Intelligent management device, method and equipment for cable trench heat dissipation system

Abstract

The invention discloses an intelligent management device, method and equipment for a cable trench heat dissipation system, and belongs to the technical field of electric power facilities. The device comprises a working state identification module which is used for identifying whether the exhaust fan in the cable trench is in a working state or a stagnation state; the temperature change data acquisition module is used for acquiring action segments of the exhaust fans and acquiring temperature change data of the action segments corresponding to the exhaust fans; the cooling effect determination module is used for correspondingly inputting the working parameters of the exhaust fan and the temperature change data of the action segment into a preset algorithm model, and determining the cooling effect score of the exhaust fan for the action segment; and the exhaust fan updating module is used for determining the exhaust fan as the exhaust fan to be replaced under the condition that the value is lower than the set value, and determining parameter information of the replaced exhaust fan according to the working parameters of the exhaust fan. According to the technical scheme, heat dissipation intelligent management of the cable in the cable trench can be improved, the operation safety of the cable is improved, and the service life of the cable is prolonged.

Description

Technical Field

[0001] This application belongs to the technical field of power facilities, and particularly relates to an intelligent management device, method and equipment for a cable trench heat dissipation system. Background Art

[0002] With the rapid development of technology, the laying scenarios and demands of cables in cities are gradually increasing, and the way of laying cables in cable trenches has become a popular choice.

[0003] When laying cables in a cable trench, there is no need to occupy aerial resources. It will not only not affect the aesthetics due to a large number of cable routes on the ground, but also will not be affected by external object impacts, sunlight, rain, etc. because it is laid in the cable trench, which can improve the operation safety of the cables to a certain extent. However, if the temperature of the cables in the cable trench is too high, it may cause signal interruption or cable damage. Moreover, because the heat in the cable trench cannot be dissipated in time, the entire cable channel is in a high-temperature environment for a long time, accelerating the aging of the cable insulation layer. Summary of the Invention

[0004] The purpose of the embodiments of this application is to provide an intelligent management device, method and equipment for a cable trench heat dissipation system, aiming to solve the problem of insufficient heat dissipation of cables in the cable trench due to environmental reasons, resulting in an increase in the aging of the cable insulation layer and even signal interruption during the working process.

[0005] In a first aspect, the embodiments of this application provide an intelligent management device for a cable trench heat dissipation system, and the device includes:

[0006] A working state recognition module, configured to recognize whether the exhaust fan in the cable trench is in a working state or a stagnant state;

[0007] A temperature change data acquisition module, configured to obtain the action segment of the exhaust fan and acquire the temperature change data of the corresponding action segment of each exhaust fan through a temperature sensor when the exhaust fan is in a working state;

[0008] A cooling effect determination module, configured to input the working parameters of the exhaust fan and the temperature change data of the action segment into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model;

[0009] An exhaust fan update module, configured to determine that the exhaust fan is an exhaust fan to be replaced when the cooling effect score is lower than a set score, and determine the parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan.

[0010] Furthermore, the temperature change data includes temperature drop rate data and / or temperature drop amplitude data within an observation period;

[0011] A cooling effect determination module, specifically used for:

[0012] Input the working parameters of the exhaust fan and the temperature drop rate data of the action segments into a preset algorithm model correspondingly, and determine the cooling effect score of the exhaust fan for the action segments through the preset algorithm model;

[0013] Or,

[0014] Input the working parameters of the exhaust fan and the temperature drop amplitude data within the observation period of the action segments into a preset algorithm model correspondingly, and determine the cooling effect score of the exhaust fan for the action segments through the preset algorithm model;

[0015] Or,

[0016] Input the working parameters of the exhaust fan, the temperature drop rate data, and the temperature drop amplitude data within the observation period of the action segments into a preset algorithm model correspondingly, and determine the cooling effect score of the exhaust fan for the action segments through the preset algorithm model.

[0017] Further, the device further includes:

[0018] An exhaust fan duct analysis module, used to construct a virtual exhaust fan duct according to the installation position of the exhaust fan and the environment where the exhaust fan and the cable are located, determine whether the exhaust fan duct is in a state conducive to heat dissipation according to the virtual exhaust fan duct, and if not, generate an exhaust fan duct blockage prompt message.

[0019] Further, the device further includes:

[0020] A temperature extension model construction module, used to determine the temperature extension model of the cable in the cable trench according to the real-time temperature obtained by each temperature sensor and the installation position of each temperature sensor;

[0021] A heat dissipation system defect warning module, used to determine whether there is a heat dissipation defect segment in the cable in the cable trench according to the temperature extension model, and if so, generate a warning message; wherein, the warning message includes the segment position information of the heat dissipation defect segment.

[0022] Further, the temperature extension model construction module, specifically used for:

[0023] Obtain the environmental parameters of the cable in the cable trench and construct an environmental model;

[0024] Determine the temperature extension model of the cable in the cable trench according to the real-time temperature obtained by each temperature sensor, the installation position of each temperature sensor, and the environmental model of the cable at different positions.

[0025] Further, the temperature extension model construction module is also specifically used for:

[0026] After constructing the environmental model, determine the heat dissipation influence factors of the environmental model at different positions according to the environmental model;

[0027] According to the real-time temperature obtained by each temperature sensor, the installation position of each temperature sensor, and the heat dissipation influence factors of the cable at different positions, determine the temperature extension model of the cable in the cable trench.

[0028] Further, the exhaust fan update module is further configured to:

[0029] After determining the parameter information of the replacement exhaust fan, generate a product parameter report of the replacement exhaust fan, where the product parameter report includes replacement position information for determining the actual installation position of each replacement exhaust fan.

[0030] In a second aspect, an embodiment of the present application provides an intelligent management method for a cable trench heat dissipation system, and the method includes:

[0031] Identify whether the exhaust fan in the cable trench is in a working state or a stagnant state;

[0032] When the exhaust fan is in a working state, obtain the action segment of the exhaust fan, and obtain the temperature change data of the action segment corresponding to each exhaust fan through a temperature sensor;

[0033] Correspondingly input the working parameters of the exhaust fan and the temperature change data of the action segment into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model;

[0034] When the cooling effect score is lower than the set score, determine that the exhaust fan is an exhaust fan to be replaced, and determine the parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan.

