A temperature warning method and device of a bus duct, a terminal and a storage medium

Abstract

The application provides a bus duct temperature warning method and device, a terminal and a storage medium. The method comprises the following steps: a white box model for representing the corresponding relationship between the bus current and the bus temperature rise is constructed according to the device mechanism modeling method and the thermodynamic principle; an initial gray box model between the bus current and the bus temperature rise is constructed based on the white box model, the initial gray box model is trained based on a machine learning algorithm to obtain a trained target gray box model; the target gray box model is used to predict a dynamic temperature rise threshold corresponding to a current current value and a predicted temperature value; the current current value is a current value corresponding to an actual temperature value of the bus duct currently monitored, and a comparison result is obtained by comparing the difference between the actual temperature value and the predicted temperature value with the dynamic temperature rise threshold; and it is determined whether to trigger a warning based on the comparison result. The application generates a dynamic threshold through model prediction, and solves the problem that the abnormal temperature rise of the bus cannot be accurately fed back due to manual configuration of a static threshold.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, and particularly relates to a bus duct temperature warning method and device, a terminal and a storage medium. BACKGROUND

[0002] With more and more data centers being built, the safety of the power system needs more reliable protection. The bus is responsible for the connection of electrical equipment such as transformers at all levels and corresponding power distribution devices in the power system. The bus plays a role in collecting, distributing and transmitting electric energy. The plug-in bus, as a new type of power distribution conductor, fully demonstrates its superiority in large current transmission and distribution compared with traditional cables. The bus has been quite common in the application of power distribution systems in high-rise buildings and large and medium-sized data centers.

[0003] At present, in order to ensure the safe operation of the bus, the temperature rise needs to be controlled within the allowed range. The bus is in a long-term live state during operation, which is not conducive to routine inspection and maintenance by conventional means. Daily maintenance usually measures the temperature rise of the shell and key connection parts. Moreover, most buses are operated in a relatively humid and dark environment for a long time, and the internal insulation materials are gradually aging and oxidizing. The bolts at some joints may become loose and other problems, which can easily lead to changes in the impedance of the bus connection and temperature rise. Once the temperature of the running bus joint is abnormally overheated, it may cause a fire or a major safety and quality accident, causing huge and irreparable personal injury and economic property loss to the enterprise.

[0004] However, the current method is to monitor the temperature of the bus in real time and configure a static threshold based on experience. When the temperature of the bus exceeds the static threshold, an alarm is generated. However, the static threshold cannot separate the peak and off-peak periods of the business, resulting in an inability to accurately set the static threshold of the alarm. If the static threshold is set too high, the actual abnormal scenario during the off-peak period cannot be notified to the responsible person. If the static threshold is set too low, frequent false alarms will occur during the peak period, affecting the judgment of the health of the business. That is, the accurate setting of the threshold will have an actual impact on the business. In a long period of time when the business just starts to appear abnormally, no alarm event can be generated because the threshold is not reached. Since the temperature of the bus has a certain correlation with the size of the bus current, the static threshold is usually set high, and the alarm generated by the system due to abnormal overheating cannot be accurately judged.

[0005] In summary, the existing bus duct temperature warning method has the technical problem that manual configuration of the static threshold cannot accurately feedback the abnormal temperature rise of the bus. Therefore, how to provide a solution to the above technical problems is a problem that those skilled in the art need to solve at present. SUMMARY

[0006] The technical problem solved by the present application is to provide a bus duct temperature warning method, device, terminal and storage medium to solve the problem that the abnormal temperature rise of the bus cannot be accurately fed back due to manual configuration of static threshold in the prior art.

[0007] The technical solution adopted by the present application to solve the technical problem is as follows:

[0008] A bus duct temperature warning method, wherein the method comprises:

[0009] A white box model for representing the corresponding relationship between the bus current and the bus temperature rise is constructed according to the device mechanism modeling method and the thermodynamic principle;

[0010] An initial gray box model between the bus current and the bus temperature rise is constructed based on the white box model, and the initial gray box model is trained based on a machine learning algorithm to obtain a trained target gray box model;

[0011] The target gray box model is used to predict a dynamic temperature rise threshold and a predicted temperature value corresponding to the current current value; wherein the current current value is the current value corresponding to the actual temperature value of the bus duct currently monitored;

[0012] The difference between the actual temperature value and the predicted temperature value is compared with the dynamic temperature rise threshold to obtain a corresponding comparison result, and it is determined whether to trigger a warning based on the comparison result.

