CN117040137B - Ring main unit temperature rise early warning method, system, terminal and medium based on multi-source data - Google Patents
Ring main unit temperature rise early warning method, system, terminal and medium based on multi-source data Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
Abstract
The invention discloses a ring main unit temperature rise early warning method, a ring main unit temperature rise early warning system, a ring main unit temperature rise early warning terminal and a ring main unit temperature rise early warning medium based on multi-source data, which relate to the technical field of power equipment and are characterized in that: establishing a history database; acquiring real-time temperature data of the ring main unit and real-time monitoring data of influence factors; matching historical monitoring data with the maximum similarity according to the real-time monitoring data, and extracting historical temperature data and temperature reference data of the ring main unit; the real-time temperature data are equivalent to the weight values of the historical temperature data corresponding to each influence factor, and weight coefficients are distributed to each influence factor; performing weight calculation according to the temperature reference data and the weight coefficient to obtain a temperature predicted value; and carrying out temperature rise early warning on the section exceeding the early warning threshold value in the temperature predicted value. According to the method, the temperature rise early warning of the ring main unit in different scenes can be realized under the condition that a large amount of sample data is not needed, and the timeliness and the accuracy of the temperature rise early warning of the ring main unit are effectively ensured.
Description
Technical Field
The invention relates to the technical field of power equipment, in particular to a ring main unit temperature rise early warning method, a ring main unit temperature rise early warning system, a ring main unit terminal and a ring main unit temperature rise early warning medium based on multi-source data.
Background
The ring main unit is one of important equipment in a power supply system, and normal operation of the ring main unit is an important guarantee for power supply safety and stability, and as the ring main unit is relatively closed equipment, the ring main unit is easily caused to have over-high internal temperature along with the influence of factors such as load current increase, insulation performance weakening, insufficient heat dissipation power, increase of humidity in the ring main unit, increase of temperature outside the ring main unit and the like, so that the normal operation of the ring main unit is influenced. For this reason, temperature monitoring of the ring main unit is necessary.
The traditional ring main unit temperature monitoring method mainly comprises the steps of comparing real-time temperature data with a set threshold value, and outputting an alarm signal when the real-time temperature data exceeds the set threshold value; when the set threshold is too large, the timeliness of temperature monitoring and alarming is poor; when the set threshold is too small, the temperature monitoring alarm error rate is high, and the alarm is frequent. Therefore, in the prior art, the machine learning algorithm is adopted to train the historical ring main unit temperature data, and the temperature of the ring main unit is predicted through the temperature prediction model constructed by training, so that the aim of early warning in time is fulfilled; in addition, it is recorded that the temperature change of the ring main unit is estimated by detecting faults which can affect the temperature rise in the ring main unit in real time and integrating all detected faults, so that the ring main unit temperature monitoring is completed.
However, because the temperature change of the ring main unit is not only affected by faults but also affected by time accumulation effect, and the obtained temperature monitoring data has time lag and nonlinearity problems, the temperature detection method based on the machine learning algorithm needs more sample data, especially the ring main unit at different positions in different power supply systems is required to monitor the temperature, so that a large amount of sample data is necessarily required, and the application cost is high, and the method is difficult to popularize and apply on a large scale; the temperature monitoring is carried out by adopting a method for detecting faults in real time, so that the temperature mutation condition caused by the mutual influence among different factors is easily ignored, and the temperature monitoring of the ring main unit cannot be covered comprehensively. Therefore, how to research and design a ring main unit temperature rise early warning method, a system, a terminal and a medium based on multi-source data, which can overcome the defects, is a problem which needs to be solved in the current state.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide the ring main unit temperature rise early warning method, the system, the terminal and the medium based on the multi-source data, so that the temperature rise early warning of the ring main unit in different scenes can be realized under the condition that a large amount of sample data is not needed, and the timeliness and the accuracy of the ring main unit temperature rise early warning are effectively ensured.