[0035] Further, the temperature change data includes temperature drop rate data and / or temperature drop amplitude data within the observation period;

[0036] Correspondingly input the working parameters of the exhaust fan and the temperature change data of the action segment into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model, including:

[0037] Correspondingly input the working parameters of the exhaust fan and the temperature drop rate data of the action segment into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model;

[0038] Or,

[0039] Input the working parameters of the exhaust fan and the data of the temperature drop amplitude within the observation period of the segmented function into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the segmented function through the preset algorithm model;

[0040] Or,

[0041] Input the working parameters of the exhaust fan, the temperature drop rate data, and the data of the temperature drop amplitude within the observation period of the segmented function into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the segmented function through the preset algorithm model.

[0042] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0043] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0044] In a fifth aspect, an embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0045] In the embodiment of the present application, a working state recognition module is used to recognize whether the exhaust fan in the cable trench is in a working state or a stagnant state; a temperature change data acquisition module is used to, when the exhaust fan is in a working state, obtain the segmented function of the exhaust fan, and obtain the temperature change data of the corresponding segmented function of each exhaust fan through a temperature sensor; a cooling effect determination module is used to input the working parameters of the exhaust fan and the temperature change data of the segmented function into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the segmented function through the preset algorithm model; an exhaust fan update module is used to, when the cooling effect score is lower than a set score, determine that the exhaust fan is an exhaust fan to be replaced, and determine the parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan. The above technical solution can monitor the temperature of the segmented function of the exhaust fan and input it into a machine learning model, and obtain the result output by the model to determine whether the function of the exhaust fan meets the cooling requirements, thereby improving the intelligent management of the heat dissipation of the cables in the cable trench, improving the operation safety of the cables, and extending the service life of the cables. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1It is a schematic structural diagram of the intelligent management device of the cable trench heat dissipation system provided in the first embodiment of the present application;

[0047] Figure 2 It is a schematic flow chart of the intelligent management method of the cable trench heat dissipation system provided in the second embodiment of the present application;

[0048] Figure 3 It is a schematic structural diagram of the electronic device provided in the third embodiment of the present application. Specific embodiments

[0049] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further describes the specific embodiments of the present application in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the sake of convenience of description, only the parts related to the present application are shown in the drawings, rather than all the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. When the operations are completed, the process can be terminated, but there can also be additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0050] The following will clearly describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0051] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0052] The following will describe in detail the intelligent management device, method and equipment of the cable trench heat dissipation system provided in the embodiments of the present application with reference to the accompanying drawings and through specific embodiments and their application scenarios.

[0053] Embodiment 1

[0054] Figure 1 It is a schematic structural diagram of an intelligent management device for a cable trench heat dissipation system provided in Embodiment 1 of the present application. As Figure 1 shown, the device includes:

[0055] A working state recognition module 110, configured to recognize whether the exhaust fan in the cable trench is in a working state or a stagnant state;

[0056] A temperature change data acquisition module 120, configured to, when the exhaust fan is in a working state, obtain the acting section of the exhaust fan, and obtain the temperature change data of the acting section corresponding to each exhaust fan through a temperature sensor;

[0057] A cooling effect determination module 130, configured to input the working parameters of the exhaust fan and the temperature change data of the acting section into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the acting section through the preset algorithm model;

[0058] An exhaust fan update module 140, configured to, when the cooling effect score is lower than a set score, determine that the exhaust fan is an exhaust fan to be replaced, and determine the parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan.

[0059] The present application is applicable to scenarios of cable temperature monitoring and heat dissipation management in cable trenches.

[0060] Based on the above usage scenarios, it can be understood that the execution entity of the present application can be an intelligent management platform, which can be deployed on intelligent devices, such as deployed on desktop computers, laptop computers, mobile phones, tablet computers, and interactive multimedia devices, etc., and no excessive limitation is made here.

[0061] Among them, the working state recognition module 110 is a program or component module with specific functions, and its purpose is to judge and distinguish the states of relevant devices.

[0062] The cable trench can be an underground or semi-underground passage structure specifically used for laying and accommodating cables, which can play a role in protecting the cables and facilitating management, etc. The exhaust fan is a mechanical and electrical device that relies on a motor to drive the fan blades to rotate to achieve functions such as air exchange and ventilation. Here, it is a device installed in the cable trench to adjust the air environment. Among them, the exhaust fan being in a working state represents a description of an operating situation where the exhaust fan is operating normally and performing expected functions such as ventilation. The exhaust fan being in a stagnant state, on the contrary, refers to a description of a static situation where the exhaust fan has stopped rotating and no longer performs related operations such as ventilation.

[0063] In this solution, by detecting physical characteristics such as whether the exhaust fan motor is powered on and whether the fan blade is rotating, or by monitoring the feedback signals of its related control circuits and other generation methods, to distinguish and determine whether the exhaust fan in the cable trench is in a working state or a stagnant state.

[0064] The temperature change data acquisition module 120 is mainly responsible for collecting data information related to temperature changes. Among them, the segmented areas of the exhaust fan's function can be the respective intervals obtained by dividing the area affected by its ventilation function based on factors such as the performance of the exhaust fan, such as ventilation volume, air supply distance, and installation location. The temperature sensor can be an electronic component or device that can sense and detect the surrounding environmental temperature and convert the temperature physical quantity into recognizable data such as corresponding electrical signals, and is used to obtain temperature-related information. Temperature change data can be data materials suitable for recording the specific situation of temperature value changes in a specific area, that is, the segmented areas of the exhaust fan's function, within a certain period of time, such as information on the amplitude of temperature increase or decrease.

[0065] In this solution, based on factors such as the installation layout of the exhaust fan and the ventilation coverage range, the segmented areas of the exhaust fan's function are determined according to the pre-set division rules, and in this way, the relevant information of the segmented areas is generated.

[0066] Among them, temperature sensors can be distributed at corresponding positions within the segmented areas of each exhaust fan, and the temperature is periodically detected at a certain time interval, and the detected temperature value is converted into an electrical signal for transmission and storage, so as to realize the method of collecting temperature change data of each segmented area.

[0067] The cooling effect determination module 130 is used to analyze and determine the degree of the exhaust fan's cooling effect. The working parameters of the exhaust fan include various related index values such as the rotation speed, power, and air volume of the exhaust fan, which can reflect its working performance and operating state. The pre-set algorithm model can be a model obtained by pre-training through machine learning, based on specific mathematical algorithms, logical relationships, etc., and is used to process the input data and output corresponding results. Here, it is a model set up for analyzing the cooling effect. The cooling effect score can be a specific numerical score obtained by operating through the pre-set algorithm model, and is used to quantitatively represent the degree of the cooling effect produced by the exhaust fan on its segmented area.

[0068] In this solution, according to the data format and corresponding relationship required by the pre-set algorithm model, the working parameters of the exhaust fan and the temperature change data of the segmented areas are accurately sorted out and then transmitted into the pre-set algorithm model, so that the model can perform subsequent operations based on these data.

[0069] Among them, a preset algorithm model calculates, analyzes, compares, and processes the input working parameters of the exhaust fan and temperature change data according to built-in mathematical operation rules, and finally generates a determination method for the cooling effect score of the exhaust fan for the action segment.

[0070] The exhaust fan update module 140 can be used to determine whether the exhaust fan needs to be replaced and determine the relevant parameter information of the replacement device. The cooling effect score can be a quantitative value reflecting the degree of the exhaust fan's cooling effect and an important reference index for measuring whether its performance meets the standard. The set score can be a specific value preset by humans and used to compare with the cooling effect score to determine whether the exhaust fan meets the usage requirements and whether it needs to be replaced. The exhaust fan to be replaced can be an exhaust fan that is determined to have a poor cooling effect after comparison and needs to be replaced. The parameter information of the replacement exhaust fan involves the relevant performance index information of the new exhaust fan to be used to replace the original exhaust fan, such as specific parameters such as power, speed, and size, so as to accurately select a suitable replacement device.

[0071] This solution can compare the numerical values of the cooling effect score of the current exhaust fan with the set score. If the cooling effect score is lower than the set score, it is determined that the exhaust fan is an exhaust fan to be replaced, and then, based on the working parameters of the original exhaust fan, referring to principles such as air volume matching and installation size adaptation, a determination method for the parameter information of the replacement exhaust fan is analyzed and generated.

[0072] Based on the above technical solution, optionally, the temperature change data includes temperature drop rate data and / or temperature drop amplitude data within the observation period;

[0073] The cooling effect determination module is specifically used for:

[0074] Correspondingly input the working parameters of the exhaust fan and the temperature drop rate data of the action segment into the preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model;

[0075] Or,

[0076] Correspondingly input the working parameters of the exhaust fan and the temperature drop amplitude data within the observation period of the action segment into the preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model;

[0077] Or,

[0078] Correspondingly input the working parameters of the exhaust fan, the temperature drop rate data, and the temperature drop amplitude data within the observation period of the action segment into the preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model.

[0079] Among them, the temperature drop rate data can be a quantitative numerical representation of how fast the temperature decreases within a specific time period. It is usually measured by the number of degrees the temperature drops per unit time (such as degrees Celsius per minute, etc.), reflecting the rate of temperature drop.

[0080] The temperature drop amplitude data within the observation period can be the temperature difference between the start and end of the observation within a pre-set observation time period, such as several hours or several days. It reflects the overall degree of temperature drop during this period.

[0081] In this solution, for the first solution, that is, inputting the temperature drop rate data, according to the data format requirements specified by the preset algorithm model, the working parameters of the exhaust fan, such as rotational speed, power, etc., can be sorted and matched with the corresponding temperature drop rate data for each functional segment, and then transmitted into the preset algorithm model. Through the internal mathematical operation logic, rules, etc. set in the model that are related to the temperature drop rate and working parameters, the determination method of the cooling effect score of the exhaust fan for this functional segment is finally generated. For example, in the model, the score may be calculated based on a certain functional relationship between the rotational speed and the temperature drop rate.

[0082] For the second solution, that is, inputting the temperature drop amplitude data within the observation period, according to the established data format of the preset algorithm model, the working parameters of the exhaust fan and the temperature drop amplitude data within the observation period for each functional segment can be integrally corresponded and accurately input into the preset algorithm model. By means of the operation rules, analysis logic, etc. constructed in the model for the relationship between the temperature drop amplitude and the working parameters, the cooling effect score of the exhaust fan for the functional segment is calculated and obtained. Its determination method is realized based on this specific input data and the internal processing mechanism of the model. For example, in the model, a weighted calculation rule for different power levels with respect to the temperature drop amplitude data is set to determine the score.

[0083] For the third solution, that is, inputting multiple types of data simultaneously, according to the format required by the preset algorithm model, the working parameters of the exhaust fan, the temperature drop rate data, and the temperature drop amplitude data within the observation period for each functional segment are all reasonably corresponded and arranged in an orderly manner and then input into the model. The model uses complex operation rules and analysis and judgment logics that comprehensively consider the relationships between these types of data to determine the cooling effect score of the exhaust fan for the functional segment. This determination method covers the operation and processing process under the collaborative action of multiple data. For example, the model will comprehensively consider the multiple relationships between the rotational speed, power, temperature drop rate, and amplitude to calculate the score.

[0084] Its execution code is as follows:

[0085] # Define the exhaust fan class, including some example working parameter attributes (simply showing two parameters, rotational speed and power here)

[0086] class VentilationFan:

[0087] def __init__(self, speed, power):

[0088] self.speed = speed # Rotation speed, the unit can be set by yourself, such as revolutions per minute

[0089] self.power = power # Power, unit is watt

[0090] # Define the action section class, including example attributes of relevant temperature data

[0091] class ActionSection:

[0092] def __init__(self, temperature_drop_speed, temperature_drop_range):

[0093] self.temperature_drop_speed = temperature_drop_speed # Temperature drop speed, unit is degrees Celsius per minute

[0094] self.temperature_drop_range = temperature_drop_range # Temperature drop amplitude within the observation period, unit is degrees Celsius

[0095] # Assume the function corresponding to the preset algorithm model. Here is just a simple example, and it will be more complex in reality. The parameters vary according to different input situations

[0096] def preset_algorithm_model_speed(fan, section):

[0097] # Simply assume that the score is calculated based on the product of the rotation speed and the temperature drop speed (to be adjusted according to the actual business logic)

[0098] score = fan.speed * section.temperature_drop_speed

[0099] return score

[0100] def preset_algorithm_model_range(fan, section):

[0101] # Simply assume that the score is calculated based on a certain linear relationship between power and temperature drop (actually, it needs to be adjusted according to the real business logic)

[0102] score = fan.power * section.temperature_drop_range

[0103] return score

[0104] def preset_algorithm_model_both(fan,section):

[0105] # Simply assume that the score is calculated by comprehensively considering the weighted calculation of several parameters (actually, it needs to be greatly adjusted according to the real business logic)

[0106] score = (fan.speed * section.temperature_drop_speed) + (fan.power * section.temperature_drop_range)

[0107] return score

[0108] Example of the main code body:

[0109] # Create an example object of an exhaust fan and set the speed and power parameters

[0110] fan = VentilationFan(speed = 1000, power = 50)

[0111] # Create an example object of an action section and set the temperature drop speed and range data

[0112] section = ActionSection(temperature_drop_speed = 0.5, temperature_drop_range = 5)

[0113] # In the first case, only input the temperature drop speed data to calculate the cooling effect score

[0114] score_speed = preset_algorithm_model_speed(fan,section)

[0115] print("Cooling effect score calculated only based on temperature drop speed:",score_speed)

[0116] # In the second case, only input the temperature drop range data during the observation period to calculate the cooling effect score

[0117] score_range = preset_algorithm_model_range(fan, section)

[0118] print("Cooling effect score calculated only based on the temperature drop amplitude within the observation period:", score_range)

[0119] # Third case, input multiple types of data to calculate the cooling effect score

[0120] score_both = preset_algorithm_model_both(fan, section)

[0121] print("Cooling effect score calculated by integrating multiple data:", score_both)

[0122] Through the above code or similar improvement solutions, data processing, function construction, parameter passing, etc. can be accurately performed according to the detailed requirements of the actual algorithm model. Moreover, links such as data acquisition, storage, and invocation also require more perfect code logic to support. At the same time, various aspects such as exception handling need to be considered.

[0123] With this setting of the present solution, an objective evaluation of the cooling effect score of the exhaust fan for the segmented action can be achieved based on different temperature monitoring data, which is used as an important parameter for cable heat dissipation management in the cable trench to ensure the safe operation of the cable.

[0124] The technical solution provided in this embodiment includes a working state recognition module for recognizing whether the exhaust fan in the cable trench is in a working state or a stagnant state; a temperature change data acquisition module for, when the exhaust fan is in a working state, obtaining the segmented action of the exhaust fan and acquiring the temperature change data of the corresponding segmented action of each exhaust fan through a temperature sensor; a cooling effect determination module for inputting the working parameters of the exhaust fan and the temperature change data of the segmented action into a preset algorithm model, and determining the cooling effect score of the exhaust fan for the segmented action through the preset algorithm model; an exhaust fan update module for, when the cooling effect score is lower than the set score, determining the exhaust fan as an exhaust fan to be replaced and determining the parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan. The above technical solution can monitor the temperature of the segmented action of the exhaust fan and input it into the machine learning model, and obtain the result output by the model to determine whether the action of the exhaust fan meets the cooling requirements, thereby improving the intelligent management of cable heat dissipation in the cable trench, enhancing the safe operation of the cable, and extending the service life of the cable.

[0125] In the above embodiments, optionally, the device further includes:

[0126] An exhaust fan duct analysis module, configured to construct a virtual exhaust fan duct according to the installation position of the exhaust fan and the environment where the exhaust fan and the cable are located, determine whether the exhaust fan duct is in a state conducive to heat dissipation according to the virtual exhaust fan duct, and if not, generate an exhaust fan duct blockage prompt message.

[0127] Among them, the installation position of the exhaust fan can be the specific spatial coordinate position information of the exhaust fan in the cable trench obtained by innovative means such as positioning sensors in the Internet of Things (IoT) technology or installation layout image recognition assisted by computer vision. Its position has a key impact on the formation of the duct, the air flow direction, etc., just like the position of a column in a building determines part of the trajectory of the wind flowing indoors.

[0128] The environment where the exhaust fan and the cable are located, with the help of intelligent environment perception technology, such as integrating a variety of sensors, temperature sensors, humidity sensors, gas sensors, etc., and the overall environmental condition information in the cable trench obtained through inspection robots combined with image analysis, including but not limited to the layout of surrounding objects, the air circulation characteristics, and whether there is foreign object accumulation.

[0129] The virtual exhaust fan duct can be constructed by using computer simulation technology, such as computational fluid dynamics (CFD) software simulation or a digital twin model driven by artificial intelligence. Based on the previously obtained data related to the installation position of the exhaust fan and the surrounding environment, a digital model corresponding to the actual duct is constructed in the virtual digital space. It can accurately reproduce the air flow path, flow velocity distribution, etc. in the duct, facilitating various analyses and predictions.

[0130] The state conducive to heat dissipation can be a criterion for evaluating the ideal state of the duct obtained based on advanced thermal imaging analysis technology combined with big data analysis from the perspective of thermal management. For example, the air flow in the duct is smooth, there are no obstacles causing air flow disorder, and heat can be quickly carried out with the air flow, so that the temperature of the environment around the exhaust fan and where the cable is located can be maintained within a reasonable heat dissipation range.

[0131] The exhaust fan duct blockage prompt message can be a reminder message generated and sent when it is determined through the above analysis that the duct is not in a state conducive to heat dissipation, with the help of intelligent communication technology, such as connecting to a remote monitoring terminal through a 5G network or pushing it to a mobile APP. The message content can detail the possible location of the blockage, the estimated degree, etc.

[0132] In this solution, the data on the installation positions of exhaust fans and multi-dimensional data related to the environment where the cables are located can be utilized. By invoking innovative technical means such as 3D modeling software interfaces or automatic modeling algorithms based on deep learning, the process of building a virtual air duct model of the exhaust fan in the virtual space according to the actual physical laws and characteristics is like an architect using digital design tools to construct a building model based on site conditions and building requirements. It requires precise integration of various types of data and following corresponding scientific principles to complete the model creation.

[0133] By importing the virtual air duct model into professional fluid analysis software, such as computational fluid dynamics software based on the finite volume method, or by using intelligent analysis algorithms, such as a classification model trained based on machine learning for judging the heat dissipation performance of the air duct, a comprehensive analysis of the air flow characteristics, heat transfer situation, etc. in the virtual air duct is carried out. Then, according to the pre-set state evaluation criteria conducive to heat dissipation, it is judged whether the actual air duct corresponding to the virtual air duct is in a good heat dissipation state.

[0134] If it is identified that the air duct is in a state that is not conducive to heat dissipation, the system automatically triggers the generation of easy-to-understand prompt statements containing key information based on natural language processing (NLP) technology. At the same time, using intelligent information push technology, such as message queues, instant messaging protocols, etc., these prompt messages are accurately sent to the terminal devices of relevant operation and maintenance personnel.

[0135] This solution realizes the intelligent monitoring and early warning of the heat dissipation state of the actual air duct through the digital means of constructing a virtual air duct, which helps to discover and solve problems affecting heat dissipation such as air duct blockage in advance, ensures the safe and stable operation of the exhaust fans and cables in the cable trench, and improves the reliability and operation and maintenance efficiency of the entire cable heat dissipation system.

[0136] In the above embodiments, optionally, the device further includes:

[0137] A temperature extension model construction module, which is used to determine the temperature extension model of the cables in the cable trench according to the real-time temperatures obtained by each temperature sensor and the installation positions of each temperature sensor;

[0138] A heat dissipation system defect warning module, which is used to determine whether there are heat dissipation defect segments in the cables in the cable trench according to the temperature extension model. If so, warning information is generated; wherein, the warning information includes the segment position information of the heat dissipation defect segments.

[0139] Among them, the real-time temperature can be the actual temperature value at the current moment in the cable trench continuously obtained by means of a high-precision temperature sensor, such as using sensitive elements such as high-precision thermistors and thermocouples, in cooperation with a high-precision analog-to-digital conversion circuit and an intelligent calibration algorithm, at a set time interval, which can be at the second level or even shorter, to reflect the thermal environment state in the cable trench.

[0140] The installation location of the temperature sensor uses positioning technology to accurately determine the spatial coordinate information of the temperature sensor in the complex environment of the cable trench. The temperature data feedback by sensors at different positions corresponds to the thermal states of different parts of the cable. Just like each coordinate point on a map marks the characteristics of different regions, these location information and real-time temperature data are mutually correlated, which are the key elements for accurately constructing the cable temperature distribution model and determine the accuracy and fineness of the temperature extension model's description of the cable temperature.

[0141] The temperature extension model of the cable can be based on the powerful computing and storage capabilities of the cloud computing platform. By integrating and analyzing a large amount of real-time temperature data and the corresponding sensor installation location data, using computational fluid dynamics (CFD) thermal simulation technology combined with algorithms such as generative adversarial network (GAN) or convolutional neural network (CNN) in deep learning, a digital model can be constructed that can reflect the temperature distribution, temperature propagation trend, and heat transfer law of the cable at different positions in the entire cable trench over time. It can clearly show where the temperature of the cable is high, where it is low, and how the temperature changes along the cable extension, providing a global viewing perspective for analyzing the cable heat dissipation system.

[0142] The heat dissipation defect segments can be the partial intervals on the cable in the cable trench that are judged by intelligent analysis based on the temperature distribution and heat transfer presented by the temperature extension model, where the temperature is too high, the heat transfer is blocked, or it does not conform to the normal heat dissipation law. These segments are key areas that need to be focused on and processed in a timely manner, and their accurate identification is crucial for ensuring the safe operation of the cable.

[0143] The alarm information can be a prompt message containing key content carefully generated using natural language processing (NLP) technology when a heat dissipation defect segment is detected, aiming to convey the relevant situation of the heat dissipation problem to the operation and maintenance personnel in a timely and accurate manner. The segment location information of the heat dissipation defect segment in the alarm information can accurately point to the location of the problem, facilitating the operation and maintenance personnel to quickly locate and take corresponding solutions to ensure the normal heat dissipation function of the cable system.

[0144] In this solution, the temperature extension model construction module first collects the real-time temperature data transmitted by temperature sensors scattered throughout the cable trench through the Internet of Things communication protocol (such as the MQTT protocol, etc.), and matches it with the corresponding sensor installation location data one by one. Then, this massive amount of data is transmitted to the big data processing platform, and data mining technology is used to extract the temperature distribution law contained therein. Then, with the help of intelligent modeling algorithms, such as the deep learning algorithms mentioned above, fitting and optimization are carried out, and finally, the process of generating a temperature extension model that can accurately reflect the temperature change and distribution characteristics of the cable is completed.

[0145] After receiving the temperature extension model, the heat dissipation system defect warning module imports it into a dedicated thermal analysis and diagnosis system, and applies pre-set heat dissipation defect judgment criteria. These criteria are obtained through training based on thermal theory, cable heat dissipation characteristics, and a large amount of historical data. Through intelligent algorithms, the temperature data in the model is analyzed and compared segment by segment. Once it is found that the temperature characteristics corresponding to a certain section of the cable meet the judgment conditions of heat dissipation defects, the warning information generation mechanism is immediately triggered. This mechanism uses natural language generation (NLG) technology, combines the position coordinate information of the heat dissipation defect segments in the model, and generates warning information containing detailed segment position information in a format that is easy to understand and standardized.

[0146] This solution uses these two modules to work together for intelligent monitoring and early warning of the heat dissipation of cables in the cable trench, which can timely detect potential heat dissipation defects, help the safe and stable operation of the cable system, effectively improve the initiative and accuracy of operation and maintenance, and reduce the failure risk caused by heat dissipation problems.

[0147] In the above embodiments, optionally, the temperature extension model construction module is specifically used for:

[0148] Obtain the environmental parameters of the cable in the cable trench and construct an environmental model;

[0149] Determine the temperature extension model of the cable in the cable trench according to the real-time temperature obtained by each temperature sensor, the installation position of each temperature sensor, and the environmental model of the cable at different positions.

[0150] Among them, the environmental parameters can be various quantitative indicators obtained by means of a variety of innovative sensors and detection technologies for describing the environment where the cable is located in the cable trench, covering the physical environment. For example, the geometric parameters such as the spatial size and shape of the cable trench are obtained by lidar scanning, the air humidity value is obtained by a humidity sensor, and the concentration of particulate matter in the air is detected by a dust sensor; it also includes chemical environment parameters, such as using a gas sensor to monitor the presence and concentration of harmful gases.

[0151] The environmental model can be based on the powerful computing and storage capabilities of the cloud computing platform, using modeling technologies related to computer graphics, virtual reality (VR) / augmented reality (AR), and spatial analysis methods in geographic information system (GIS) to integrate, render, and digitally reconstruct the numerous environmental parameters of the cable in the cable trench collected, and generate a virtual model that can vividly present the spatial environment status of the cable. You can intuitively see the environmental layout around the cable and the distribution of various elements, which is an important intermediate carrier for further analyzing the relationship between the cable temperature change and the environment.

[0152] In this solution, the temperature extension model construction module establishes communication links with various sensors and related data acquisition devices. For example, it uses communication methods such as industrial Ethernet and wireless sensor networks. According to preset data acquisition rules and frequencies, it actively sends request instructions to each sensor and devices that can provide cable trench environment parameters, and receives data packets containing environment parameter information feedback from them. Subsequently, it uses data parsing algorithms to extract the valid data in the data packets.

[0153] After obtaining rich environment parameters, the module transfers these data to a professional modeling software platform, which may be a customized modeling tool developed based on the open-source Blender, or software such as commercial 3DMAX combined with specialized plugins. Using functional modules such as geometric modeling, material addition, and environment rendering in the software, according to the cable trench space shape, the distribution of various substances, etc. described by the environment parameters, a three-dimensional and visual environment model is gradually built.

[0154] The real-time temperature data collected from each temperature sensor and the corresponding installation position information are deeply integrated with the already constructed environment model at different positions of the cable. First, through the data mapping algorithm, the temperature data is mapped to the corresponding positions in the environment model according to the position, forming a spatial data set with temperature attributes. Then, intelligent algorithms are used to extract features, mine rules, and fit models for this integrated data set. According to the thermal principle and the physical law of cable heat dissipation, the model parameters are continuously adjusted and the model structure is optimized. Finally, a temperature extension model that conforms to the actual situation and can accurately reflect the temperature extension of the cable in the cable trench is generated.

[0155] In this solution, by integrating multi-source data, a temperature extension model with high accuracy and practicability is constructed, providing strong technical support for the heat dissipation monitoring and management of cables in the cable trench, helping to discover potential high-temperature risks in advance, and ensuring the safe and stable operation of the cable system.

[0156] In the above embodiments, optionally, the temperature extension model construction module is further specifically used for:

[0157] After constructing the environment model, determine the heat dissipation influence factors at different positions of the environment model according to the environment model;

[0158] According to the real-time temperature obtained by each temperature sensor, the installation positions of each temperature sensor, and the heat dissipation influence factors of the cable at different positions, determine the temperature extension model of the cable in the cable trench.

[0159] Among them, the heat dissipation influence factor can be a set of key indicators abstracted based on a deep analysis of the complex environment in the cable trench, which can quantitatively characterize the degree of influence of different environmental factors on the cable heat dissipation effect. By using big data analysis technology to conduct correlation mining on a large amount of historical environmental data, covering various aspects of information such as temperature, humidity, air velocity, surrounding object materials and layouts, and the corresponding cable heat dissipation situation data, and combining feature engineering methods in machine learning to extract representative influencing factors, and using mathematical models such as multiple linear regression models and decision tree models to calculate the weight coefficients of the influence of each factor on heat dissipation at different positions, these coefficients constitute the heat dissipation influence factor. For example, at a certain position, the numerical value of the heat dissipation influence factor corresponding to the air velocity is relatively large, indicating that the air flow situation plays a relatively key role in the cable heat dissipation at this position. Just as different ecological factors have different degrees of influence on the growth of organisms in an ecosystem, the heat dissipation influence factor can help accurately grasp the role size of each environmental factor in cable heat dissipation at different positions.

[0160] The heat dissipation influence factors of the environmental model at different positions are the set of heat dissipation influence factors associated with specific environmental elements at each spatial position corresponding to the constructed cable trench environmental model. Due to the differences in environmental conditions at different positions in the cable trench, such as good air circulation near the ventilation opening, while the air may be relatively static and prone to heat accumulation in the corner position, the key factors affecting cable heat dissipation and their action degrees at each position are also different. Sorting and marking these heat dissipation influence factors distinguished by position and closely combined with the environmental model form the heat dissipation influence factors of the environmental model at different positions.

[0161] For each position's data subset in this solution, a big data storage and analysis platform is called, such as a data processing platform built based on the Hadoop distributed file system and Spark computing framework. From the vast amount of historical operation and maintenance data, including the cable temperature, heat dissipation situation monitored at the corresponding position in the past, and the environmental parameter records at that time, data records that match are screened out to construct a local data set for that position. Using feature selection algorithms in the machine learning algorithm library, such as rich algorithm tools provided by Scikit-learn and other methods based on correlation analysis, recursive feature elimination algorithms, etc., to perform feature screening on these local data sets, and find out the key environmental features with strong correlation with cable heat dissipation, that is, to determine which environmental factors have a more significant influence on heat dissipation at this position. Then, by taking the screened key features as independent variables and the actual heat dissipation effect of the cable at the corresponding position as the dependent variable, substituting them into a suitable mathematical model, such as the multiple linear regression and decision tree models mentioned above, and selecting according to the data characteristics and actual situation for training and fitting, the weight coefficients corresponding to each key environmental feature at this position are calculated, that is, the heat dissipation influence factors of the environmental model at this position are determined.

[0162] After obtaining the real-time temperature data of each temperature sensor, the corresponding installation positions, and the heat dissipation influence factors of the cable at different positions, the module first maps the real-time temperature data and the installation positions of the temperature sensors to the corresponding spatial positions in the environmental model, realizing the preliminary fusion of temperature data and the environmental model. Incorporate the heat dissipation influence factors corresponding to each position into a data set, so that the temperature data at each position is associated with its unique heat dissipation influence weight information. For example, the temperature data at a certain position will reflect the influence degree of air velocity on the temperature change at this position according to the heat dissipation influence factor corresponding to the air velocity at this position, making the temperature data more environmentally relevant. Use intelligent algorithms, such as the hybrid architecture of convolutional neural network (CNN) combined with recurrent neural network (RNN) in deep learning. CNN can extract the temperature features at spatial positions, and RNN can process the sequential features of temperature changes over time. Perform operations such as deep feature extraction, time series analysis, and mining of spatial temperature distribution laws on the data set integrated with heat dissipation influence factors. According to the thermal principle and the physical law of cable heat dissipation, through continuously adjusting the parameters in the algorithm and optimizing the model structure, perform multiple iterative calculations. Generate a temperature extension model that can comprehensively and accurately reflect the temperature distribution, temperature propagation law of the cable along its route in the cable trench at different positions over time, and the heat exchange situation with the surrounding environment considering the heat dissipation influence factors at each position. This process is like an artist presenting the global image of cable temperature according to different colors (temperature data), different weights (heat dissipation influence factors), and composition rules.

[0163] The temperature extension model constructed by this solution is more in line with the actual situation and can more accurately provide strong technical support for subsequent work such as heat dissipation monitoring and fault warning of the cable in the cable trench, ensuring the safe and stable operation of the cable system.

[0164] In the above embodiments, optionally, the exhaust fan update module is further configured to:

[0165] After determining the parameter information of the replacement exhaust fan, generate a product parameter report of the replacement exhaust fan, where the product parameter report includes replacement position information for determining the actual installation positions of each replacement exhaust fan.

[0166] Among them, the parameter information for replacing the exhaust fan can be a data set that details the performance indicators of the new device to be used to replace the existing exhaust fan. It covers mechanical performance aspects, such as the impeller diameter, speed range, motor power, etc. of the new exhaust fan. These parameters directly affect the ventilation capacity such as the air volume and air pressure of the exhaust fan; it also includes electrical performance parameters, such as rated voltage, current, power factor, etc., which are related to the adaptability and operation stability of the exhaust fan in the electrical system; and other parameters related to the operating environment and user experience of the device, such as protection level, noise level, etc.

[0167] Among them, the product parameter report can be generated using document automation technology based on the parameter information of the replacement exhaust fan. The report can contain various exhaust fan parameter information and also add important content such as replacement location information. Its purpose is to provide relevant materials for equipment procurement, installation and commissioning, and subsequent operation and maintenance.

[0168] The replacement location information can be specific position coordinates or area identification information clearly specifying the installation location of each replacement exhaust fan in the specific spatial layout of the cable trench. It is determined by analyzing the original exhaust fan layout in the cable trench, combining the distribution of cable heat dissipation requirements, and the optimized design of the ventilation system. For example, a position coding method based on a three-dimensional coordinate system can be used to accurately locate the specific position of a certain exhaust fan in the length, width, and height directions of the cable trench; or in the form of area division, such as dividing the cable trench into multiple ventilation zones, with specific exhaust fan installation sites in each zone, and corresponding zone numbers and site identifications are given.

[0169] In this solution, the document generation software interface is called, such as the docx library based on Python or the API of professional office automation software, to create a new document template. Then, the parameter information of the replacement exhaust fan is filled into the document template field by field according to the predetermined format and order. This process is like filling in a form according to a fixed template, ensuring that each parameter appears accurately in the corresponding position of the report. The previously determined replacement location information is also added to the report to make the report completely cover the performance parameters and installation location information of the exhaust fan. The entire generation process can use automated software tools and preset data processing processes to efficiently and accurately integrate various types of information into a complete product parameter report, providing information support for the subsequent exhaust fan replacement work.

[0170] This solution improves the relevant information of the exhaust fan through the product parameter report. By simultaneously recording the equipment parameters and installation location, it helps to improve the efficiency of subsequent replacement operations and ensure the smooth progress of the cable trench ventilation system update work and achieve the expected effect.

[0171] Example 2

[0172] Figure 2 It is a schematic flowchart of the intelligent management method of the cable trench heat dissipation system provided in the fourth embodiment of the present application. As Figure 2 shown, it specifically includes the following steps:

[0173] S201. Identify whether the exhaust fan in the cable trench is in a working state or a stagnant state;

[0174] S202. When the exhaust fan is in a working state, obtain the working section of the exhaust fan, and obtain the temperature change data of the corresponding working section of each exhaust fan through a temperature sensor;

[0175] S203. Input the working parameters of the exhaust fan and the temperature change data of the working section into a preset algorithm model correspondingly, and determine the cooling effect score of the exhaust fan for the working section through the preset algorithm model;

[0176] S204. When the cooling effect score is lower than the set score, determine that the exhaust fan is an exhaust fan to be replaced, and determine the parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan.

[0177] In this embodiment, identify whether the exhaust fan in the cable trench is in a working state or a stagnant state; when the exhaust fan is in a working state, obtain the working section of the exhaust fan, and obtain the temperature change data of the corresponding working section of each exhaust fan through a temperature sensor; input the working parameters of the exhaust fan and the temperature change data of the working section into a preset algorithm model correspondingly, and determine the cooling effect score of the exhaust fan for the working section through the preset algorithm model; when the cooling effect score is lower than the set score, determine that the exhaust fan is an exhaust fan to be replaced, and determine the parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan. The above technical solution can monitor the temperature of the working section of the exhaust fan and input it into the machine learning model to obtain the result output by the model to determine whether the function of the exhaust fan meets the cooling requirements, thereby improving the intelligent management of the heat dissipation of the cables in the cable trench, improving the operation safety of the cables, and extending the service life of the cables.

[0178] Further, the temperature change data includes temperature drop rate data and / or temperature drop amplitude data within the observation period;

[0179] Inputting the working parameters of the exhaust fan and the temperature change data of the working section into a preset algorithm model correspondingly, and determining the cooling effect score of the exhaust fan for the working section through the preset algorithm model includes:

[0180] Input the working parameters of the exhaust fan and the temperature drop rate data of the working section into a preset algorithm model correspondingly, and determine the cooling effect score of the exhaust fan for the working section through the preset algorithm model;

[0181] Alternatively,

[0182] Input the working parameters of the exhaust fan and the data of the temperature drop amplitude within the observation period of the action segment into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model;

[0183] Alternatively,

[0184] Input the working parameters of the exhaust fan, the temperature drop rate data, and the data of the temperature drop amplitude within the observation period of the action segment into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model.

[0185] The intelligent management method of the cable trench heat dissipation system provided by the embodiments of the present application corresponds to the intelligent management device of the cable trench heat dissipation system provided by the above embodiments, and has the same execution process and beneficial effects. To avoid repetition, it will not be elaborated here.

[0186] Embodiment III

[0187] As Figure 3 shown, the embodiments of the present application further provide an electronic device 300, including a processor 301, a memory 302, a program or instruction stored on the memory 302 and executable on the processor 301. When the program or instruction is executed by the processor 301, it implements each process of the above embodiments of the intelligent management device of the cable trench heat dissipation system, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0188] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0189] Embodiment IV

[0190] The embodiments of the present application further provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the above embodiments of the intelligent management device of the cable trench heat dissipation system, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0191] Wherein, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0192] Embodiment V

[0193] Another embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is configured to run programs or instructions to implement each process of the intelligent management device embodiment of the above cable trench heat dissipation system, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0194] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, system chip, chip system, or system-on-a-chip, etc.

[0195] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0196] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0197] The embodiments of the present application have been described above with reference to the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them belong to the protection scope of the present application.

[0198] The above are only the preferred embodiments of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, more other equivalent embodiments can be included, and the scope of the present application is determined by the scope of the claims.

Claims

1. An intelligent management device for a cable trench heat dissipation system, characterized in that: The device comprises: A working state identification module is used to identify whether the exhaust fan in the cable trench is in working state or in stagnant state; A temperature change data acquisition module, used to obtain the action segments of the exhaust fan when the exhaust fan is in working state, and obtain the temperature change data of the action segments corresponding to each exhaust fan through a temperature sensor; A cooling effect determination module, used to input the working parameters of the exhaust fan and the temperature change data of the action segment into a preset algorithm model, and determine the cooling effect score of the exhaust fan for the action segment through the preset algorithm model; The exhaust fan update module is used to determine that the exhaust fan is an exhaust fan to be replaced when the cooling effect score is lower than a set score, and determine parameter information of the replacement exhaust fan according to the working parameters of the exhaust fan.

2. The intelligent management device for the cable trench heat dissipation system according to claim 1 is characterized in that: The temperature change data includes temperature drop rate data and / or temperature drop amplitude data within an observation period; The cooling effect determination module is specifically used for: Inputting the working parameters of the exhaust fan and the temperature drop speed data of the action segment into a preset algorithm model, and determining the cooling effect score of the exhaust fan on the action segment through the preset algorithm model; or, Input the working parameters of the exhaust fan and the temperature drop amplitude data of the action segment during the observation period into a preset algorithm model, and determine the cooling effect score of the exhaust fan on the action segment through the preset algorithm model; or, The working parameters of the exhaust fan, the temperature drop speed data and the temperature drop amplitude data within the observation period of the action segment are correspondingly input into the preset algorithm model, and the cooling effect score of the exhaust fan for the action segment is determined by the preset algorithm model.

3. The intelligent management device for the cable trench heat dissipation system according to claim 1 is characterized in that: The device also includes: The exhaust fan duct analysis module is used to construct an exhaust fan virtual duct according to the installation position of the exhaust fan and the environment of the exhaust fan and the cable, and determine whether the exhaust fan duct is in a state conducive to heat dissipation according to the exhaust fan virtual duct. If not, generate exhaust fan duct blockage prompt information.

4. The intelligent management device for the cable trench heat dissipation system according to claim 1, characterized in that: The device also includes: A temperature extension model building module is used to determine a temperature extension model of the cables in the cable trench according to the real-time temperature obtained by each temperature sensor and the installation position of each temperature sensor; The heat dissipation system defect alarm module is used to determine whether there is a heat dissipation defect segment in the cable in the cable trench according to the temperature extension model, and if so, generate an alarm message; wherein the alarm message includes the segment position information of the heat dissipation defect segment.

5. The intelligent management device for the cable trench heat dissipation system according to claim 4 is characterized in that: The temperature extension model building module is specifically used for: Obtain the environmental parameters of the cable in the cable trench and build an environmental model; The temperature extension model of the cable in the cable trench is determined according to the real-time temperature obtained by each temperature sensor, the installation position of each temperature sensor and the environmental model of the cable at different positions.

6. The intelligent management device for the cable trench heat dissipation system according to claim 5, characterized in that: The temperature extension model building module is also specifically used for: After the environmental model is constructed, heat dissipation influencing factors of the environmental model at different positions are determined according to the environmental model; The temperature extension model of the cable in the cable trench is determined based on the real-time temperature obtained by each temperature sensor, the installation position of each temperature sensor, and the heat dissipation influencing factors of the cable at different positions.

7. The intelligent management device for the cable trench heat dissipation system according to claim 1, characterized in that: The exhaust fan update module is also used for: After determining the parameter information of the replacement exhaust fans, a product parameter report of the replacement exhaust fans is generated, wherein the product parameter report includes replacement position information for determining the actual installation position of each replacement exhaust fan.

8. An intelligent management method for a cable trench heat dissipation system, characterized in that: The method comprises: Identify whether the exhaust fan in the cable trench is in working or stagnant state; When the exhaust fan is in working state, obtaining the action segments of the exhaust fan, and obtaining temperature change data of the action segments corresponding to each exhaust fan through a temperature sensor; Inputting the working parameters of the exhaust fan and the temperature change data of the action segment into a preset algorithm model, and determining the cooling effect score of the exhaust fan on the action segment through the preset algorithm model; When the cooling effect score is lower than the set score, the exhaust fan is determined to be the exhaust fan to be replaced, and parameter information of the replacement exhaust fan is determined according to the working parameters of the exhaust fan.

9. The intelligent management method of the cable trench heat dissipation system according to claim 8, characterized in that: The temperature change data includes temperature drop rate data and / or temperature drop amplitude data within an observation period; Inputting the working parameters of the exhaust fan and the temperature change data of the action segment into a preset algorithm model accordingly, and determining the cooling effect score of the exhaust fan on the action segment by the preset algorithm model, including: Inputting the working parameters of the exhaust fan and the temperature drop speed data of the action segment into a preset algorithm model, and determining the cooling effect score of the exhaust fan on the action segment through the preset algorithm model; or, Input the working parameters of the exhaust fan and the temperature drop amplitude data of the action segment during the observation period into a preset algorithm model, and determine the cooling effect score of the exhaust fan on the action segment through the preset algorithm model; or, The working parameters of the exhaust fan, the temperature drop speed data and the temperature drop amplitude data within the observation period of the action segment are correspondingly input into the preset algorithm model, and the cooling effect score of the exhaust fan for the action segment is determined by the preset algorithm model.

10. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the intelligent management method of the cable trench heat dissipation system as described in any one of claims 8 to 9 are implemented.

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