CN115470976A - Cable temperature prediction method, cable temperature prediction device, computer equipment and storage medium - Google Patents
Cable temperature prediction method, cable temperature prediction device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN115470976A CN115470976A CN202211046736.3A CN202211046736A CN115470976A CN 115470976 A CN115470976 A CN 115470976A CN 202211046736 A CN202211046736 A CN 202211046736A CN 115470976 A CN115470976 A CN 115470976A
- Authority
- CN
- China
- Prior art keywords
- current
- carrying
- data
- sampling
- cable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000005070 sampling Methods 0.000 claims abstract description 326
- 230000008859 change Effects 0.000 claims abstract description 187
- 238000004590 computer program Methods 0.000 claims abstract description 40
- 238000010438 heat treatment Methods 0.000 claims description 29
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 8
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 239000004020 conductor Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K7/00—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/12—Simultaneous equations, e.g. systems of linear equations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Databases & Information Systems (AREA)
- Primary Health Care (AREA)
- Algebra (AREA)
- Water Supply & Treatment (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to a cable temperature prediction method, a cable temperature prediction device, a computer device, a storage medium and a computer program product. The method comprises the following steps: acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature; when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold value, acquiring historical current-carrying sampling data of the cable; and determining a predicted temperature based on historical current-carrying sampling data of the cable, the current node current-carrying capacity and the current temperature. According to the method, the current-carrying capacity is used as a parameter, the cable temperature is predicted based on the relation between the current-carrying capacity and the temperature change, and the accuracy of cable temperature prediction can be improved.
Description
Technical Field
The present application relates to the field of electrical technologies, and in particular, to a method and an apparatus for predicting a temperature of a cable, a computer device, a storage medium, and a computer program product.
Background
The power cable is one of important devices for transmitting electric energy in a power system, and is widely applied. The conductor temperature of the running cable needs to be judged in the running process of the cable, so that the fault point of the cable is positioned.
In the conventional technology, a temperature sensor is usually adopted to measure the temperature of the outer sheath or the buffer layer of the cable, and then the conductor temperature value is calculated in a theoretical calculation mode.
However, the heat transfer model is changed due to parameter selection, different cable laying states, environment changes and the like, so that deviation of thermal calculation occurs, and therefore the prediction accuracy of the temperature of the conductor of the running cable in the traditional technology is low.
Disclosure of Invention
In view of the above, it is necessary to provide a cable temperature prediction method, apparatus, computer device, computer readable storage medium and computer program product capable of mentioning the accuracy of temperature prediction in view of the above technical problems.
In a first aspect, the present application provides a method for cable temperature prediction. The method comprises the following steps:
acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature;
when the current carrying capacity of the current node is larger than a preset current carrying capacity threshold value, acquiring historical current carrying sampling data of the cable;
and determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature.
In one embodiment, the historical current carrying sample data comprises historical node current carrying capacity; determining a predicted temperature based on historical current carrying sampled data, current carrying sampled data, and a current temperature of the cable, comprising:
acquiring historical current-carrying sampling data corresponding to current-carrying capacities of a plurality of historical nodes larger than a preset current-carrying capacity threshold value based on historical current-carrying sampling data of the cable, and taking the historical current-carrying sampling data as candidate node current-carrying sampling data;
acquiring a preset number of historical current-carrying sampled data before the current moment, taking the historical current-carrying sampled data as a first sampled data set, and determining a first change slope of the current-carrying sampled data based on the first sampled data set;
acquiring a preset number of historical current-carrying sampled data before the sampling time of the current-carrying sampled data of each candidate node as a second sampled data set aiming at the current-carrying sampled data of each candidate node, and determining a second change slope of the current-carrying sampled data of the candidate nodes based on the second sampled data set;
determining a prediction parameter corresponding to the current-carrying sampling data based on the first change slope and a plurality of second change slopes;
a predicted temperature is determined based on the prediction parameter.
Determining a temperature error based on the predicted temperature and the actual temperature of the next node in the same direction as the time flow from the sampling time of the current ampacity sampling data;
and when the temperature error is larger than the error threshold value, performing data correction on the prediction parameter to obtain the corrected prediction parameter.
In one embodiment, the historical carrier flow sample data includes historical temperature; determining a prediction parameter corresponding to the current-carrying sampled data based on the first change slope and a plurality of second change slopes, including:
according to the sampling time of the current carrying capacity sampling data, sequentially and respectively calculating similarity errors of the first change slope and the plurality of second change slopes in a direction opposite to the time flow direction from the sampling time of the current carrying capacity sampling data, and determining a third change slope based on the similarity errors;
determining a first current-carrying capacity overload coefficient and a first average heating rate corresponding to a first change slope based on current-carrying sampling data; determining a second current-carrying capacity overload coefficient and a second average heating rate corresponding to a third change slope based on historical current-carrying sampling data;
and determining a prediction parameter of the current node based on the first ampacity overload coefficient, the first average heating rate, the second ampacity overload coefficient and the second average heating rate.
In one embodiment, according to the sampling time of the current-capacity sampling data, similarity errors between the first change slope and the plurality of second change slopes are respectively calculated in sequence from the sampling time of the current-capacity sampling data in a direction opposite to the time flow direction, and a third change slope is determined based on the similarity errors, including:
according to the sampling time of the current carrying capacity sampling data, acquiring the previous sampling time adjacent to the sampling time of the current carrying capacity sampling data as a candidate sampling time;
obtaining a second change slope corresponding to the candidate sampling time, calculating a similarity error between the first change slope and the second change slope, obtaining the previous sampling time of the candidate sampling time as a new candidate sampling time when the similarity error is greater than a similarity error threshold, and repeating the step until a second change slope with the similarity error between the first change slope and the second change slope being less than or equal to the similarity error threshold is obtained through calculation;
and taking a second change slope with the similarity error with the first change slope smaller than or equal to the similarity error threshold value as a third change slope.
