CN113823075A - Intelligent adjustment method and device for request waiting time delay of concentrator high-efficiency meter reading equipment - Google Patents
Intelligent adjustment method and device for request waiting time delay of concentrator high-efficiency meter reading equipment Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
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- H—ELECTRICITY
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses an intelligent adjusting method and device for request waiting time delay of concentrator high-efficiency meter reading equipment, wherein the concentrator reads meter data; the condition characteristic model management module screens and classifies the ammeter sample data; incremental calculation and learning of the condition characteristic model processing module and the characteristic processing module of the ammeter; the concentrator calculates the final request time delay according to the processing result and the success rate weighting of each module, and updates the success rate of each processing module according to the reading result of the sample.
Description
Technical Field
The invention relates to the technical field of intelligent ammeter communication, in particular to an intelligent adjusting method and device for the request waiting time delay of concentrator high-efficiency meter reading equipment.
Background
Domestic electricity information collection is developed at the end of the 80's 20 th century, and has been developed for more than 30 years so far. With the progress of computer technology, communication technology and automation technology and the continuous improvement of power marketing and management requirements, the industry development is greatly changed. Particularly, in 2005, the national grid company starts marketing modernization construction, and the electric energy information acquisition industry enters a high-speed development stage.
According to the data display of research and study of the prospective industry research institute, by 2012, the full-aperture users of the national power grid realize that the power utilization information acquisition covers 1.25 hundred million users, and the acquisition coverage rate reaches 40.45%. Wherein three-phase general industrial and commercial users, single-phase general industrial and commercial users and residential users account for more than 99% of the total number of users of the power grid, the number of the special transformer users accounts for nearly 1%, and the acquisition coverage rate of the special transformer users exceeds 70% at present.
According to the national network planning, the national network can realize 'full coverage, full collection and full cost control' for all users in the direct supply direct current region. At present, the construction of the main collection system stations of 27 provincial companies of the national grid company system is completely finished and put into operation, so that convenience is provided for the construction of the power utilization information collection system, and the development of the coverage rate of the national grid power utilization information collection system is further accelerated.
In the traditional concentrator equipment, when a terminal requests electric meter equipment data, a fixed waiting time delay is often set or simple single model adjustment is often performed, real-time incremental learning cannot be performed on various manufacturers, various electric meters and various types of electric meter data, and deep learning, mutual correction and intelligent adjustment cannot be performed according to real-time production environment conditions. For example: when the terminal reads different types of electric meters, different requests and waiting time delays are often caused due to different manufacturers, different communication types, different reading data types, different temperature environment factors and different other various factors, the original traditional scheme is not flexible and intelligent, great waiting time is wasted, acquisition efficiency is low, and the reading task issued by the master station cannot be completed within a specified time.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent adjustment method for the request waiting time delay of the concentrator high-efficiency reading electric meter equipment, and in order to achieve the purpose, the invention adopts the following technical scheme:
the intelligent adjustment method for the request waiting time delay of the concentrator high-efficiency meter reading equipment comprises the following steps:
s1: the concentrator reads data of different types of electric meters according to a configured fixed reading acquisition scheme so as to generate various data samples required by various data models, and initializes each condition characteristic model processing module M and a characteristic model processing module S of each electric meter according to default configuration;
s2: the condition characteristic model management module in the concentrator screens and classifies according to different data types, and inputs the sample data A to the condition characteristic model processing module M of the corresponding category;
s3: the condition characteristic model processing module M performs incremental calculation and learning, and obtains a request time delay R1;
s4: the condition characteristic model management module inputs the sample data A again to a self characteristic model processing module S of the corresponding ammeter;
s5: the self characteristic model processing module S performs incremental calculation and learning and obtains a request time delay R2;
s6: the concentrator respectively weights and corrects the results R1 and R2 according to the success rates of the conditional feature model processing module M and the self feature model processing module S, and calculates the final request time delay R;
s7: the concentrator uses the final request time delay R to read the sample data A;
s8.1: based on S7, if the reading result of the current sample data A is successfully read by the concentrator, updating the success rate of the condition characteristic model M and the self characteristic model S, and using the current actual value as statistical data;
s8.2: based on the step S7, if the reading result of the current sample data a fails, the success rate of the condition feature model M and the self feature model S is updated, but the current actual value is not used as the statistical value.