The technical aim of the invention is realized by the following technical scheme:
in a first aspect, a ring main unit temperature rise early warning method based on multi-source data is provided, which includes the following steps:
determining an influence factor set influencing the temperature change of the ring main unit, and establishing a history database according to data for history monitoring of each influence factor in the influence factor set;
acquiring real-time temperature data of the ring main unit in a monitoring period and real-time monitoring data corresponding to each influence factor in the influence factor set;
according to the real-time monitoring data, matching the historical monitoring data with the maximum similarity from a historical database, and extracting the historical temperature data of the ring main unit in the same monitoring period with the historical monitoring data and the temperature reference data of the ring main unit in the next monitoring period from the historical database;
the real-time temperature data are equivalent to the weight values of the historical temperature data corresponding to each influence factor, and weight coefficients are distributed to each influence factor;
Carrying out weight calculation according to the temperature reference data and the weight coefficient of each influence factor to obtain a temperature predicted value of the ring main unit in the next period to be early-warned and monitored;
and screening out a section exceeding the early warning threshold value from the temperature predicted value, and carrying out temperature rise early warning on the section exceeding the early warning threshold value.
Further, the influencing factors include load current, outside temperature, heat dissipation power and inside humidity.
Further, the expression for matching the historical monitoring data with the maximum similarity from the historical database according to the real-time monitoring data is specifically:
；
Wherein, Representing the influencing factor/>The corresponding real-time monitoring data is the/>A data value of the moment; /(I)Representing the influencing factor/>The corresponding historical monitoring data is the/>A data value of the moment; /(I)Representing the duration of the monitoring period; /(I)Representation and influencing factor/>Residual factor/>, corresponding to real-time monitoring data in the same monitoring periodThe corresponding real-time monitoring data average value; /(I)Representation and influencing factor/>Residual factor/>, corresponding to historical monitoring data in the same monitoring periodThe corresponding historical monitoring data average value; /(I)Representing the residual factor/>Is a difference threshold of (2); /(I)Indicating the influence factor concentration divided by the influence factor/>All remaining factors except; /(I)Representing the set of influencing factors.
Further, the weight coefficient allocation process of the influence factor specifically includes:
Randomly distributing weight coefficients to each influence factor in the influence factor set;
carrying out weight calculation on the temperature value of the historical temperature data corresponding to each influence factor at the same moment according to the weight coefficient to obtain a real-time temperature predicted value at the corresponding moment;
and carrying out error analysis on the real-time temperature data and the real-time temperature predicted values at all moments, and selecting the weight coefficient which is allocated corresponding to the minimum error as the weight coefficient which is finally allocated.
Further, the weight coefficient allocation formula of the influence factor specifically includes:
;
Wherein, Representation of the monitoring period/>A real-time temperature pre-estimated value of time; /(I)Represents the/>A weight coefficient of each influence factor; /(I)Represents the/>The/>, in the historical temperature data corresponding to the influence factorsA temperature value at a time; /(I)Representing the number of influence factors in the set of influence factors; /(I)Representing the duration of the monitoring period; /(I)Representing the/>, in real-time temperature dataReal-time temperature value at time.
Further, the weight coefficient allocation process of the influence factor specifically includes:
Taking a real-time temperature value at a certain moment in the real-time temperature data as a weight value of a temperature value at the same corresponding moment in each historical temperature data, and distributing the weight coefficient at the corresponding moment for each influence factor;
Analyzing errors of weight coefficients distributed at all moments in a monitoring period of a single influence factor;
And solving to obtain the optimal weight coefficient mean value of each influence factor as a final weight coefficient by taking the minimum sum of errors of the weight coefficients allocated to all the influence factors as an optimization target.
Further, the weight coefficient allocation formula of the influence factor specifically includes:
;
Wherein, Representing the/>, in real-time temperature dataA real-time temperature value at a moment; /(I)Represents the/>The individual influencing factors are at the/>The weight coefficient of the moment; /(I)Representing the number of influence factors in the set of influence factors; /(I)Represents the/>The/>, in the historical temperature data corresponding to the influence factorsA temperature value at a time; /(I)Representing the duration of the monitoring period; /(I)Represents the/>The weight coefficient mean value of each influence factor at all moments.