In one embodiment, the method further includes:
when the predicted temperature is higher than the preset temperature threshold, the load of the cable is disconnected or transferred to the cable around the cable.
In a second aspect, the present application further provides a cable temperature prediction device. The device comprises:
the first data acquisition module is used for acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature;
the second data acquisition module is used for acquiring historical current-carrying sampling data of the cable when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold;
and the predicted temperature determining module is used for determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of any one of the above embodiments when executing the computer program.
In a fourth aspect, the present application further provides a computer device readable storage medium. The computer device readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, performs the steps of the method of any of the above embodiments.
According to the cable temperature prediction method, the cable temperature prediction device, the computer equipment, the storage medium and the computer program product, the current-carrying sampled data of the cable are firstly obtained, and the current-carrying sampled data comprise the current-carrying capacity and the current temperature of the current node; when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold value, acquiring historical current-carrying sampling data of the cable; and determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature. According to the method and the device, the current-carrying capacity is used as a parameter, the cable temperature is predicted based on the relation between the current-carrying capacity and the temperature change, and the accuracy of cable temperature prediction can be improved.
Drawings
FIG. 1 is a schematic flow diagram of a method for cable temperature prediction in one embodiment;
FIG. 2 is a schematic flow chart illustrating a cable temperature prediction method applied to a server side according to an embodiment;
FIG. 3 is a block diagram of a cable temperature prediction device according to an embodiment;
FIG. 4 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The cable temperature prediction method provided by the embodiment of the application can be applied to a server or a terminal single side, can also be applied to a system comprising the terminal and the server, and can be realized through interaction of the terminal and the server.
In the cable temperature prediction method provided by the embodiment of the application, a plurality of cables can be respectively communicated with a server through a network. The data storage system may store data that the server needs to process. The data storage system can be integrated on a server, and can also be placed on a cloud or other network server. The server provides an environment for cable temperature prediction. Firstly, a server acquires current-carrying sampling data of a cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature. Further, the server judges the relation between the current-carrying capacity of the current node and a preset current-carrying capacity threshold, and when the current-carrying capacity of the current node is larger than the preset current-carrying capacity threshold, historical current-carrying sampling data of the cable are obtained. Finally, the server may determine a predicted temperature of the cable based on historical current-carrying sampled data of the cable, a current node current-carrying capacity, and a current temperature. The server may be implemented by an independent server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 1, a cable temperature prediction method is provided, which is described by taking the method as an example applied to a service end side, and includes the following steps 102 to 106.
In this embodiment, the server may obtain, in real time, the current carrying capacity of the current node of the cable through the smart grid. Wherein the current carrying capacity of the cable has a periodic variation.
And step 104, acquiring historical current-carrying sampling data of the cable when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold value.
In this embodiment, when the current-carrying capacity of the current node is less than or equal to the preset current-carrying capacity threshold, the temperature of the cable tends to be stable or gradually decreased, and does not continue to increase, and no measure is taken.
In this embodiment, when the current-carrying capacity of the current node is greater than the preset current-carrying capacity threshold, it indicates that the current-carrying capacity of the cable is overloaded at the current time, and temperature prediction needs to be performed, so that a worker can take different processing measures based on the temperature change trend of the cable.
In this embodiment, the historical carrier stream sample data may include, but is not limited to: historical node ampacity, historical temperature, etc. And the current-carrying capacity of the plurality of historical nodes corresponds to the plurality of historical temperatures one by one.
And 106, determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature.
In the embodiment, the current-carrying capacity of the cable has a periodic change, and the current-carrying capacity of the same node has a correlation with temperature. Therefore, the server can predict the current-carrying capacity and the temperature of the next node according to the historical current-carrying sampling data, the current-carrying capacity of the current node and the current temperature.
In the embodiment, the server predicts the cable temperature by predicting the cable temperature variation trend in the same direction as the time flow from the sampling time of the current ampacity sampling data.
In the cable temperature prediction method, current-carrying sampling data of a cable are obtained firstly, wherein the current-carrying sampling data comprise current node current-carrying capacity and current temperature; when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold value, acquiring historical current-carrying sampling data of the cable; and determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature. According to the method and the device, the current-carrying capacity is used as a parameter, the cable temperature is predicted based on the relation between the current-carrying capacity and the temperature change, and the accuracy of cable temperature prediction can be improved.
In some embodiments, the historical current-carrying sample data comprises historical node current-carrying capacity; determining a predicted temperature based on historical current-carrying sampled data, current-carrying sampled data, and a current temperature of the cable may include: acquiring historical current-carrying sample data corresponding to the current-carrying capacities of a plurality of historical nodes larger than a preset current-carrying capacity threshold value based on the historical current-carrying sample data of the cable, and taking the historical current-carrying sample data as candidate node current-carrying sample data; acquiring a preset number of historical current-carrying sampled data before the current moment, taking the historical current-carrying sampled data as a first sampled data set, and determining a first change slope of the current-carrying sampled data based on the first sampled data set; for each candidate node current-carrying sampling data, acquiring a preset number of historical current-carrying sampling data before the sampling time of the candidate node current-carrying sampling data, taking the historical current-carrying sampling data as a second sampling data set, and determining a second change slope of the candidate node current-carrying sampling data based on the second sampling data set; determining a prediction parameter corresponding to the current-carrying sampling data based on the first change slope and a plurality of second change slopes; a predicted temperature is determined based on the prediction parameter.