[0013] In an implementation manner, the white box model is:

[0014] Delta T equals I 2 Rt / K t ;

[0015] Wherein, Delta T represents the bus temperature rise, I represents the bus current, R represents the resistance of the conductor, K t represents the total heat dissipation coefficient, and t represents time.

[0016] In an implementation manner, the initial gray box model between the bus current and the bus temperature rise is constructed based on the white box model, comprising:

[0017] The device physical properties of the white box model are determined, and the device physical properties of the white box model are mathematically abstracted to determine the gray box model target, input feature and basic model of the bus duct;

[0018] The initial gray box model between the bus current and the bus temperature rise is constructed based on the gray box model target, the input feature and the basic model;

[0019] The grey-box model target is the busbar temperature rise, the input feature is the busbar current, and the basic model is a one-dimensional quadratic function containing a random quantity.

[0020] In an implementation manner, the initial grey-box model is:

[0021] Delta T=a1x+a2x 2 +epsilon;

[0022] Delta T represents the busbar temperature rise, a1 represents a first coefficient, a2 represents a second coefficient, x represents the busbar current, and epsilon represents the random quantity.

[0023] In an implementation manner, the initial grey-box model is trained based on the machine learning algorithm to obtain a trained target grey-box model, including:

[0024] The initial grey-box model is trained based on a linear regression algorithm in the machine learning algorithm and pre-collected training data to obtain the trained target grey-box model.

[0025] In an implementation manner, after the initial grey-box model is trained based on the machine learning algorithm to obtain the trained target grey-box model, the method further includes:

[0026] The target grey-box model is verified based on a pre-collected verification data set to obtain a corresponding verification result.

[0027] Based on the verification result, it is determined whether to continue the step of training the initial grey-box model based on the machine learning algorithm to obtain the trained target grey-box model.

[0028] In an implementation manner, after the initial grey-box model is trained based on the machine learning algorithm to obtain the trained target grey-box model, the method further includes:

[0029] The target grey-box model is subjected to reinforcement learning to obtain an enhanced target grey-box model.

[0030] The target grey-box model is subjected to reinforcement learning to obtain an enhanced target grey-box model.

[0031] The dynamic temperature rise threshold value and the predicted temperature value corresponding to the current current value are predicted based on the enhanced target grey-box model.

[0032] The application further discloses a temperature warning device of a busbar slot.

[0033] The first model construction module is configured to construct a white-box model for representing the corresponding relationship between the busbar current and the busbar temperature rise according to the device mechanism modeling manner and the thermodynamic principle.

[0034] The second model construction module is configured to construct an initial gray-box model between the busbar current and the busbar temperature rise based on the white-box model.

[0035] The model training module is configured to train the initial gray-box model based on a machine learning algorithm to obtain a trained target gray-box model.

[0036] The prediction module is configured to predict a dynamic temperature rise threshold corresponding to a current current value and a predicted temperature value by using the target gray-box model, wherein the current current value is a current value corresponding to an actual temperature value of the busbar slot currently monitored.

[0037] The alarm module is configured to compare a difference between the actual temperature value and the predicted temperature value with the dynamic temperature rise threshold to obtain a corresponding comparison result, and determine whether to trigger an alarm based on the comparison result.

[0038] The application further discloses a terminal, which comprises a memory, a processor, and a busbar slot temperature alarm program stored in the memory and executable on the processor, and the busbar slot temperature alarm program implements the steps of the busbar slot temperature alarm method when executed by the processor.

[0039] The application further discloses a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the busbar slot temperature alarm method.