In a second aspect, a ring main unit temperature rise early warning system based on multi-source data is provided, including:
the factor determining module is used for determining an influence factor set influencing the temperature change of the ring main unit and establishing a history database according to the data of history monitoring of each influence factor in the influence factor set;
The data acquisition module is used for acquiring real-time temperature data of the ring main unit in the monitoring period and real-time monitoring data corresponding to each influence factor in the influence factor set;
The data matching module is used for matching the historical monitoring data with the maximum similarity from the historical database according to the real-time monitoring data, and extracting the historical temperature data of the ring main unit in the same monitoring period with the historical monitoring data and the temperature reference data of the ring main unit in the next monitoring period from the historical database;
the weight distribution module is used for enabling the real-time temperature data to be equivalent to the weight value of the historical temperature data corresponding to each influence factor, and distributing weight coefficients for each influence factor;
The temperature prediction module is used for carrying out weight calculation according to the temperature reference data and the weight coefficient of each influence factor to obtain a temperature prediction value of the ring main unit in the next period to be early-warned and monitored;
And the temperature rise early warning module is used for screening out the section exceeding the early warning threshold value from the temperature predicted value and carrying out temperature rise early warning on the section exceeding the early warning threshold value.
In a third aspect, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the ring main unit temperature rise early warning method based on multi-source data according to any one of the first aspects when executing the program.
In a fourth aspect, a computer readable medium is provided, on which a computer program is stored, where the computer program is executed by a processor to implement the multi-source data-based ring main unit temperature rise early warning method according to any one of the first aspects.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the ring main unit temperature rise early warning method based on the multi-source data, the historical approximate scenes of the single influence factor on the ring main unit temperature are matched according to the similarity analysis between the historical monitoring data and the real-time monitoring data, the real-time simulation scenes of the ring main unit are simulated more truly by combining the historical approximate scenes of a plurality of influence factors, the weight coefficient of each influence factor is determined by the contribution degree of each influence factor to the ring main unit temperature in the real-time simulation scenes, meanwhile, the temperature predicted value of the ring main unit in the next monitoring period to be early warned is estimated by combining the temperature change condition of each historical approximate scene in the next monitoring period, and the early warning of the temperature rise of the ring main unit in different scenes can be realized under the condition that a large amount of sample data is not needed, so that the timeliness and the accuracy of the ring main unit temperature rise early warning are effectively ensured;
2. When the historical monitoring data with the maximum similarity is matched from the historical database, the similarity between the real-time monitoring data and the historical monitoring data is considered, and the difference value between the real-time monitoring data corresponding to other residual factors in the influence factor set and the historical monitoring data does not exceed the set threshold value is used as a constraint condition, so that the data under the condition of abrupt change is filtered, and the reliability of the matched data is effectively ensured;
3. When the real-time temperature data are equivalent to the weight values of the historical temperature data corresponding to each influence factor, the weight calculation is carried out on the temperature values of the historical temperature data corresponding to each influence factor at the same moment according to the randomly distributed weight coefficients, and the overall influence angle of all the influence factors on the ring main unit temperature is analyzed, so that the method is more suitable for the conditions of large number of the influence factors and complex interaction;
4. When the real-time temperature data is equivalent to the weight value of the historical temperature data corresponding to each influence factor, the real-time temperature value at a certain moment in the real-time temperature data is used as the weight value of the temperature value at the same corresponding moment in each historical temperature data, the local influence angle of the single influence factor on the ring main unit temperature is analyzed, and the method is more suitable for the conditions of small quantity of influence factors and relatively simple interaction.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a flow chart in embodiment 1 of the present invention;
Fig. 2 is a system block diagram in embodiment 3 of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1: the ring main unit temperature rise early warning method based on the multi-source data is specifically realized by the following steps as shown in fig. 1.
Step one: and determining an influence factor set influencing the temperature change of the ring main unit, and establishing a history database according to the data of history monitoring of each influence factor in the influence factor set.
The influencing factors include, but are not limited to, load current, outside temperature, heat dissipation power and inside humidity, and may also include factors such as partial discharge frequency, cable insulation performance, and the like. Wherein the load current may be between the currents of the respective load terminals. The heat dissipation power can be the real-time power of the radiator or the air quantity in the heat exchange process.
When the data of the historical monitoring of the influence factors is obtained, the change data of all the influence factors in the monitoring period and the change data of the temperature in the ring main unit are required to be obtained.
For this purpose, the data in the history database can be unified on a time axis, and the data at each moment on the time axis is a data set including the change data of all the influencing factors and the change data of the temperature in the ring main unit.
Step two: and acquiring real-time temperature data of the ring main unit in the monitoring period and real-time monitoring data corresponding to each influence factor in the influence factor set. The real-time temperature data and the real-time monitoring data can be acquired in real time through corresponding sensors.
If the ring main unit temperature rise early warning of the next working day is needed, the collected real-time temperature data and real-time monitoring data are all data in the current working.
Step three: and matching the historical monitoring data with the maximum similarity from the historical database according to the real-time monitoring data, and extracting the historical temperature data of the ring main unit in the same monitoring period as the historical monitoring data and the temperature reference data of the ring main unit in the next monitoring period from the historical database.
The influence factor set including the influence factor x, the influence factor y, and the influence factor z is described as an example. When the real-time monitoring data of the influence factor x is matched with the history monitoring data from the history database, if the history monitoring data matched with the influence factor x is the history monitoring data x, the data monitoring period of the history monitoring data x is T1, and the next period of the T1 is defined as T2. For example, T1 is the 5 th day before, and T2 is the 4 th day before. Then, it is also necessary to extract the historical temperature data C1 of the ring main unit at T1 and the temperature reference data C2 of the ring main unit at T2 from the database.
The expression of the history monitoring data with the maximum similarity is specifically:
；
Wherein, Representing the influencing factor/>The corresponding real-time monitoring data is the/>A data value of the moment; /(I)Representing the influencing factor/>The corresponding historical monitoring data is the/>A data value of the moment; /(I)Representing the duration of the monitoring period; /(I)Representation and influencing factor/>Residual factor/>, corresponding to real-time monitoring data in the same monitoring periodThe corresponding real-time monitoring data average value; /(I)Representation and influencing factor/>Residual factor/>, corresponding to historical monitoring data in the same monitoring periodThe corresponding historical monitoring data average value; /(I)Representing the residual factor/>Is a difference threshold of (2); /(I)Indicating the influence factor concentration divided by the influence factor/>All remaining factors except; /(I)Representing the set of influencing factors.
It should be noted that if the influence factor in the above formulaFor the influence factor x, then the remaining factors are the influence factor y or the influence factor z. In addition, the sum of absolute values of the differences is the minimum in the formula as an optimization target solving process, and the absolute value of the difference can be replaced by the minimum sum of squares of the differences, and can also be replaced by the square correction of the differences.
When the historical monitoring data with the maximum similarity is matched from the historical database, the method not only considers the similarity between the real-time monitoring data and the historical monitoring data, but also takes the difference value between the real-time monitoring data corresponding to other residual factors in the influence factor set and the historical monitoring data not exceeding a set threshold value as a constraint condition, so that the data under the condition of abrupt change is filtered, and the reliability of the matched data is effectively ensured.
Step four: and (3) enabling the real-time temperature data to be equivalent to the weight value of the historical temperature data corresponding to each influence factor, and distributing weight coefficients for each influence factor.
The weight coefficient distribution process of the influence factors specifically comprises the following steps: randomly distributing weight coefficients to each influence factor in the influence factor set; carrying out weight calculation on the temperature value of the historical temperature data corresponding to each influence factor at the same moment according to the weight coefficient to obtain a real-time temperature predicted value at the corresponding moment; and carrying out error analysis on the real-time temperature data and the real-time temperature predicted values at all moments, and selecting the weight coefficient which is allocated corresponding to the minimum error as the weight coefficient which is finally allocated.