In this embodiment, the server takes historical current-carrying sample data corresponding to a plurality of historical node current-carrying capacities greater than a preset current-carrying capacity threshold as candidate node current-carrying sample data based on the historical current-carrying sample data and the current node current-carrying capacity.
In this embodiment, the server obtains a preset number of historical carrier flow sampling data before the current time as a first sampling data set based on the first sampling data set. For example, when the preset number is 5, the time corresponding to the current-carrying sampling data is t 7 And the sampling time of the sampling data from the current ampacity is in the opposite direction to the time flow, and the following are carried out in sequence: t is t 6 ，t 5 ，t 4 ，t 3 ，t 2 ，t 1 ，t 0 At the time of waiting, the server obtains 5 historical current-carrying sampled data (namely t) in the direction opposite to the time flow direction from the sampling time of the current-carrying sampled data 6 ，t 5 ，t 4 ，t 3 ，t 2 Historical carrier stream sample data for a time instant) as a first set of data samples.
In the present embodiment, for each candidate node current-carrying sampling data, the candidate node is obtainedAnd taking a preset number of historical current-carrying sampled data before the sampling time of the current-carrying sampled data as a second sampled data set. For example, when the preset number is 6, the time corresponding to the current-carrying sampling data of the candidate node is t 6 And the sampling time of the current-carrying capacity sampling data from the candidate node is sequentially in the opposite direction of the time flow from the sampling time of the current-carrying capacity sampling data: t is t 5 ，t 4 ，t 3 ，t 2 ，t 1 ，t 0 ，t -1 ，t -2 At equal time, the server acquires 6 historical current-carrying sample data (namely t) in the direction opposite to the time flow direction from the sampling time of the candidate node current-carrying sample data 5 ，t 4 ，t 3 ，t 2 ，t 1 ，t 0 Historical carrier stream sample data for a time of day) as a second set of data samples.
In this embodiment, the number of elements in the first sample data set is the same as the number of elements in the second sample data set.
In this embodiment, the server calculates, based on the first sampling data set, a current-carrying capacity overload coefficient and average temperature rise data corresponding to the first sampling data set, and determines a first change slope of current-carrying sampling data. And the server calculates a current-carrying capacity overload coefficient and average temperature rise data corresponding to the second sampling data set based on the second sampling data set, and determines a second change slope of the historical current-carrying sampling data.
In this embodiment, the current temperature T and current capacity I of the cable can be obtained every Δ T time in the smart cable. Let us assume at t 6 Current carrying capacity of time cable I 6 Exceeds a set threshold value I m The server may obtain 5 historical carrier flow sample data from the sampling time of the current carrier flow sample data in a direction opposite to the time flow direction as the first data sample set. The coordinates of the sampling points of the 5 historical current-carrying sampling data can be (t) 1 ,I 1 )、(t 2 ,I 2 )、(t 3 ,I 3 )、(t 4 ,I 4 )、(t 5 ,I 5 ) The change slope (i.e., the first change slope) K of the current carrying capacity of the current carrying sampled data I1 Minimum of two as shown in equation (1)The multiplication is performed.
Wherein,
is the time average of the data in the first set of data samples,
i =1,2,3,4,5, which is the average of the ampacity of the data in the first set of data samples.
Similarly, the server may sample the sampling time (e.g., t) of the data from the current capacity of the candidate node 5 ) 5 historical current-carrying sample data are acquired as a second set of data samples in a direction opposite to the time flow. The coordinates of the sampling points of the 5 historical current-carrying sampling data can be (t) 0 ,I 0 )、(t 1 ,I 1 )、(t 2 ,I 2 )、(t 3 ,I 3 )、(t 4 ,I 4 ) The change slope (i.e. the second change slope) K of the current-carrying capacity of the candidate node current-carrying sampling data I2 The calculation is performed as the least squares method shown in equation (2).
Wherein,
is the time average of the data in the second set of data samples,
i =0,1,2,3,4, which is the average ampacity of the data in the second set of data samples.
In some embodiments, the historical carrier flow sample data includes historical temperatures; determining a prediction parameter corresponding to the current-carrying sampled data based on the first change slope and the plurality of second change slopes may include: according to the sampling time of the current carrying capacity sampling data, sequentially and respectively calculating similarity errors of the first change slope and the plurality of second change slopes in a direction opposite to the time flow direction from the sampling time of the current carrying capacity sampling data, and determining a third change slope based on the similarity errors; determining a first current-carrying capacity overload coefficient and a first average heating rate corresponding to a first change slope based on current-carrying sampling data; determining a second current-carrying capacity overload coefficient and a second average heating rate corresponding to a third change slope based on historical current-carrying sampling data; and determining the prediction parameters of the current node based on the first current-carrying capacity overload coefficient, the first average heating rate, the second current-carrying capacity overload coefficient and the second average heating rate.
In this embodiment, the server may obtain the sampling time of the current ampacity sampling data according to the sampling time of the ampacity sampling data, sequentially calculate the similarity errors between the first change slope and the plurality of second change slopes in a direction opposite to the time flow direction until finding a second change slope whose similarity error with the first change slope is less than or equal to the similarity error threshold, and use the second change slope as a third change slope.
