of this invention provided they come within the scope of the invention and their equivalents.
Claims (8)
1. A multi-table joint self-checking method based on multi-module coordination is characterized by comprising the following steps:
acquiring a special transformer acquisition terminal, wherein the special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module and an interface conversion module;
respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through the data sampling module;
interface conversion is carried out on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information through the interface conversion module, so that heat meter conversion data information, water meter conversion data information and gas meter conversion data information are obtained;
acquiring multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information;
calling and obtaining a multi-table combined self-checking model through the CPU module;
inputting the multi-table data acquisition information into the multi-table combined self-checking model for analysis, and outputting a multi-table combined self-checking result;
and carrying out display management on the multi-table combined self-inspection result based on the liquid crystal display module.
2. The method of claim 1, wherein obtaining the multi-table joint self-test model comprises:
acquiring historical ammeter data acquisition information, historical heat meter data acquisition information, historical water meter data acquisition information and historical gas meter data acquisition information;
respectively performing characteristic analysis on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information to obtain ammeter operation characteristics, heat meter operation characteristics, water meter operation characteristics and gas meter operation characteristics;
respectively carrying out self-checking model training according to the ammeter operation characteristics, the heat meter operation characteristics, the water meter operation characteristics and the gas meter operation characteristics to obtain an ammeter self-checking analysis model, a heat meter self-checking analysis model, a water meter self-checking analysis model and a gas meter self-checking analysis model;
extracting model parameters of the ammeter self-checking analysis model, the heat meter self-checking analysis model, the water meter self-checking analysis model and the gas meter self-checking analysis model to obtain ammeter analysis model parameters, heat meter analysis model parameters, water meter analysis model parameters and gas meter analysis model parameters;
and constructing the multi-meter combined self-checking model based on the electric meter analysis model parameters, the thermal meter analysis model parameters, the water meter analysis model parameters and the gas meter analysis model parameters.
3. The method of claim 2, wherein obtaining the meter operating characteristics, the heat meter operating characteristics, the water meter operating characteristics, and the gas meter operating characteristics comprises:
carrying out unsupervised learning classification on the historical electric meter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information respectively to generate a continuous meter data set;
constructing a table data change curve set according to the continuity table data set;
carrying out curvature analysis on the table data change curve to obtain a table data curvature change result set;
and inputting the table data curvature change result set into a table data characteristic evaluation model to respectively obtain the electric meter operation characteristic, the heat meter operation characteristic, the water meter operation characteristic and the gas meter operation characteristic.
4. The method of claim 3, wherein the generating the continuity table dataset comprises:
traversing access processing is respectively carried out on the historical ammeter data acquisition information, the historical heat meter data acquisition information, the historical water meter data acquisition information and the historical gas meter data acquisition information to generate a uniformity meter data set;
defining data in the uniformity table data set as P clusters;
carrying out average calculation on pairwise distances of data points in the P clusters to obtain an average distance data set;
obtaining a class table data set according to the average distance data set, wherein the class table data set comprises a classification set with the minimum distance average value;
performing layer-by-layer recursive clustering on the average distance data set according to the class table data set until a table data clustering tree of the uniformity table data set is generated;
and performing learning classification based on the table data clustering tree to generate the continuity table data set.
5. The method of claim 3, wherein the method comprises:
obtaining a discrete table data set according to the complement of the continuity table data set;
obtaining a discrete persistence table data set exceeding a predetermined time threshold in the discrete table data set;
carrying out variance calculation on the numerical values in the discrete persistence table data set, and taking the variance calculation result as the table data deviation degree;
and if the deviation degree of the meter data exceeds the preset deviation degree, carrying out self-checking early warning on the meter data.
6. The method of claim 1, wherein outputting the multi-table joint self-test result comprises:
the multi-table combined self-checking model comprises an input layer, a characteristic analysis layer, a self-checking analysis layer and an output layer;
inputting the multi-meter data acquisition information serving as an input layer into the characteristic analysis layer to obtain meter data operation characteristics;
inputting the table data operation characteristics into the self-checking analysis layer to obtain a multi-table combined self-checking result;
and outputting the multi-table combined self-checking result as a model output result through the output layer.
7. The method as claimed in claim 1, wherein said performing display management on the result of the multi-table combined self-test based on the lcd module comprises:
obtaining a table fault type according to the multi-table combined self-checking result;
performing fault grade evaluation on the multi-table combined self-detection result to obtain a table fault grade coefficient;
formulating a table fault solution based on the table fault type and the table fault grade coefficient;
and performing display management and control on the meter fault type, the meter fault grade coefficient and the meter fault solution through the liquid crystal display module.
8. A multi-table joint self-checking system based on multi-module coordination is characterized in that the system comprises:
the special transformer acquisition terminal acquisition module is used for acquiring a special transformer acquisition terminal, and the special transformer acquisition terminal comprises a CPU module, a data sampling module, a liquid crystal display module and an interface conversion module;
the data sampling module is used for respectively acquiring ammeter data acquisition information, heat meter data acquisition information, water meter data acquisition information and gas meter data acquisition information through the data sampling module;
the interface conversion module is used for performing interface conversion on the heat meter data acquisition information, the water meter data acquisition information and the gas meter data acquisition information through the interface conversion module to obtain heat meter conversion data information, water meter conversion data information and gas meter conversion data information;
the multi-meter data information acquisition module is used for acquiring multi-meter data acquisition information according to the electric meter data acquisition information, the heat meter conversion data information, the water meter conversion data information and the gas meter conversion data information;
the model calling module is used for calling the multi-table combined self-checking model through the CPU module;
the model analysis output module is used for inputting the multi-table data acquisition information into the multi-table joint self-checking model for analysis and outputting a multi-table joint self-checking result;
and the display management module is used for carrying out display management on the multi-table combined self-checking result based on the liquid crystal display module.
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