For example, the weight coefficient assignment formula of the influence factor is specifically:
;
Wherein, Representation of the monitoring period/>A real-time temperature pre-estimated value of time; /(I)Represents the/>A weight coefficient of each influence factor; /(I)Represents the/>The/>, in the historical temperature data corresponding to the influence factorsA temperature value at a time; /(I)Representing the number of influence factors in the set of influence factors; /(I)Representing the duration of the monitoring period; /(I)Representing the/>, in real-time temperature dataReal-time temperature value at time.
The first time of the historical temperature data, the real-time temperature data and the real-time temperature predicted valueThe time is calculated from the starting point of the monitoring period in which the corresponding data is located.
When the real-time temperature data is equivalent to the weight value of the historical temperature data corresponding to each influence factor, the weight calculation is carried out on the temperature value of the historical temperature data corresponding to each influence factor at the same moment according to the randomly allocated weight coefficient, and the overall influence angle of all the influence factors on the ring main unit temperature is analyzed, so that the method is more suitable for the conditions of large number of the influence factors and complex interaction.
Step five: and carrying out weight calculation according to the temperature reference data and the weight coefficient of each influence factor to obtain a temperature predicted value of the ring main unit in the next period to be early-warned and monitored. The next cycle of monitoring to be alerted is understood herein as the next working day described above.
Step six: and screening out a section exceeding the early warning threshold value from the temperature predicted value, and carrying out temperature rise early warning on the section exceeding the early warning threshold value. The early warning threshold value can be a temperature critical value corresponding to the normal operation of the ring main unit.
Example 2: embodiment 2 is different from embodiment 1 in the distribution process of the weight coefficient.
In this embodiment, the weight coefficient allocation process of the influence factor specifically includes: taking a real-time temperature value at a certain moment in the real-time temperature data as a weight value of a temperature value at the same corresponding moment in each historical temperature data, and distributing the weight coefficient at the corresponding moment for each influence factor; analyzing errors of weight coefficients distributed at all moments in a monitoring period of a single influence factor; and solving to obtain the optimal weight coefficient mean value of each influence factor as a final weight coefficient by taking the minimum sum of errors of the weight coefficients allocated to all the influence factors as an optimization target.
The real-time temperature data is used as the weight value of the historical temperature data corresponding to each influence factor
For example, the weight coefficient assignment formula of the influence factor is specifically:
;
Wherein, Representing the/>, in real-time temperature dataA real-time temperature value at a moment; /(I)Represents the/>The individual influencing factors are at the/>The weight coefficient of the moment; /(I)Representing the number of influence factors in the set of influence factors; /(I)Represents the/>The/>, in the historical temperature data corresponding to the influence factorsA temperature value at a time; /(I)Representing the duration of the monitoring period; /(I)Represents the/>The weight coefficient mean value of each influence factor at all moments.
When the real-time temperature data is equivalent to the weight value of the historical temperature data corresponding to each influence factor, the real-time temperature value at a certain moment in the real-time temperature data is used as the weight value of the temperature value at the same corresponding moment in each historical temperature data, the local influence angle of the single influence factor on the ring main unit temperature is analyzed, and the method is more suitable for the conditions of small quantity of influence factors and relatively simple interaction.
Example 3: the ring main unit temperature rise early warning system based on the multi-source data is used for realizing the ring main unit temperature rise early warning method based on the multi-source data described in the embodiment 1 or the embodiment 2, and comprises a factor determining module, a data acquisition module, a data matching module, a weight distribution module, a temperature prediction module and a temperature rise early warning module as shown in fig. 2.
The factor determining module is used for determining an influence factor set which influences the temperature change of the ring main unit and establishing a history database according to data for history monitoring of each influence factor in the influence factor set; the data acquisition module is used for acquiring real-time temperature data of the ring main unit in the monitoring period and real-time monitoring data corresponding to each influence factor in the influence factor set; the data matching module is used for matching the historical monitoring data with the maximum similarity from the historical database according to the real-time monitoring data, and extracting the historical temperature data of the ring main unit in the same monitoring period with the historical monitoring data and the temperature reference data of the ring main unit in the next monitoring period from the historical database; the weight distribution module is used for enabling the real-time temperature data to be equivalent to the weight value of the historical temperature data corresponding to each influence factor, and distributing weight coefficients for each influence factor; the temperature prediction module is used for carrying out weight calculation according to the temperature reference data and the weight coefficient of each influence factor to obtain a temperature prediction value of the ring main unit in the next period to be early-warned and monitored; and the temperature rise early warning module is used for screening out the section exceeding the early warning threshold value from the temperature predicted value and carrying out temperature rise early warning on the section exceeding the early warning threshold value.
Working principle: according to the method, the historical approximate scenes of the influence of the single influence factors on the temperature of the ring main unit are matched according to similarity analysis between the historical monitoring data and the real-time monitoring data, the real-time simulation scenes of the ring main unit are simulated more truly by combining the historical approximate scenes of the influence factors, the weight coefficient of each influence factor is determined according to the contribution degree of each influence factor to the temperature of the ring main unit in the real-time simulation scenes, meanwhile, the temperature predicted value of the ring main unit in the next monitoring period to be pre-warned is estimated by combining the temperature change condition of each historical approximate scene in the next monitoring period, and the temperature rise pre-warning of the ring main unit in different scenes can be realized under the condition that a large amount of sample data is not needed, and the timeliness and the accuracy of the temperature rise pre-warning of the ring main unit are effectively ensured.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. The ring main unit temperature rise early warning method based on the multi-source data is characterized by comprising the following steps of:
determining an influence factor set influencing the temperature change of the ring main unit, and establishing a history database according to data for history monitoring of each influence factor in the influence factor set;
acquiring real-time temperature data of the ring main unit in a monitoring period and real-time monitoring data corresponding to each influence factor in the influence factor set;
according to the real-time monitoring data, matching the historical monitoring data with the maximum similarity from a historical database, and extracting the historical temperature data of the ring main unit in the same monitoring period with the historical monitoring data and the temperature reference data of the ring main unit in the next monitoring period from the historical database;
the real-time temperature data are equivalent to the weight values of the historical temperature data corresponding to each influence factor, and weight coefficients are distributed to each influence factor;
Carrying out weight calculation according to the temperature reference data and the weight coefficient of each influence factor to obtain a temperature predicted value of the ring main unit in the next period to be early-warned and monitored;
Screening out a section exceeding an early warning threshold value from the temperature predicted value, and carrying out temperature rise early warning on the section exceeding the early warning threshold value;
the expression of the history monitoring data with the maximum similarity is matched from the history database according to the real-time monitoring data, and is specifically as follows:
Wherein, Representing a data value at the ith moment in the real-time monitoring data corresponding to the influence factor a; /(I)A data value representing the ith moment in the history monitoring data corresponding to the influence factor a; Δt represents the duration of the monitoring period; /(I)Representing the real-time monitoring data average value corresponding to the residual factor b of the same monitoring period of the real-time monitoring data corresponding to the influence factor a; Representing the average value of the historical monitoring data corresponding to the residual factor b of the same monitoring period of the historical monitoring data corresponding to the influence factor a; k b denotes the difference threshold of the residual factor b; b all denotes all remaining factors in the influence factor set except for the influence factor a; d represents the set of influencing factors.
2. The multi-source data-based ring main unit temperature rise early warning method according to claim 1, wherein the influence factors comprise load current, external temperature, heat dissipation power and internal humidity.
3. The ring main unit temperature rise early warning method based on multi-source data according to claim 1, wherein the weight coefficient distribution process of the influence factors is specifically as follows:
Randomly distributing weight coefficients to each influence factor in the influence factor set;
carrying out weight calculation on the temperature value of the historical temperature data corresponding to each influence factor at the same moment according to the weight coefficient to obtain a real-time temperature predicted value at the corresponding moment;
and carrying out error analysis on the real-time temperature data and the real-time temperature predicted values at all moments, and selecting the weight coefficient which is allocated corresponding to the minimum error as the weight coefficient which is finally allocated.
4. The ring main unit temperature rise early warning method based on multi-source data according to claim 3, wherein the weight coefficient distribution formula of the influence factor is specifically as follows:
Wherein, Representing a real-time temperature pre-estimated value at the i-th moment in the monitoring period; delta n represents the weight coefficient of the nth influence factor; /(I)The temperature value at the ith moment in the historical temperature data corresponding to the nth influence factor is represented; n represents the number of influence factors in the set of influence factors; Δt represents the duration of the monitoring period; /(I)And the real-time temperature value at the i-th moment in the real-time temperature data is represented.
5. The ring main unit temperature rise early warning method based on multi-source data according to claim 1, wherein the weight coefficient distribution process of the influence factors is specifically as follows:
Taking a real-time temperature value at a certain moment in the real-time temperature data as a weight value of a temperature value at the same corresponding moment in each historical temperature data, and distributing the weight coefficient at the corresponding moment for each influence factor;
Analyzing errors of weight coefficients distributed at all moments in a monitoring period of a single influence factor;
And solving to obtain the optimal weight coefficient mean value of each influence factor as a final weight coefficient by taking the minimum sum of errors of the weight coefficients allocated to all the influence factors as an optimization target.
6. The ring main unit temperature rise early warning method based on multi-source data according to claim 5, wherein the weight coefficient distribution formula of the influence factor is specifically as follows:
Wherein, A real-time temperature value at the i-th moment in the real-time temperature data is represented; delta n,i represents the weight coefficient of the nth influence factor at the ith moment; n represents the number of influence factors in the set of influence factors; /(I)The temperature value at the ith moment in the historical temperature data corresponding to the nth influence factor is represented; Δt represents the duration of the monitoring period; /(I)And the weight coefficient mean value of the nth influence factor at all moments is represented.
7. Ring main unit temperature rise early warning system based on multisource data, characterized by including:
the factor determining module is used for determining an influence factor set influencing the temperature change of the ring main unit and establishing a history database according to the data of history monitoring of each influence factor in the influence factor set;
The data acquisition module is used for acquiring real-time temperature data of the ring main unit in the monitoring period and real-time monitoring data corresponding to each influence factor in the influence factor set;
The data matching module is used for matching the historical monitoring data with the maximum similarity from the historical database according to the real-time monitoring data, and extracting the historical temperature data of the ring main unit in the same monitoring period with the historical monitoring data and the temperature reference data of the ring main unit in the next monitoring period from the historical database;
the weight distribution module is used for enabling the real-time temperature data to be equivalent to the weight value of the historical temperature data corresponding to each influence factor, and distributing weight coefficients for each influence factor;
The temperature prediction module is used for carrying out weight calculation according to the temperature reference data and the weight coefficient of each influence factor to obtain a temperature prediction value of the ring main unit in the next period to be early-warned and monitored;
the temperature rise early warning module is used for screening out a section exceeding the early warning threshold value from the temperature predicted value and carrying out temperature rise early warning on the section exceeding the early warning threshold value;
the expression of the history monitoring data with the maximum similarity is matched from the history database according to the real-time monitoring data, and is specifically as follows:
Wherein, Representing a data value at the ith moment in the real-time monitoring data corresponding to the influence factor a; /(I)A data value representing the ith moment in the history monitoring data corresponding to the influence factor a; Δt represents the duration of the monitoring period; /(I)Representing the real-time monitoring data average value corresponding to the residual factor b of the same monitoring period of the real-time monitoring data corresponding to the influence factor a; Representing the average value of the historical monitoring data corresponding to the residual factor b of the same monitoring period of the historical monitoring data corresponding to the influence factor a; k b denotes the difference threshold of the residual factor b; b all denotes all remaining factors in the influence factor set except for the influence factor a; d represents the set of influencing factors.
8. A computer terminal comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the multi-source data-based ring main unit temperature rise early warning method according to any one of claims 1-6 when executing the program.
9. A computer readable medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the multi-source data based ring main unit temperature rise pre-warning method of any one of claims 1-6.
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