In the present embodiment, the first change slope K I1 And a second change slope K I2 The similarity error θ of (a) is calculated as shown in equation (3):
in this embodiment, the current node (assuming that the sampling time of the current-carrying sampled data is t) 5 ) The ampacity overload coefficient of (2) is calculated as shown in equation (4):
wherein, I m To preset the carrying capacity threshold, I 5 Carrying capacity for current node, α 1 And the current carrying capacity overload coefficient of the current node.
In this embodiment, the current node (assuming that the sampling time of the current carrying sampled data is t) 5 ) The average temperature rise rate of (2) is calculated as shown in equation (5):
wherein, Δ t 1 Δ T is the sampling period (i.e., the time interval during which the server samples the ampacity data from the smart cable), the sampling period 1 For cables at Δ t 1 The average temperature-rise value in the time,
is the current node Δ t 1 Average rate of temperature rise over time, alpha 1 And k and c are undetermined constants for the current carrying capacity overload coefficient of the current node.
In the present embodiment, a candidate node (the candidate node is assumed to carry sampled data at a sampling time t 2 ) The ampacity overload coefficient of (2) is calculated as shown in equation (6):
wherein, I m To preset the carrying capacity threshold, I 2 Ampacity of the candidate node, α 2 And the current carrying capacity overload coefficient is the current carrying capacity overload coefficient of the candidate node.
In this embodiment, the current node (assuming that the sampling time of the current-carrying sampled data is t) 5 ) The average temperature rise rate of (2) is calculated as shown in equation (7):
wherein, Δ t 1 Δ T is the sampling period (i.e., the time interval during which the server samples the ampacity data from the smart cable) 2 KT2 is based onCandidate node at Δ t 1 Average rate of temperature rise over time, alpha 2 And k and c are undetermined constants for the current carrying capacity overload coefficient of the current node.
In this embodiment, the values of the undetermined coefficients k and c can be obtained based on the above equations (4), (5), (6), and (7) in a simultaneous system.
In the present embodiment, the temperature T is predicted 2 The calculation formula (c) is shown in formula (8):
T 2 ＝T 1 +K T1 Δt (8)
wherein, T 1 Is the current temperature, Δ t is the sampling period, K T1 Is the average temperature rise rate of the current node in delta T time, T 2 Is the predicted temperature of the next node in the same direction as the time flow for the sampling instant of the data sampled from the current ampacity.
In some embodiments, the method may further include: determining a temperature error based on the predicted temperature and the actual temperature of the next node in the same direction as the time flow from the sampling time of the current ampacity sampling data; and when the temperature error is larger than the error threshold, performing data correction on the prediction parameter to obtain a corrected prediction parameter.
In this embodiment, based on formula (8), the server may calculate the predicted temperature T of the next node corresponding to the current ampacity sampling data 2 . Further, the server obtains the actual temperature T of the next node in the same direction as the time flow from the sampling time of the current ampacity sampling data based on the sampling period of delta T 3 。
In this embodiment, the temperature error w is calculated as shown in equation (9):
in this embodiment, when the temperature error is greater than the error threshold (e.g., 5%, 7%, etc.), the undetermined constant k in the predicted parameter is subjected to data correction. The server corrects the prediction parameters as shown in formula (10):
wherein k' is the corrected k.
In this embodiment, when the server predicts the temperature of the second node, which is in the same direction as the time flow, at the sampling time of the current ampacity sampling data by using the corrected prediction parameter based on the current ampacity sampling data of the first node, which is in the same direction as the time flow, at the sampling time of the current ampacity sampling data.
In some embodiments, calculating, according to the sampling time of the current-capacity sampling data, similarity errors between the first change slope and the plurality of second change slopes in a direction opposite to the time flow from the sampling time of the current-capacity sampling data, and determining the third change slope based on the similarity errors may include: according to the sampling time of the current carrying capacity sampling data, acquiring the previous sampling time adjacent to the sampling time of the current carrying capacity sampling data as a candidate sampling time; obtaining a second change slope corresponding to the candidate sampling time, calculating a similarity error between the first change slope and the second change slope, obtaining the previous sampling time of the candidate sampling time as a new candidate sampling time when the similarity error is greater than a similarity error threshold, and repeating the step until a second change slope with the similarity error between the first change slope and the second change slope being less than or equal to the similarity error threshold is obtained through calculation; and taking a second change slope with the similarity error with the first change slope smaller than or equal to the similarity error threshold value as a third change slope.
In this embodiment, the server sequentially calculates, from the sampling time of the current ampacity sampling data in a direction opposite to the time flow direction, similarity errors between a first change slope and a second change slope corresponding to a plurality of candidate sampling times, and when a second change slope whose similarity error with the first change slope is smaller than or equal to a similarity error threshold is obtained through calculation, does not continue to calculate the similarity errors between the first change slope and the second change slope corresponding to the subsequent candidate sampling times. And a second change slope, in which a similarity error with the first change slope is less than or equal to a similarity error threshold, is taken as a third change slope.
In some embodiments, the method may further include: when the predicted temperature is higher than the preset temperature threshold, the load of the cable is disconnected or transferred to the cable around the cable. By predicting in real time, the load of the cable is cut off or transferred based on the predicted temperature, and the faults of the cable caused by overhigh load can be reduced.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a cable temperature prediction device for implementing the cable temperature prediction method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the cable temperature prediction device provided below can be referred to the limitations of the cable temperature prediction method in the foregoing, and details are not described here.