Preferably, based on the step S8.1, the process of performing two additional corrections to update the statistics of other modules includes the following steps:
s8.1.1: the condition feature model management module detects whether the condition feature model processing module M after incremental calculation and learning is stable, if the standard deviation of the condition feature model processing module M is larger than 1s, the factor is considered to be extremely unstable, the condition feature model processing module M is divided into a condition feature model processing module M1 and a condition feature model processing module M2, the condition feature model processing module M1 and the condition feature model processing module M2 are initialized by using the statistical value of the condition feature model processing module M, and then after S8.1.2 is waited to complete subsequent calculation;
s8.1.2: and the condition characteristic model management module detects whether the standard deviation and the expected value of the other condition characteristic model processing module M3 and the condition characteristic model processing module M4 are stable and can meet the merging requirement according to the sorting result, if the standard deviation and the expected value of the condition characteristic model processing module M3 and the condition characteristic model processing module M4 are less than 0.1s and the difference between the expected values is less than 0.2s, the standard deviation and the expected value are merged into a condition characteristic model processing module Mz, the Mz is initialized by using the statistical values of the condition characteristic model processing module M3 and the condition characteristic model processing module M4, the statistics and the correction are completed at this time, and the calculation is finished.
Preferably, the final calculation method of the request time delay R in step S6 is as follows:
the statistical values of the condition characteristic model processing module M and the self characteristic model processing module S which belong to the current request type of the ammeter are as follows:
the expected μ 1 and standard deviation σ 1 of M, the number of successes n1 and the total number s1, the success rate r1 being n1/s 1;
desired μ 2 and standard deviation σ 2 of S, success number n2 and total number S2, success rate r2 ═ n 2/S2;
the formula for calculating the time delay R of the current request of the electric meter is as follows:
R＝μ1*k1+μ2*k2+x
wherein k1 and k2 are the expected weight values corresponding to M and S, respectively 0.5, the sum of which is 1, and x is a correction value, and if the previous request of the category sample fails, the calculation formula of x is as follows:
x＝(σ1*k3+σ2*k4)*(1-r1)*(1-r2)+b
wherein k3 and k4 are standard deviation weighted values corresponding to M and S, which are respectively 0.5, the sum of the weighted values is 1, b is the minimum offset, and the default is 1S.
If the previous request for the category sample is successful, the calculation formula of x is as follows:
x＝-(σ1*k3+σ2*k4)*(1-r1)*(1-r2)。
compared with the prior art, the invention has the following beneficial effects:
(1) according to the invention, the concentrator is used for reading data of different types of electric meters according to a configured fixed reading acquisition scheme, so that various data samples required by various data models are generated and continuously input into the condition characteristic model processing module M and the self characteristic model processing module S, the time delay value of the time can be accurately calculated, and real-time incremental learning and intelligent adjustment can be carried out on different types of electric meters; and the condition characteristic model processing module M is added to carry out incremental calculation and second additional correction in the learning process, so that estimation value errors caused by overlarge deviation of self abnormal values are avoided, and meanwhile, the condition characteristic model processing module M can be combined and split according to statistical results, so that repeated calculation is effectively reduced, the calculated amount and the storage space of a terminal are reduced, the communication failure times of all electric meters are finally reduced, and the meter reading efficiency is improved.
(2) When the quantity of the electric meter equipment types and the reading data types connected with the concentrator is large, the types of the condition data models are more, and meanwhile, when the times of data sample training are more, the condition characteristic data models and the characteristic data models of the electric meters are more accurate, so that the purpose of efficiently and intelligently adjusting the request time delay is realized.
Drawings
FIG. 1 is an overall flow chart of the present invention.
FIG. 2 is a flow chart of the second additional correction of the present invention.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
As shown in fig. 1, S1: the concentrator reads data of different types of electric meters according to the configured fixed reading acquisition scheme, so that various data samples required by various data models are generated, and the specific expression is as follows: for different communication type electric meters, data are collected in different modes, meanwhile, different databases can be selected for speed and capacity improvement according to terminal performance through data storage, a concentrator initializes each condition feature model processing module M and a self feature model processing module S of each electric meter according to default configuration, (when the concentrator leaves a factory, each condition feature model processing module (M, M1, M2..... Mx) and a self feature model processing module (S, S1, S2...... Sy) of each table can be initialized according to default configuration), and the types of generated initial condition feature models are also few and simple and are usually an initial value obtained according to production experience of different manufacturers);
s2: the condition characteristic model management module in the concentrator screens and classifies according to different data types, and inputs the sample data A to the condition characteristic model processing module M of the corresponding category;
s3: the condition characteristic model processing module M performs incremental calculation and learning, and obtains a request time delay R1;
s4: the condition characteristic model management module inputs the sample data A again to a self characteristic model processing module S of the corresponding ammeter;
s5: the self characteristic model processing module S performs incremental calculation and learning and obtains a request time delay R2;
and (3) incremental calculation and learning of the condition characteristic model processing module M and the characteristic processing module S of the ammeter, wherein all sample data are processed respectively according to characteristic attribute categories, for example, the forward active electric quantity of the ammeter of a certain manufacturer is read, each record comprises the freezing time, the ammeter type and the communication type of data, the single-phase PLC of the manufacturer reads the forward active electric quantity in 12: 00-15: 00 on a whiteday basis as a joint candidate column, and all samples meeting the condition participate in statistics.
S6: the concentrator respectively weights and corrects the results R1 and R2 according to the success rates of the conditional feature model processing module M and the self feature model processing module S, and calculates the final request delay R, in this embodiment, the request delay data sample of the electric meter obeys normal distribution, that is:
s7: the concentrator uses the final request time delay R to read the sample data A;
s8.1: based on S7, if the reading result of the current sample data A is successfully read by the concentrator, updating the success rate of the condition characteristic model M and the self characteristic model S, and using the current actual value as statistical data;
s8.2: based on the step S7, if the reading result of the current sample data a fails, the success rate of the condition feature model M and the self feature model S is updated, but the current actual value is not used as the statistical value.
In the process of performing incremental calculation and learning by the condition feature model processing module M in step S3, the terminal initially configures default feature attributes to candidate columns, where the attributes are atomic and inseparable, such as different manufacturers, electric meter types, reading time, request types, communication types, and the like, the terminal calculates the request delay R of each electric meter under different candidate columns by continuously acquiring electric meter data, calculates the expected μ and the standard deviation σ, and a sample failed in acquisition will not participate in the calculation due to no numerical value, but will serve as a reference value for subsequently calculating the delay R;
as shown in fig. 2, the concentrator succeeds in reading the current sample data a, updates the success rate of the conditional feature model M and the feature model S of the concentrator, and performs secondary additional correction after using the current actual value as statistical data, and updates the statistical values of other modules, which includes the following steps:
s8.1.1: the condition feature model management module detects whether the condition feature model processing module M after incremental calculation and learning is stable, if the standard deviation of the condition feature model processing module M is larger than 1s, the factor is considered to be extremely unstable, the condition feature model processing module M is divided into a condition feature model processing module M1 and a condition feature model processing module M2, the condition feature model processing module M1 and the condition feature model processing module M2 are initialized by using the statistical value of the condition feature model processing module M, and then after S8.1.2 is waited to complete subsequent calculation;
s8.1.2: and the condition characteristic model management module detects whether the standard deviation and the expected value of the other condition characteristic model processing module M3 and the condition characteristic model processing module M4 are stable and can meet the merging requirement according to the sorting result, if the standard deviation and the expected value of the condition characteristic model processing module M3 and the condition characteristic model processing module M4 are less than 0.1s and the difference between the expected values is less than 0.2s, the standard deviation and the expected value are merged into a condition characteristic model processing module Mz, the Mz is initialized by using the statistical values of the condition characteristic model processing module M3 and the condition characteristic model processing module M4, the statistics and the correction are completed at this time, and the calculation is finished.
At the stage of steps S8.1.1 and S8.1.2, the number of atomic columns of the joint candidate column may be more than 2, in this embodiment, only two are selected for explanation, and according to the actual field use condition, the number of atomic columns can be continuously increased as long as the condition is satisfied, and the condition feature model processing module M can automatically merge and split according to the statistical result, thereby effectively reducing the repeated calculation, reducing the calculation amount and storage space of the terminal, and finally reducing the number of communication failures of all the electric meters and improving the meter reading efficiency.
The final calculation method of the request time delay R in step S6 is: the statistical values of the condition characteristic model processing module M and the self characteristic model processing module S which belong to the current request type of the ammeter are as follows:
the expected μ 1 and standard deviation σ 1 of M, the number of successes n1 and the total number s1, the success rate r1 being n1/s 1;
desired μ 2 and standard deviation σ 2 of S, success number n2 and total number S2, success rate r2 ═ n 2/S2;
the formula for calculating the time delay R of the current request of the electric meter is as follows:
R＝μ1*k1+μ2*k2+x
wherein k1 and k2 are the expected weight values corresponding to M and S, respectively 0.5, the sum of which is 1, and x is a correction value, and if the previous request of the category sample fails, the calculation formula of x is as follows:
x＝(σ1*k3+σ2*k4)*(1-r1)*(1-r2)+b
where k3 and k4 are standard deviation weighted values corresponding to M and S, respectively, 0.5, the sum of which is 1, and b is the minimum offset, which is set to 1S in this embodiment.
If the previous request for the category sample is successful, the calculation formula of x is as follows:
x＝-(σ1*k3+σ2*k4)*(1-r1)*(1-r2)。
according to the final calculation method of the request time delay R, the adjustment amplitude is related to the standard deviation and the success rate of the condition characteristic model processing module M and the self characteristic model processing module S, the larger the standard deviation is, the larger the adjustment is, and otherwise, the smaller the adjustment is; the higher the success rate, the smaller the adjustment value, and conversely, the larger the adjustment. Meanwhile, it can also be observed that when the sample succeeds in the category last time, the adjustment is small because a stable value is reached; in failure, due to the fact that a proper value needs to be found quickly, adjustment is large, through a large amount of data processing, the time delay parameter estimated finally is closer to the actual situation, even if the failure situation occurs, recovery can be conducted quickly, in addition, the minimum offset b can be set according to the actual situation, a statistical analysis value is made in the follow-up consideration, and expectation of the statistical analysis value is calculated.
The above-mentioned embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and therefore, modifications, equivalent changes, improvements, etc. made in the claims of the present invention are still included in the scope of the present invention.
Claims (3)
1. The intelligent adjustment method for the request waiting time delay of the concentrator high-efficiency meter reading equipment is characterized by comprising the following steps of:
s1: the concentrator reads data of different types of electric meters according to a configured fixed reading acquisition scheme so as to generate various data samples required by various data models, and initializes each condition characteristic model processing module M and a characteristic model processing module S of each electric meter according to default configuration;
s2: the condition characteristic model management module in the concentrator screens and classifies according to different data types, and inputs the sample data A to the condition characteristic model processing module M of the corresponding category;
s3: the condition characteristic model processing module M performs incremental calculation and learning, and obtains a request time delay R1;
s4: the condition characteristic model management module inputs the sample data A again to a self characteristic model processing module S of the corresponding ammeter;
s5: the self characteristic model processing module S performs incremental calculation and learning and obtains a request time delay R2;
s6: the concentrator respectively weights and corrects the results R1 and R2 according to the success rates of the conditional feature model processing module M and the self feature model processing module S, and calculates the final request time delay R;
s7: the concentrator uses the final request time delay R to read the sample data A;
s8.1: based on S7, if the reading result of the current sample data A is successfully read by the concentrator, updating the success rate of the condition characteristic model M and the self characteristic model S, and using the current actual value as statistical data;
s8.2: based on the step S7, if the reading result of the current sample data a fails, the success rate of the condition feature model M and the self feature model S is updated, but the current actual value is not used as the statistical value.
2. The intelligent adjustment method for the request waiting time delay of the concentrator high-efficiency meter reading equipment according to claim 1, is characterized in that: a process of performing secondary additional correction based on the step S8.1, and updating the statistics of other modules, the steps of which are as follows:
s8.1.1: the condition feature model management module detects whether the condition feature model processing module M after incremental calculation and learning is stable, if the standard deviation of the condition feature model processing module M is larger than 1s, the factor is considered to be extremely unstable, the condition feature model processing module M is divided into a condition feature model processing module M1 and a condition feature model processing module M2, the condition feature model processing module M1 and the condition feature model processing module M2 are initialized by using the statistical value of the condition feature model processing module M, and then after S8.1.2 is waited to complete subsequent calculation;
s8.1.2: and the condition characteristic model management module detects whether the standard deviation and the expected value of the other condition characteristic model processing module M3 and the condition characteristic model processing module M4 are stable and can meet the merging requirement according to the sorting result, if the standard deviation and the expected value of the condition characteristic model processing module M3 and the condition characteristic model processing module M4 are less than 0.1s and the difference between the expected values is less than 0.2s, the standard deviation and the expected value are merged into a condition characteristic model processing module Mz, the Mz is initialized by using the statistical values of the condition characteristic model processing module M3 and the condition characteristic model processing module M4, the statistics and the correction are completed at this time, and the calculation is finished.
3. The intelligent adjustment method for the request waiting time delay of the concentrator high-efficiency meter reading equipment according to claim 1, is characterized in that: the final calculation method of the request time delay R in step S6 is:
the statistical values of the condition characteristic model processing module M and the self characteristic model processing module S which belong to the current request type of the ammeter are as follows:
the expected μ 1 and standard deviation σ 1 of M, the number of successes n1 and the total number s1, the success rate r1 being n1/s 1;
desired μ 2 and standard deviation σ 2 of S, success number n2 and total number S2, success rate r2 ═ n 2/S2;
the formula for calculating the time delay R of the current request of the electric meter is as follows:
R＝μ1*k1+μ2*k2+x
wherein k1 and k2 are the expected weight values corresponding to M and S, respectively 0.5, the sum of which is 1, and x is a correction value, and if the previous request of the category sample fails, the calculation formula of x is as follows:
x＝(σ1*k3+σ2*k4)*(1-r1)*(1-r2)+b
wherein k3 and k4 are standard deviation weighted values corresponding to M and S, respectively 0.5, the sum of which is 1, b is the minimum offset, defaults to 1S,
if the previous request for the category sample is successful, the calculation formula of x is as follows:
x＝-(σ1*k3+σ2*k4)*(1-r1)*(1-r2)。
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