[0040] This invention provides a temperature alarm method, device, terminal, and storage medium for busbar trunking. The temperature alarm method for busbar trunking includes: constructing a white-box model to characterize the correspondence between busbar current and busbar temperature rise based on equipment mechanism modeling and thermodynamic principles; constructing an initial gray-box model of the relationship between busbar current and busbar temperature rise based on the white-box model, and training the initial gray-box model using a machine learning algorithm to obtain a trained target gray-box model; using the target gray-box model to predict a dynamic temperature rise threshold and a predicted temperature value corresponding to the current current value; wherein the current current value is the current value corresponding to the actual temperature value of the currently monitored busbar trunking; comparing the difference between the actual temperature value and the predicted temperature value with the dynamic temperature rise threshold to obtain a corresponding comparison result, and determining whether to trigger an alarm based on the comparison result. This invention models the busbar trunking system, constructing a white-box model of the relationship between busbar temperature and current based on equipment mechanism modeling and thermodynamic principles. A corresponding gray-box model is then built based on this white-box model, and trained using machine learning algorithms to fit the actual model, thus improving the model's adaptability. In other words, this method combines equipment mechanism and big data machine learning to model the busbar trunking system, resulting in wider applicability and better alarm performance. Finally, a dynamic threshold is generated through model prediction. This dynamic threshold automatically adapts to abnormal temperature conditions under different currents, solving the problem of inaccurate feedback of abnormal busbar temperature rise due to manually configured static thresholds. Therefore, the technical solution of this application ensures that the alarm strategy follows thermodynamic principles, is independent of pure data dependence, and avoids inverse logic, resulting in higher reliability and better alarm performance. Attached Figure Description

[0041] Figure 1 This is a flowchart of a preferred embodiment of the busbar temperature alarm method in this invention;

[0042] Figure 2 This is a schematic diagram showing the relationship between the temperature rise and current load of the busbar trunking in this invention.

[0043] Figure 3 This is a schematic diagram of the relationship between temperature and current in a specific busbar trunking in this invention;

[0044] Figure 4 This is a functional principle block diagram of a preferred embodiment of the busbar temperature alarm device in this invention;

[0045] Figure 5 This is a functional principle block diagram of a preferred embodiment of the terminal in this invention. Detailed Implementation

[0046] For purposes of promoting an understanding of the principles of the application, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will, nevertheless, be understood that no limitation of the scope of the application is thereby intended, such alterations and further modifications in the illustrated device, and such further applications of the principles of the application as illustrated therein being contemplated as would normally occur to one skilled in the art to which the application relates.

[0047] Referring to Figure 1 , Figure 1 is a flowchart of the temperature warning method of the bus duct in the present application. As shown in Figure 1 , the temperature warning method of the bus duct in the embodiment of the present application comprises:

[0048] Step S100, constructing a white box model for representing the corresponding relationship between the bus current and the bus temperature rise according to the device mechanism modeling method and the thermodynamic principle.

[0049] In the embodiment of the present application, the physical characteristics of the bus duct device are analyzed according to the device mechanism modeling method and the thermodynamic principle, and then a white box model for representing the corresponding relationship between the bus current and the bus temperature rise is constructed, that is, a thermodynamic white box power model between the current and the temperature rise of the bus duct device is constructed.

[0050] Specifically, the white box model can be:

[0051] ΔT=I 2 Rt / K t ;

[0052] Wherein, ΔT represents the bus temperature rise, I represents the bus current, R represents the resistance of the conductor, K t represents the total heat dissipation coefficient, and t represents time.

[0053] It should be pointed out that the bus temperature rise is the temperature rise caused by the heat generated by the current passing through the conductor. In this process, the existence of the conductor resistance is involved. The resistance will cause energy loss when the current passes through the conductor, and the energy loss will be released in the form of heat, thereby causing the temperature rise of the conductor. The bus temperature rise calculation formula in the embodiment of the present application is only an approximate value, which is actually affected by other factors such as the environment temperature, the cooling method and the wiring method.

[0054] It can be understood that the white box model can represent the quadratic relationship between the bus temperature rise and the bus current, wherein the load curve of the temperature rise and the current is shown in Figure 2 .

[0055] As shown in Figure 1 , the temperature warning method of the bus duct in the embodiment further comprises:

[0056] Step S200, constructing an initial gray-box model between the busbar current and the busbar temperature rise based on the white-box model, and training the initial gray-box model based on a machine learning algorithm to obtain a trained target gray-box model.

[0057] In an embodiment of the present application, the step S200 specifically comprises:

[0058] Step S210, determining the device physical characteristics of the white-box model, and performing mathematical abstraction processing on the device physical characteristics of the white-box model to determine the gray-box model target, input feature and basic model of the busbar trunking.

[0059] Step S220, constructing an initial gray-box model between the busbar current and the busbar temperature rise based on the gray-box model target, the input feature and the basic model; wherein the gray-box model target is the busbar temperature rise, the input feature is the busbar current, and the basic model is a one-dimensional quadratic function containing a random quantity.

[0060] It can be understood that the gray-box model between the busbar temperature rise and the busbar current is mainly constructed by performing mathematical abstraction processing on the device physical characteristics of the white-box model, so that the gray-box model target of the busbar trunking device can be determined, that is, the model target, the input feature and the optional basic model, that is, the basic mathematical function, and the loss function used for subsequent training of the initial gray-box model can also be determined, as shown in Table 1:

[0061] Table 1

[0062] Model objective Bus temperature rise ΔΤ Input features Bus current Basic model that can be used Monomial quadratic function to be corrected, containing random quantities Loss function Squared difference between true and predicted values (i.e. L2 loss)

[0063] Specifically, the initial gray-box model is:

[0064] ΔT = a1x + a2x 2 + ε;

[0065] Wherein, ΔT represents the busbar temperature rise, a1 represents the first coefficient, a2 represents the second coefficient, x represents the busbar current, and ε represents the random quantity.

[0066] The embodiments of the present application construct the corresponding gray-box model based on the white-box model, and train the gray-box model based on the machine learning algorithm, so as to fit the real model, improve the adaptability of the model, that is, realize the modeling of the busbar trunking device by combining the device mechanism and the big data machine learning, so that the alarm applicability is wider, the effect is better, and the calculation efficiency is faster when the alarm is generated. It should be noted that this modeling method combining the device mechanism and the big data machine learning is not only suitable for the busbar trunking device of the present application, but also can be used for modeling the switch cabinet circuit breaker.

[0067] Specifically, the initial gray-box model is trained based on a machine learning algorithm to obtain a trained target gray-box model, which can specifically include: based on a linear regression algorithm in the machine learning algorithm, and using pre-collected training data to train the initial gray-box model to obtain the trained target gray-box model.

[0068] For example, relevant historical data are collected, and then the initial gray-box model is trained using a gradient descent method and the historical data, wherein a loss function used by the gradient descent method describes the error between a predicted value and an actual value, so the optimal solution of the parameter a corresponds to the minimum value of the loss function.

[0069] The known loss function is:

[0070] cost = (Preal - Ppred) 2 ;

[0071] The predicted value is:

[0072] Ppred = a1x 1 +a2x 2 + random quantity

[0073] Therefore, the loss function can be:

[0074] cost = (Preal - (a1x 1 +a2x 2 + random quantity)) 2 ;

[0075] Therefore, the gradient descent method can be used to solve the a value corresponding to the minimum value of the cost, thereby completing the training of the initial gray-box model between the busbar temperature rise and the busbar current to obtain the target gray-box model.

[0076] It can be understood that the embodiments of the present application repeatedly iterate the machine learning model through the loss function, and the gradient descent method is used in the iteration process to find the optimal solution, wherein linear regression is a common type of machine learning.

[0077] Through the above model construction method, the target gray-box model corresponding to all busbars in the current scenario can be constructed.

[0078] After the initial gray box model is trained based on the machine learning algorithm to obtain the trained target gray box model, the method can further include: verifying the target gray box model by using a pre-collected verification data set to obtain a corresponding verification result; and determining whether to continue the step of training the initial gray box model based on the machine learning algorithm to obtain the trained target gray box model based on the verification result. It can be understood that the relationship between the busbar temperature rise and the busbar current is modeled, then the machine learning algorithm is selected to train the model, and the trained model is further verified to ensure the accuracy and reliability of the model.

[0079] As shown in Figure 1 , the busbar slot temperature warning method of the embodiment further includes:

[0080] In step S300, the target gray box model is used to predict a dynamic temperature rise threshold corresponding to a current current value and a predicted temperature value; wherein the current current value is a current value corresponding to the actual temperature value of the busbar slot monitored in real time.

[0081] It can be understood that the actual temperature value of the busbar slot is monitored in real time, wherein the temperature of each monitoring point on the same loop should not differ greatly because the currents are the same, and the busbar current value corresponding to the actual temperature value is determined at the same time, and then the target gray box model of the busbar temperature rise and the busbar current is constructed to calculate the reasonable range value of the temperature rise, i.e. the predicted temperature rise threshold, and to predict the predicted temperature value corresponding to the current current value of the busbar slot at the same time. The dynamic threshold based on the algorithm does not require engineers to configure each monitoring point, preventing mismatching and missing configuration, simplifying the configuration process, and compared with the static threshold configured by artificial, the dynamic threshold predicted based on the mechanism model and the big data machine learning algorithm can automatically adapt to the temperature abnormality value under different currents, i.e. the adaptability of the dynamic threshold is wider.

[0082] It should be noted that in order to increase the adaptability to the environment, the model needs to be operated periodically, and the latest historical data can be used for reinforcement learning, i.e. the target gray box model is subjected to reinforcement learning to obtain an enhanced target gray box model, and then the enhanced target gray box model is used to predict a dynamic temperature rise threshold corresponding to a current current value and a predicted temperature value.

[0083] As shown in Figure 1 , the busbar slot temperature warning method of the embodiment further includes:

[0084] In step S400, the difference between the actual temperature value and the predicted temperature value is compared with the dynamic temperature rise threshold to obtain a corresponding comparison result, and it is determined whether to trigger a warning based on the comparison result.

[0085] Understandably, the difference between the actual temperature value corresponding to the current current value of the busbar trunking and the predicted temperature is determined, and then this difference is compared with the dynamic temperature rise threshold predicted by the model. If the difference is greater than the dynamic temperature rise threshold predicted by the model, an alarm is triggered; if the difference is not greater than the dynamic temperature rise threshold predicted by the model, no alarm is triggered.

[0086] For example, see Figure 3 As shown, when the current value is 0.8A, the model predicts the busbar temperature to be 2.5 degrees Celsius, while the actual temperature at the current current value is 4 degrees Celsius. It can be seen that the actual temperature value deviates from the predicted temperature value, and the degree of deviation is greater than the reasonable range of predicted temperature rise (+ΔT=10%), thus triggering an alarm. In other words, when the temperature change deviates from the curve fitted by the temperature and load current, especially when it deviates positively and exceeds the reasonable range of temperature rise, an alarm is triggered.

[0087] In one embodiment, such as Figure 4 As shown, based on the above-described busbar temperature alarm method, the present invention also provides a busbar temperature alarm device, comprising:

[0088] The first model building module 100 is used to build a white-box model to characterize the relationship between bus current and bus temperature rise based on the equipment mechanism modeling method and thermodynamic principles.

[0089] The second model construction module 200 is used to construct an initial gray box model between the bus current and the bus temperature rise based on the white box model.

[0090] The model training module 300 is used to train the initial gray box model based on a machine learning algorithm to obtain a trained target gray box model.

[0091] The prediction module 400 is used to predict the dynamic temperature rise threshold and the predicted temperature value corresponding to the current current value using the target gray box model; wherein, the current current value is the current value corresponding to the actual temperature value of the busbar trunking currently monitored.

[0092] The alarm module 500 is used to compare the difference between the actual temperature value and the predicted temperature value with the dynamic temperature rise threshold to obtain a corresponding comparison result, and to determine whether to trigger an alarm based on the comparison result.

[0093] Figure 5 A schematic diagram of the structure of a terminal provided in an embodiment of this application. The terminal may include:

[0094] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0095] The processor 502 implements the bus duct temperature warning method provided in the above embodiments when executing the program.

[0096] Further, the terminal further comprises:

[0097] The communication interface 503 is used for communication between the memory 501 and the processor 502.

[0098] The memory 501 is used for storing a computer program executable on the processor 502.

[0099] The memory 501 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0100] If the memory 501, the processor 502 and the communication interface 503 are independently implemented, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, only one line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0101] Optionally, in specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can complete communication between each other through an internal interface.

[0102] The processor 502 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0103] The embodiments also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the bus duct temperature warning method as above.

[0104] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0105] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features, and do not imply or connote relative importance or a specific order of categorization of the indicated features. Accordingly, features described as "first" or "second" can be explicitly or implicitly included in at least one of the features. In the description of the application, the term "N" means at least two, for example, two, three, etc., unless explicitly stated otherwise.

[0106] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes and methods described can be executably encoded on a machine- readable medium in a data signal embodied in an electromagnetic signal, a wireless signal, or a propagated signal.

[0107] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a list of instructions to implement a logical function, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination thereof. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a product of the manufacturing and / or processing. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electronic connection having one or N wires (electronic devices), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program can be printed, because the program can be electronically captured, via the optical scan of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in the computer memory.

[0108] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, any of the following technologies known in the art or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0109] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, which can be stored in a computer-readable storage medium. The program, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0110] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. The integrated module, if realized in the form of a software functional module and sold or used as an independent product, can also be stored in a computer readable storage medium.

[0111] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

[0112] In summary, the temperature warning method, device, terminal and storage medium of the bus duct disclosed by the present application, the temperature warning method of the bus duct comprises: constructing a white box model for representing the corresponding relationship between the bus current and the bus temperature rise according to the device mechanism modeling method and the thermodynamic principle; based on the white box model, an initial gray box model between the bus current and the bus temperature rise is constructed, and the initial gray box model is trained based on a machine learning algorithm to obtain a trained target gray box model; the target gray box model is used to predict a dynamic temperature rise threshold corresponding to a current current value and a predicted temperature value; wherein the current current value is the current value corresponding to the actual temperature value of the bus duct currently monitored; the difference between the actual temperature value and the predicted temperature value is compared with the dynamic temperature rise threshold to obtain a corresponding comparison result, and whether to trigger an alarm is determined based on the comparison result. The present application models the bus duct, constructs a white box model between the bus temperature and the bus current according to the device mechanism modeling method and the thermodynamic principle, and then constructs a corresponding gray box model based on the white box model, and trains the gray box model based on a machine learning algorithm, so as to fit the real model, improve the adaptability of the model, i.e. the modeling of the bus duct device is realized by combining the device mechanism and the big data machine learning, so that the alarm applicability is wider and the effect is better. Finally, a dynamic threshold is generated by model prediction, which can automatically adapt to temperature abnormality under different currents, thereby solving the problem that manual configuration of static threshold cannot accurately feedback the abnormal temperature rise of the bus, i.e. the technical scheme of the present application makes the alarm strategy follow the thermodynamic principle, be independent of pure data, and avoid anti-logic, thereby having higher reliability and better alarm effect.

[0113] It is to be understood that the application is not limited to the examples described above, which can be modified or adapted in several ways by those skilled in the art without departing from the scope of the present application, as defined by the appended claims.

Claims

1. A method for temperature alarm of a busbar trunking, characterized in that, The method includes: A white-box model is constructed based on the equipment mechanism modeling method and thermodynamic principles to characterize the relationship between bus current and bus temperature rise; Based on the white-box model, an initial gray-box model is constructed between the bus current and the bus temperature rise, and the initial gray-box model is trained using a machine learning algorithm to obtain a trained target gray-box model. The target gray box model is used to predict the dynamic temperature rise threshold and the predicted temperature value corresponding to the current current value; wherein, the current current value is the current value corresponding to the actual temperature value of the bus trunking currently monitored. The difference between the actual temperature value and the predicted temperature value is compared with the dynamic temperature rise threshold to obtain the corresponding comparison result, and an alarm is triggered based on the comparison result. The white-box model is as follows: ； Where ∆T represents the busbar temperature rise, I represents the busbar current, R represents the conductor resistance, and K t The coefficient of performance is represented by t, and time is represented by t. The initial gray-box model for constructing the relationship between the bus current and the bus temperature rise based on the white-box model includes: The physical characteristics of the equipment in the white-box model are determined, and the physical characteristics of the equipment in the white-box model are mathematically abstracted to determine the target, input features and basic model of the gray-box model of the busbar. Based on the gray box model objective, the input features, and the basic model, an initial gray box model is constructed to connect the bus current and the bus temperature rise. The gray box model targets the bus temperature rise, the input feature is the bus current, and the basic model is a univariate quadratic function containing random variables. The initial gray box model is: ； in, This indicates the temperature rise of the busbar. Indicates the first coefficient. This represents the second coefficient, where x represents the bus current. This refers to the random quantity.

2. The busbar temperature alarm method according to claim 1, characterized in that, The step of training the initial gray box model using a machine learning algorithm to obtain the trained target gray box model includes: The target gray box model is obtained by training the initial gray box model using the linear regression algorithm in machine learning and the pre-collected training data.

3. The busbar temperature alarm method according to claim 1, characterized in that, After training the initial gray box model using a machine learning algorithm to obtain the trained target gray box model, the process further includes: The target gray-box model is validated using a pre-collected validation dataset to obtain the corresponding validation results; Based on the verification results, determine whether to continue the step of training the initial gray box model using a machine learning algorithm to obtain the trained target gray box model.

4. The busbar temperature alarm method according to any one of claims 1 to 3, characterized in that, After training the initial gray box model using a machine learning algorithm to obtain the trained target gray box model, the process further includes: The target gray box model is subjected to reinforcement learning to obtain an enhanced target gray box model; The step of using the target gray box model to predict the dynamic temperature rise threshold and predicted temperature value corresponding to the current value includes: The enhanced target gray box model is used to predict the dynamic temperature rise threshold and the predicted temperature value corresponding to the current current value.

5. A temperature alarm device for a busbar trunking, characterized in that, The device includes: The first model building module is used to build a white-box model that characterizes the relationship between bus current and bus temperature rise based on the equipment mechanism modeling method and thermodynamic principles. The second model construction module is used to construct an initial gray box model between the bus current and the bus temperature rise based on the white box model. The model training module is used to train the initial gray box model based on machine learning algorithms to obtain the trained target gray box model. The prediction module is used to predict the dynamic temperature rise threshold and the predicted temperature value corresponding to the current current value using the target gray box model; wherein the current current value is the current value corresponding to the actual temperature value of the busbar trunking currently monitored. The alarm module is used to compare the difference between the actual temperature value and the predicted temperature value with the dynamic temperature rise threshold to obtain a corresponding comparison result, and to determine whether to trigger an alarm based on the comparison result. The white-box model is as follows: ； Where ∆T represents the busbar temperature rise, I represents the busbar current, R represents the conductor resistance, and K t The coefficient of performance is represented by t, and time is represented by t. The second model building module is specifically used for: The physical characteristics of the equipment in the white-box model are determined, and the physical characteristics of the equipment in the white-box model are mathematically abstracted to determine the target, input features and basic model of the gray-box model of the busbar. Based on the gray box model objective, the input features, and the basic model, an initial gray box model is constructed to connect the bus current and the bus temperature rise. The gray box model targets the bus temperature rise, the input feature is the bus current, and the basic model is a univariate quadratic function containing random variables. The initial gray box model is: ； in, This indicates the temperature rise of the busbar. Indicates the first coefficient. This represents the second coefficient, where x represents the bus current. This refers to the random quantity.

6. A terminal, characterized in that, include: The system includes a memory, a processor, and a busbar temperature alarm program stored in the memory and executable on the processor, wherein the busbar temperature alarm program, when executed by the processor, implements the steps of the busbar temperature alarm method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that can be executed to implement the steps of the busbar temperature alarm method as described in any one of claims 1 to 4.

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