In one embodiment, as shown in fig. 3, there is provided a cable temperature prediction apparatus including: a first data acquisition module 302, a second data acquisition module 304, and a predicted temperature determination module 306, wherein:
the first data obtaining module 302 is configured to obtain current-carrying sampling data of the cable, where the current-carrying sampling data includes current node current-carrying capacity and current temperature.
And a second data obtaining module 304, configured to obtain historical current-carrying sampling data of the cable when the current-carrying capacity of the current node is greater than the preset current-carrying capacity threshold.
And a predicted temperature determination module 306, configured to determine a predicted temperature based on the historical current-carrying sampled data of the cable, the current-carrying capacity of the current node, and the current temperature.
In one embodiment, the historical current carrying sample data comprises historical node current carrying capacity; the predicted temperature determination module 306 may include:
and the data screening submodule is used for acquiring historical current-carrying sampling data corresponding to the current-carrying capacities of a plurality of historical nodes larger than a preset current-carrying capacity threshold value based on the historical current-carrying sampling data of the cable, and using the historical current-carrying sampling data as candidate node current-carrying sampling data.
And the first change slope determining submodule is used for acquiring a preset number of historical current-carrying sampled data before the current moment as a first sampled data set, and determining a first change slope of the current-carrying sampled data based on the first sampled data set.
And the second change slope determining submodule is used for acquiring a preset number of historical current-carrying sampled data before the sampling time of the candidate node current-carrying sampled data as a second sampled data set aiming at each candidate node current-carrying sampled data, and determining a second change slope of the candidate node current-carrying sampled data based on the second sampled data set.
And the prediction parameter determining submodule is used for determining the prediction parameter corresponding to the current carrying sampling data based on the first change slope and the plurality of second change slopes.
A predicted temperature determination sub-module to determine a predicted temperature based on the prediction parameter.
In one embodiment, the apparatus may further include:
and the temperature error determination module is used for determining the temperature error based on the predicted temperature and the actual temperature of the next node which is in the same direction with the time flow from the sampling time of the current ampacity sampling data.
And the parameter correction module is used for correcting data of the prediction parameters when the temperature error is greater than the error threshold value to obtain corrected prediction parameters.
In one embodiment, the historical carrier flow sample data includes historical temperature; the prediction parameter determination sub-module may include:
and the third change slope determining unit is used for sequentially and respectively calculating similarity errors of the first change slope and the plurality of second change slopes from the sampling time of the current carrying capacity sampling data in a direction opposite to the time flow direction according to the sampling time of the current carrying capacity sampling data, and determining the third change slope based on the similarity errors.
The average heating rate determining unit is used for determining a first current-carrying capacity overload coefficient and a first average heating rate corresponding to the first change slope based on the current-carrying sampling data; and determining a second current-carrying capacity overload coefficient and a second average heating rate corresponding to the third change slope based on historical current-carrying sampling data.
And the prediction parameter determining unit is used for determining the prediction parameters of the current node based on the first current-carrying capacity overload coefficient, the first average heating rate, the second current-carrying capacity overload coefficient and the second average heating rate.
In one embodiment, the third change slope determining unit may include:
and the candidate sampling moment determining subunit is used for acquiring the sampling moment of the current-carrying capacity sampling data, which is adjacent to the previous sampling moment, as the candidate sampling moment according to the sampling moment of the current-carrying capacity sampling data.
And the error screening subunit is used for acquiring a second change slope corresponding to the candidate sampling time, calculating a similarity error between the first change slope and the second change slope, acquiring the previous sampling time of the candidate sampling time as a new candidate sampling time when the similarity error is greater than a similarity error threshold, and repeating the step until a second change slope, the similarity error of which with the first change slope is less than or equal to the similarity error threshold, is calculated.
And the slope determining subunit is used for taking a second change slope with the similarity error of the first change slope smaller than or equal to the similarity error threshold value as a third change slope.
In one embodiment, the apparatus further includes:
and the load processing module is used for disconnecting the load of the cable or transferring the load of the cable to the cable around the cable when the predicted temperature is higher than the preset temperature threshold value.
The various modules in the cable temperature prediction device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as prediction parameters, current-carrying sampling data and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a cable temperature prediction method.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of: acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature; when the current carrying capacity of the current node is larger than a preset current carrying capacity threshold value, acquiring historical current carrying sampling data of the cable; and determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature.
In one embodiment, the historical current-carrying sampled data includes historical node current-carrying capacity, and the processor when executing the computer program further implements determining the predicted temperature based on the historical current-carrying sampled data of the cable, the current-carrying sampled data, and the current temperature may include: acquiring historical current-carrying sampling data corresponding to current-carrying capacities of a plurality of historical nodes larger than a preset current-carrying capacity threshold value based on historical current-carrying sampling data of the cable, and taking the historical current-carrying sampling data as candidate node current-carrying sampling data; acquiring a preset number of historical current-carrying sampled data before the current moment, taking the historical current-carrying sampled data as a first sampled data set, and determining a first change slope of the current-carrying sampled data based on the first sampled data set; for each candidate node current-carrying sampling data, acquiring a preset number of historical current-carrying sampling data before the sampling time of the candidate node current-carrying sampling data, taking the historical current-carrying sampling data as a second sampling data set, and determining a second change slope of the candidate node current-carrying sampling data based on the second sampling data set; determining a prediction parameter corresponding to the current-carrying sampling data based on the first change slope and a plurality of second change slopes; a predicted temperature is determined based on the prediction parameter.
In one embodiment, the processor when executing the computer program further performs the steps of: determining a temperature error based on the predicted temperature and the actual temperature of the next node in the same direction as the time flow from the sampling time of the current ampacity sampling data; and when the temperature error is larger than the error threshold, performing data correction on the prediction parameter to obtain a corrected prediction parameter.
In one embodiment, the historical current-carrying sample data includes a historical temperature, and the processor when executing the computer program further enables determining a prediction parameter corresponding to the current-carrying sample data based on the first change slope and a plurality of second change slopes, which may include: according to the sampling time of the current carrying capacity sampling data, sequentially and respectively calculating similarity errors of the first change slope and the plurality of second change slopes in a direction opposite to the time flow direction from the sampling time of the current carrying capacity sampling data, and determining a third change slope based on the similarity errors; determining a first current-carrying capacity overload coefficient and a first average heating rate corresponding to a first change slope based on current-carrying sampling data; determining a second current-carrying capacity overload coefficient and a second average heating rate corresponding to a third change slope based on historical current-carrying sampling data; and determining a prediction parameter of the current node based on the first ampacity overload coefficient, the first average heating rate, the second ampacity overload coefficient and the second average heating rate.
In one embodiment, the processor, when executing the computer program, further implements calculating similarity errors between the first change slope and the plurality of second change slopes in sequence and in a direction opposite to a time flow direction from the sampling time of the current ampacity sampling data according to the sampling time of the ampacity sampling data, and determining the third change slope based on the similarity errors, which may include: according to the sampling time of the current carrying capacity sampling data, acquiring the previous sampling time adjacent to the sampling time of the current carrying capacity sampling data as a candidate sampling time; obtaining a second change slope corresponding to the candidate sampling time, calculating a similarity error between the first change slope and the second change slope, obtaining the previous sampling time of the candidate sampling time as a new candidate sampling time when the similarity error is greater than a similarity error threshold, and repeating the step until a second change slope with the similarity error between the first change slope and the second change slope being less than or equal to the similarity error threshold is obtained through calculation; and taking a second change slope with the similarity error with the first change slope smaller than or equal to the similarity error threshold value as a third change slope.
In one embodiment, the processor when executing the computer program further performs the steps of: when the predicted temperature is higher than the preset temperature threshold, the load of the cable is disconnected or transferred to the cable around the cable.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature; when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold value, acquiring historical current-carrying sampling data of the cable; and determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature.
In one embodiment, the historical current-carrying sampled data includes historical node current-carrying capacity, and the computer program when executed by the processor further implements determining the predicted temperature based on the historical current-carrying sampled data of the cable, the current-carrying sampled data, and the current temperature may include: acquiring historical current-carrying sampling data corresponding to current-carrying capacities of a plurality of historical nodes larger than a preset current-carrying capacity threshold value based on historical current-carrying sampling data of the cable, and taking the historical current-carrying sampling data as candidate node current-carrying sampling data; acquiring a preset number of historical current-carrying sampled data before the current moment, taking the historical current-carrying sampled data as a first sampled data set, and determining a first change slope of the current-carrying sampled data based on the first sampled data set; for each candidate node current-carrying sampling data, acquiring a preset number of historical current-carrying sampling data before the sampling time of the candidate node current-carrying sampling data, taking the historical current-carrying sampling data as a second sampling data set, and determining a second change slope of the candidate node current-carrying sampling data based on the second sampling data set; determining a prediction parameter corresponding to the current-carrying sampling data based on the first change slope and a plurality of second change slopes; a predicted temperature is determined based on the prediction parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a temperature error based on the predicted temperature and the actual temperature of the next node in the same direction as the time flow from the sampling time of the current ampacity sampling data; and when the temperature error is larger than the error threshold value, performing data correction on the prediction parameter to obtain the corrected prediction parameter.
In one embodiment, the historical current-carrying sample data includes a historical temperature, and the computer program when executed by the processor further enables determining a prediction parameter corresponding to the current-carrying sample data based on the first slope of change and a plurality of second slopes of change, may include: according to the sampling time of the current carrying capacity sampling data, sequentially and respectively calculating similarity errors of the first change slope and the plurality of second change slopes in a direction opposite to the time flow direction from the sampling time of the current carrying capacity sampling data, and determining a third change slope based on the similarity errors; determining a first current-carrying capacity overload coefficient and a first average heating rate corresponding to a first change slope based on current-carrying sampling data; determining a second current-carrying capacity overload coefficient and a second average heating rate corresponding to a third change slope based on historical current-carrying sampling data; and determining a prediction parameter of the current node based on the first ampacity overload coefficient, the first average heating rate, the second ampacity overload coefficient and the second average heating rate.
In one embodiment, the computer program when executed by the processor further enables calculating similarity errors between the first change slope and the plurality of second change slopes respectively in sequence from the sampling time of the current ampacity sampling data in a direction opposite to the time flow direction according to the sampling time of the ampacity sampling data, and determining the third change slope based on the similarity errors, which may include: according to the sampling time of the current carrying capacity sampling data, acquiring the previous sampling time adjacent to the sampling time of the current carrying capacity sampling data as a candidate sampling time; acquiring a second change slope corresponding to the candidate sampling time, calculating a similarity error between the first change slope and the second change slope, acquiring the previous sampling time of the candidate sampling time as a new candidate sampling time when the similarity error is greater than a similarity error threshold, and repeating the steps until a second change slope with the similarity error of the first change slope less than or equal to the similarity error threshold is calculated; and taking a second change slope with the similarity error with the first change slope smaller than or equal to the similarity error threshold value as a third change slope.
In one embodiment, the computer program when executed by the processor further performs the steps of: when the predicted temperature is higher than the preset temperature threshold, the load of the cable is disconnected or transferred to the cable around the cable.
In one embodiment, a computer program product is provided, comprising a computer program which when executed by a processor performs the steps of: acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature; when the current carrying capacity of the current node is larger than a preset current carrying capacity threshold value, acquiring historical current carrying sampling data of the cable; and determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current-carrying capacity of the current node and the current temperature.
In one embodiment, the historical current-carrying sampled data includes historical node current-carrying capacity, and the computer program when executed by the processor further implements determining the predicted temperature based on the historical current-carrying sampled data of the cable, the current-carrying sampled data, and the current temperature may include: acquiring historical current-carrying sampling data corresponding to current-carrying capacities of a plurality of historical nodes larger than a preset current-carrying capacity threshold value based on historical current-carrying sampling data of the cable, and taking the historical current-carrying sampling data as candidate node current-carrying sampling data; acquiring a preset number of historical current-carrying sampled data before the current moment, taking the historical current-carrying sampled data as a first sampled data set, and determining a first change slope of the current-carrying sampled data based on the first sampled data set; for each candidate node current-carrying sampling data, acquiring a preset number of historical current-carrying sampling data before the sampling time of the candidate node current-carrying sampling data, taking the historical current-carrying sampling data as a second sampling data set, and determining a second change slope of the candidate node current-carrying sampling data based on the second sampling data set; determining a prediction parameter corresponding to the current-carrying sampling data based on the first change slope and a plurality of second change slopes; a predicted temperature is determined based on the prediction parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a temperature error based on the predicted temperature and an actual temperature of a next node in the same direction as the time flow from the sampling time of the current carrying capacity sampling data; and when the temperature error is larger than the error threshold, performing data correction on the prediction parameter to obtain a corrected prediction parameter.
In one embodiment, the historical current-carrying sample data includes a historical temperature, and the computer program when executed by the processor further enables determining a prediction parameter corresponding to the current-carrying sample data based on the first slope of change and a plurality of second slopes of change, including: according to the sampling time of the current carrying capacity sampling data, sequentially and respectively calculating similarity errors of the first change slope and the plurality of second change slopes in a direction opposite to the time flow direction from the sampling time of the current carrying capacity sampling data, and determining a third change slope based on the similarity errors; determining a first current-carrying capacity overload coefficient and a first average heating rate corresponding to a first change slope based on current-carrying sampling data; determining a second current-carrying capacity overload coefficient and a second average heating rate corresponding to a third change slope based on historical current-carrying sampling data; and determining a prediction parameter of the current node based on the first ampacity overload coefficient, the first average heating rate, the second ampacity overload coefficient and the second average heating rate.
In one embodiment, the computer program when executed by the processor further enables calculating similarity errors between the first change slope and the plurality of second change slopes respectively in sequence from the sampling time of the current ampacity sampling data in a direction opposite to the time flow direction according to the sampling time of the ampacity sampling data, and determining the third change slope based on the similarity errors, which may include: according to the sampling time of the current carrying capacity sampling data, acquiring the previous sampling time adjacent to the sampling time of the current carrying capacity sampling data as a candidate sampling time; obtaining a second change slope corresponding to the candidate sampling time, calculating a similarity error between the first change slope and the second change slope, obtaining the previous sampling time of the candidate sampling time as a new candidate sampling time when the similarity error is greater than a similarity error threshold, and repeating the step until a second change slope with the similarity error between the first change slope and the second change slope being less than or equal to the similarity error threshold is obtained through calculation; and taking a second change slope with the similarity error with the first change slope smaller than or equal to the similarity error threshold value as a third change slope.
In one embodiment, the computer program when executed by the processor further performs the steps of: when the predicted temperature is higher than the preset temperature threshold, the load of the cable is disconnected or transferred to the cable around the cable.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the various embodiments provided herein may be, without limitation, general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, or the like.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method of cable temperature prediction, the method comprising:
acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature;
when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold value, acquiring historical current-carrying sampling data of the cable;
and determining a predicted temperature based on historical current-carrying sampling data of the cable, the current node current-carrying capacity and the current temperature.
2. The method of claim 1, wherein the historical current carrying sample data comprises historical node current carrying capacity; determining a predicted temperature based on the historical current-carrying sampled data of the cable, the current-carrying sampled data, and the current temperature, including:
acquiring historical current-carrying sample data corresponding to the current-carrying capacities of a plurality of historical nodes larger than the preset current-carrying capacity threshold value based on the historical current-carrying sample data of the cable, and taking the historical current-carrying sample data as candidate node current-carrying sample data;
acquiring a preset number of historical current-carrying sampled data before the current moment to serve as a first sampled data set, and determining a first change slope of the current-carrying sampled data based on the first sampled data set;
for each candidate node current-carrying sampling data, acquiring a preset number of historical current-carrying sampling data before the sampling time of the candidate node current-carrying sampling data, taking the historical current-carrying sampling data as a second sampling data set, and determining a second change slope of the candidate node current-carrying sampling data based on the second sampling data set;
determining a prediction parameter corresponding to current carrying sampling data based on the first change slope and a plurality of second change slopes;
a predicted temperature is determined based on the prediction parameter.
3. The method of claim 2, further comprising:
determining a temperature error based on the predicted temperature and an actual temperature of a next node in the same direction as the time flow from the sampling time of the current ampacity sampling data;
and when the temperature error is larger than the error threshold value, performing data correction on the prediction parameter to obtain a corrected prediction parameter.
4. The method of claim 2, wherein the historical carrier flow sample data comprises historical temperature; the determining, based on the first change slope and a plurality of second change slopes, a prediction parameter corresponding to current-carrying sampled data includes:
according to the sampling time of the current carrying capacity sampling data, sequentially and respectively calculating similarity errors of the first change slope and the plurality of second change slopes in a direction opposite to the time flow direction from the sampling time of the current carrying capacity sampling data, and determining a third change slope based on the similarity errors;
determining a first current-carrying capacity overload coefficient and a first average heating rate corresponding to the first change slope based on the current-carrying sampling data; determining a second current-carrying capacity overload coefficient and a second average heating rate corresponding to the third change slope based on the historical current-carrying sampling data;
and determining a prediction parameter of the current node based on the first ampacity overload coefficient, the first average heating rate, the second ampacity overload coefficient and the second average heating rate.
5. The method according to claim 4, wherein the calculating similarity errors between the first change slope and the plurality of second change slopes respectively and sequentially from the sampling time of the current ampacity sampling data in a direction opposite to a time flow direction according to the sampling time of the ampacity sampling data, and determining a third change slope based on the similarity errors comprises:
according to the sampling time of the current carrying capacity sampling data, acquiring the previous sampling time adjacent to the sampling time of the current carrying capacity sampling data as a candidate sampling time;
obtaining a second change slope corresponding to the candidate sampling time, calculating a similarity error between the first change slope and the second change slope, obtaining the previous sampling time of the candidate sampling time as a new candidate sampling time when the similarity error is greater than the similarity error threshold, and repeating the step until a second change slope with the similarity error between the first change slope and the second change slope being less than or equal to the similarity error threshold is obtained through calculation;
and taking the second change slope of which the similarity error with the first change slope is less than or equal to a similarity error threshold value as a third change slope.
6. The method of claim 1, further comprising:
disconnecting the load of the cable or transferring the load of the cable to the cable surrounding the cable when the predicted temperature is above a preset temperature threshold.
7. A cable temperature prediction apparatus, the apparatus comprising:
the first data acquisition module is used for acquiring current-carrying sampling data of the cable, wherein the current-carrying sampling data comprises current node current-carrying capacity and current temperature;
the second data acquisition module is used for acquiring historical current-carrying sampling data of the cable when the current-carrying capacity of the current node is larger than a preset current-carrying capacity threshold;
and the predicted temperature determining module is used for determining the predicted temperature based on the historical current-carrying sampling data of the cable, the current node current-carrying capacity and the current temperature.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211046736.3A CN115470976A (en) | 2022-08-30 | 2022-08-30 | Cable temperature prediction method, cable temperature prediction device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211046736.3A CN115470976A (en) | 2022-08-30 | 2022-08-30 | Cable temperature prediction method, cable temperature prediction device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115470976A true CN115470976A (en) | 2022-12-13 |
Family
ID=84368876
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211046736.3A Pending CN115470976A (en) | 2022-08-30 | 2022-08-30 | Cable temperature prediction method, cable temperature prediction device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115470976A (en) |
-
2022
- 2022-08-30 CN CN202211046736.3A patent/CN115470976A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021204010A1 (en) | Time series anomaly detection method and apparatus, and computer device and storage medium | |
| CN112131274B (en) | Method, device, equipment and readable storage medium for detecting abnormal points of time sequence | |
| WO2020237689A1 (en) | Network structure search method and apparatus, computer storage medium, and computer program product | |
| CN112818535B (en) | Method and device for establishing electrothermal simulation model and obtaining electrothermal simulation value | |
| US11262407B2 (en) | Method and apparatus with battery state estimation | |
| JP7357659B2 (en) | Data anomaly determination device and internal state prediction system | |
| CN115470976A (en) | Cable temperature prediction method, cable temperature prediction device, computer equipment and storage medium | |
| CN110850296A (en) | Method and device for evaluating health degree of battery | |
| CN110544031B (en) | Method and device for predicting reliability of electric energy meter, computer equipment and storage medium | |
| CN116577552A (en) | Method and device for diagnosing electricity larceny of intelligent measuring switch | |
| CN116306030A (en) | New energy prediction dynamic scene generation method considering prediction error and fluctuation distribution | |
| CN116187510A (en) | Ammeter box fault prediction method, device, computer equipment and storage medium | |
| Fedorchuk et al. | The method of decoding signals of temperature sensors of communication network equipment based on the use of nonlinear Volterra integral models | |
| CN111784535A (en) | Wind energy direct interval prediction method and device based on feedback neural network | |
| CN112731150A (en) | Voltage sag state estimation method and device, computer equipment and storage medium | |
| JP6757937B2 (en) | Estimator and estimation method | |
| JP2019087978A (en) | Quality estimation device and quality estimation method | |
| CN116579489B (en) | Regional power spot-discharge clearing method based on direct-current frequency limiting and adjusting capability | |
| WO2024212762A1 (en) | Capacity determination method and device for distributed system, device, and medium | |
| CN116819223A (en) | Cable running state monitoring method, device, computer equipment, medium and product | |
| CN109390038B (en) | Method and system for detecting pathogenicity of mutation by combining population frequency with mutation prediction | |
| Alneamy et al. | Proposed binding between genetic algorithm and grey wolf optimizer to estimate the parameters of software reliability growth models | |
| CN116819224A (en) | Cable running state monitoring method, device, computer equipment, medium and product | |
| CN117725777A (en) | Cable temperature field determining method, device, computer equipment and storage medium | |
| CN114637606A (en) | Task unloading method, device and equipment based on distribution substation area distribution transformation gateway |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |