Temperature control charging cabin for cable tunnel inspection robot and working method of temperature control charging cabin

Abstract

The invention belongs to the field of cable inspection, and discloses a temperature control charging cabin for a cable tunnel inspection robot and a working method of the temperature control charging cabin, an integrated identity verification module ensures the safety of the charging process, and meanwhile, a self-adaptive temperature control platform remarkably improves the environmental adaptability of a charging system. According to the platform, the real-time temperature in a charging cabin is firstly collected, then a control decision unit intelligently optimizes PID controller parameters by using a variable universe fuzzy reasoning technology in combination with a multi-population genetic algorithm, and the principle enables a temperature control system to accurately respond to wide temperature range changes. The temperature adjustment execution unit rapidly adjusts the temperature in the cabin according to the optimization parameters, and effectively compensates the influence of the extreme temperature on the lithium battery capacity and the sensor precision. By adopting the temperature control charging cabin, the charging efficiency is improved, the service life of equipment is prolonged, the high sensitivity is kept, the all-weather and high-density inspection requirements of an urban power grid are met, and the limitation of a traditional manual battery replacement mode and the threat of an extreme temperature environment to a charging system are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of cable inspection, and particularly relates to a temperature-controlled charging cabin for a cable tunnel inspection robot and its working method. Background Art

[0002] In recent years, with the development of urban power grids towards high voltage and intelligence, the application scale of power cable lines in cities such as Taiyuan has been continuously expanding. As an important equipment to ensure the safe operation of cables, cable tunnel inspection robots have significantly improved the operation and maintenance efficiency by replacing manual inspections and reduced the risk of working in confined spaces. However, there are obvious defects in the existing technical system in the charging support link, which restricts the large-scale application of robots.

[0003] Currently, the defects in the charging support link are specifically manifested as follows: First, the traditional manual battery replacement mode has significant limitations. Existing inspection robots rely on full-time personnel to manually replace the battery pack, and it is necessary to configure double redundant batteries and implement special management. This mode not only increases the operation and maintenance costs but also has poor response timeliness. It is only applicable to small-scale temporary scenarios and is difficult to meet the all-weather and high-density inspection requirements of urban power grids. Second, extreme temperature environments pose a serious threat to the charging system. There is a wide temperature range environment of -20°C to 50°C in cable tunnels, and existing charging cabins lack a temperature control compensation mechanism. Experimental data shows that the lithium battery capacity decays by 30% in a -10°C environment, and the sensor accuracy drops by 15% when the equipment operating temperature is below 0°C, directly resulting in the interruption of inspection tasks.

[0004] It can be seen that the charging equipment of existing cable tunnel inspection robots has weak adaptability to extreme temperature environments and cannot achieve good temperature compensation, resulting in low charging efficiency and affecting the service life and sensitivity of the equipment itself. Summary of the Invention

[0005] The present invention provides a temperature-controlled charging cabin for a cable tunnel inspection robot and its working method to solve the technical problem that the charging equipment of existing cable tunnel inspection robots has weak adaptability to extreme temperature environments and cannot achieve good temperature compensation, resulting in low charging efficiency and affecting the service life and sensitivity of the equipment itself.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A temperature-controlled charging cabin for a cable tunnel inspection robot, comprising: An identity verification module for collecting the identity information of the inspection robot; An adaptive temperature control platform for dynamically and adaptively adjusting the internal temperature of the charging cabin; A charging module for charging the inspection robot that has passed the identity verification; Among them, the adaptive temperature control platform includes: A collection unit for collecting the real-time temperature inside the charging cabin; A control decision-making unit for performing variable universe fuzzy reasoning based on the collected real-time temperature inside the charging cabin, and optimizing the parameters of the variable universe fuzzy PID controller using a multi-population genetic algorithm; A temperature regulation execution unit for regulating the internal temperature of the charging cabin according to the optimized parameters.

[0007] Furthermore, the performing variable universe fuzzy reasoning based on the collected real-time temperature inside the charging cabin includes: Obtaining the control quantity of the charging cabin temperature based on the collected real-time temperature inside the charging cabin; Performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy-processed control quantity; Dividing the fuzzy space of the charging cabin temperature control according to the fuzzy-processed control quantity; Constructing a variable universe for the charging cabin temperature control according to the fuzzy-processed control quantity and the fuzzy space of the charging cabin temperature control; Converting the original universe into a variable universe using a scaling factor to obtain the deviation and rate of change of deviation of the charging cabin temperature, and completing the variable universe fuzzy reasoning.

[0008] Furthermore, the performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy-processed control quantity, and the specific formula is as follows:

[0009] In the formula, Z represents the fuzzy-processed control quantity, g represents the control quantity of the charging cabin temperature, Z max and Z min are respectively the maximum value and the minimum value of the charging cabin temperature control quantity; The constructing a variable universe for the charging cabin temperature control according to the fuzzy-processed control quantity and the fuzzy space of the charging cabin temperature control, where the variable universe is expressed as follows:

[0010]

[0011] In the formula, and are both universe scaling factors; and are respectively the variable universes of x and y; and are both initial universes; x and y are charging cabin temperature variables.

[0012] Further, the parameter optimization of the variable universe fuzzy PID controller using the multi-population genetic algorithm includes: Obtaining the initial parameters of the variable universe fuzzy PID controller using the multi-population genetic algorithm; Based on the initial parameters of the variable universe fuzzy PID controller and the fuzzy rules of the preset fuzzy PID, using the multi-population genetic algorithm for iterative optimization, and outputting the optimal solution as the optimized parameters of the variable universe fuzzy PID controller.

[0013] Further, the identity authentication module includes an image recognition unit; The image recognition unit is used to collect the identity information and real-time position of the inspection robot; the identity information of the inspection robot is used to match with a preset identity library. If the match is successful, it means that the inspection robot passes the identity authentication.

[0014] Further, the temperature-controlled charging cabin further includes a rolling gate control module; The rolling gate control module is used to open the rolling gate of the temperature-controlled charging cabin according to the identity authentication result of the inspection robot and the real-time position; wherein, when the inspection robot passes the identity authentication and the real-time position is within the first preset range, the rolling gate is controlled to open automatically; when the inspection robot finishes charging and leaves the second preset range, the rolling gate is controlled to close automatically.

[0015] Further, the temperature-controlled charging cabin further includes a controller; the controller is respectively connected to the identity authentication module, the adaptive temperature control platform and the charging module; The controller is used to verify the identity information of the inspection robot collected by the identity authentication module; is also used to control the adaptive temperature control platform for dynamic adaptive temperature regulation; and is used to control the charging module to charge the inspection robot that passes the identity authentication; Among them, the controller adopts an Arduino controller.

[0016] Further, the temperature-controlled charging cabin further includes a charging cabin body; the charging cabin body is arranged in a double-layer structure inside and outside, with an intermediate heat insulation layer arranged between the inner layer and the outer layer, and the intermediate heat insulation layer adopts rock wool; a hydrogen energy storage unit for converting into charging energy is arranged inside the charging cabin body; the identity authentication module is installed outside the charging cabin body; the charging module is located at the bottom of the charging cabin body and adopts a wireless charging system.

[0017] A working method of a temperature-controlled charging cabin for a cable tunnel inspection robot, based on the above-mentioned temperature-controlled charging cabin for a cable tunnel inspection robot, includes: The adaptive temperature control platform performs dynamic adaptive temperature regulation on the internal temperature of the charging cabin; The authentication module collects the identity information of the patrol robot, and the identity information of the patrol robot is used for the identity authentication of the patrol robot; The charging module charges the patrol robot that has passed the identity authentication; Among them, the adaptive temperature control platform includes: A collection unit for collecting the real-time temperature in the charging cabin; A control decision-making unit for performing variable universe fuzzy reasoning based on the collected real-time temperature in the charging cabin and optimizing the parameters of the variable universe fuzzy PID controller using a multi-population genetic algorithm; A temperature regulation execution unit for adjusting the internal temperature of the charging cabin according to the optimized parameters.

[0018] Further, the performing variable universe fuzzy reasoning based on the collected real-time temperature in the charging cabin includes: Based on the collected real-time temperature in the charging cabin, obtaining the control quantity of the charging cabin temperature; Performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy processed control quantity; Dividing the fuzzy space of the charging cabin temperature control according to the fuzzy processed control quantity; Constructing a variable universe for the charging cabin temperature control according to the fuzzy processed control quantity and the fuzzy space of the charging cabin temperature control; Using a scaling factor to convert the original universe into a variable universe, obtaining the deviation and deviation change rate of the charging cabin temperature, and completing the variable universe fuzzy reasoning; The performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy processed control quantity, and the specific formula is as follows:

[0019] In the formula, Z represents the fuzzy processed control quantity, g represents the control quantity of the charging cabin temperature, Z max 、Z min are the maximum and minimum values of the charging cabin temperature control quantity respectively; The constructing a variable universe for the charging cabin temperature control according to the fuzzy processed control quantity and the fuzzy space of the charging cabin temperature control, where the variable universe expression is as follows:

[0020]

[0021] In the formula, 、 are both universe scaling factors; 、 are the variable universes of x and y respectively; 、 Both are the initial universes of discourse; x and y are the temperature variables of the charging cabin; The parameter optimization of the variable universe fuzzy PID controller by using the multi-population genetic algorithm includes: Obtaining the initial parameters of the variable universe fuzzy PID controller by using the multi-population genetic algorithm; Based on the initial parameters of the variable universe fuzzy PID controller and the fuzzy rules of the preset fuzzy PID, using the multi-population genetic algorithm for iterative optimization, and outputting the optimal solution as the optimized parameters of the variable universe fuzzy PID controller.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a temperature-controlled charging cabin for a cable tunnel inspection robot. This charging cabin ensures the safety of the charging process through an integrated identity verification module, and at the same time, the adaptive temperature control platform significantly improves the environmental adaptability of the charging system. The platform first collects the real-time temperature inside the charging cabin, and then the control decision unit uses the variable universe fuzzy inference technology and combines the multi-population genetic algorithm to intelligently optimize the parameters of the PID controller. This principle enables the temperature control system to accurately respond to wide temperature range changes. The temperature adjustment execution unit then quickly adjusts the temperature inside the cabin according to the optimized parameters, effectively compensating for the influence of extreme temperatures on the lithium battery capacity and sensor accuracy. Using this temperature-controlled charging cabin not only improves the charging efficiency, but also extends the service life of the equipment and maintains high sensitivity, thus meeting the all-weather and high-density inspection requirements of the urban power grid and overcoming the limitations of the traditional manual battery replacement mode and the threat of extreme temperature environments to the charging system.

[0023] Preferably, in the present invention, through variable universe fuzzy inference, the temperature control system can more accurately respond to temperature changes, improving the accuracy and flexibility of temperature control. Using the scaling factor to adjust the universe of discourse enables the system to maintain good control performance in different temperature ranges, enhancing the adaptive ability of the system. Preferably, in the present invention, using the multi-population genetic algorithm to optimize the parameters of the variable universe fuzzy PID controller can automatically find the optimal control parameters, improving the control accuracy and response speed of the temperature control system. This intelligent optimization method reduces the complexity of manual debugging and improves the overall performance of the system.

[0024] Preferably, in the present invention, the application of the image recognition unit improves the accuracy and reliability of identity verification, enabling the charging cabin to only provide services for legitimate inspection robots. This enhances the security of the system and prevents unauthorized access and use. Preferably, in the present invention, the rolling gate control module automatically opens or closes the rolling gate according to the identity verification result and the real-time position, realizing the intelligent management of the charging cabin. This not only improves the convenience of use, but also enhances the security and energy saving of the system. Preferably, in the present invention, the integration of the controller enables the various modules of the charging cabin to work together, achieving intelligent control of the charging process. The application of the Arduino controller improves the flexibility and scalability of the system, enabling the charging cabin to adapt to different usage scenarios and requirements. Preferably, in the present invention, the double-layer setting of the charging cabin body and the application of the intermediate heat-insulating layer improve the heat-insulating performance of the cabin body, reducing energy loss. The application of the hydrogen energy storage unit provides a clean and efficient energy source for charging. The installation position of the identity verification module makes the operation more convenient. The present invention provides a working method for a temperature-controlled charging cabin for a cable tunnel inspection robot. Through the adaptive temperature control platform, dynamic adaptive adjustment of the internal temperature of the charging cabin is achieved, effectively solving the threat of extreme temperature environments to the charging system. The identity verification module ensures that only legitimate inspection robots can charge, improving the security of the system. The charging module works after the identity verification is passed, ensuring the accuracy and reliability of charging. The adaptive temperature control platform uses the acquisition unit to obtain temperature data in real time. The control decision unit uses variable universe fuzzy inference and multi-population genetic algorithm to optimize the parameters of the PID controller. The temperature adjustment execution unit adjusts the temperature inside the cabin according to the optimized parameters. This working principle enables the charging cabin to accurately respond to wide temperature range changes, compensating for the influence of temperature on the lithium battery capacity and sensor accuracy, improving the charging efficiency, extending the equipment life, and meeting the all-weather and high-density inspection requirements of the urban power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic structural diagram of a temperature-controlled charging cabin for a cable tunnel inspection robot provided by an embodiment of the present invention; Figure 2 It is a temperature control flow chart of a temperature-controlled charging cabin for a cable tunnel inspection robot provided by an embodiment of the present invention; Figure 3 It is a flow chart of optimizing using a multi-population genetic algorithm provided by an embodiment of the present invention; Figure 4 It is a steady-state thermal analysis flow chart of a temperature-controlled charging cabin for a cable tunnel inspection robot provided by an embodiment of the present invention; Figure 5 It is a structural diagram of the charging cabin body of a temperature-controlled charging cabin for a cable tunnel inspection robot provided by an embodiment of the present invention; Figure 6 It is a schematic diagram of the control system of a temperature-controlled charging cabin for a cable tunnel inspection robot provided by an embodiment of the present invention; Figure 7 It is a schematic structural diagram of the controller provided by an embodiment of the present invention; Figure 8Connection diagram of the AC motor drive board and the relay provided by the embodiment of the present invention; Figure 9 Schematic diagram of the connection between Arduino and OpenMV provided by the embodiment of the present invention; Figure 10 Design circuit diagram of the single-chip microcomputer provided by the embodiment of the present invention; among them, (a) is the reset circuit; (b) is the clock; (c) is the wiring terminal of the single-chip microcomputer; Figure 11 Circuit diagram of the liquid crystal display provided by the embodiment of the present invention; Figure 12 Schematic diagram of the wireless charging structure provided by the embodiment of the present invention; Figure 13 Wireless charging voltage-time curve provided by the embodiment of the present invention; Figure 14 Workflow diagram of the temperature-controlled charging cabin for the cable tunnel inspection robot provided by the embodiment of the present invention.

[0026] Reference numerals: 1. Charging cabin body; 2. Rolling shutter door. Detailed implementation manners

[0027] To further understand the content of the present invention, the following describes the present invention in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention rather than limiting it.

[0028] As described in the background art, the defects in the charging support link are specifically manifested in: First, the existing charging methods for cable tunnel inspection robots are mostly manual replacement of battery packs by full-time personnel, which requires dedicated personnel and management, and twice as many battery packs need to be configured. This method relies on the timely response of dedicated personnel and is only suitable for small-scale temporary use, seriously restricting the large-scale and wide application of tunnel cable inspection robots. Second, the current design and research of charging cabins suitable for cable tunnel robots are still blank, and the existing robot charging cabins do not consider the harm of extreme temperatures to cable inspection. Long-term use of equipment at low temperatures will lead to shortened service life, decreased sensitivity, and the battery life will also decrease sharply with the decrease of temperature, disrupting the inspection planning tasks of the robot and affecting the intelligent and autonomous inspection work of the cable. Third, the problem of online power supply for intelligent inspection robots is prominent. With the continuous research and manufacture of various types of power tunnel intelligent inspection robots such as wheeled mobile robots, tracked inspection robots, and quadruped robot dogs, a tunnel charging cabin suitable for multiple types of inspection robots has become an urgent need.

[0029] To solve the above problems, this embodiment provides a temperature-controlled charging cabin for cable tunnel inspection robots. This temperature-controlled charging cabin can solve problems such as difficult charging for existing cable tunnel inspection robots, lack of temperature control consideration in the charging cabin, and inability to be compatible with multiple types of robots, realizing automated unattended charging, intelligent temperature control environment management, and multi-type compatible charging, improving the inspection efficiency and quality of cable tunnels, and reducing operation and maintenance costs.

[0030] As Figure 1 shown, this embodiment provides a temperature-controlled charging cabin for cable tunnel inspection robots, including: An authentication module for collecting the identity information of the inspection robot; an adaptive temperature control platform for dynamically and adaptively adjusting the temperature inside the charging cabin; a charging module for charging the inspection robot that has passed the authentication. Among them, the adaptive temperature control platform includes: a collection unit for collecting the real-time temperature inside the charging cabin; a control decision-making unit for performing variable universe fuzzy reasoning based on the collected real-time temperature inside the charging cabin and optimizing the parameters of the variable universe fuzzy PID controller using a multi-population genetic algorithm; a temperature adjustment execution unit for adjusting the temperature inside the charging cabin according to the optimized parameters.

[0031] It can be seen that the temperature-controlled charging cabin for cable tunnel inspection robots provided in this embodiment ensures the safety of the charging process by integrating the authentication module. At the same time, the adaptive temperature control platform significantly improves the environmental adaptability of the charging system. This platform first collects the real-time temperature inside the charging cabin, and then the control decision-making unit uses the variable universe fuzzy reasoning technology and combines the multi-population genetic algorithm to intelligently optimize the parameters of the PID controller. This principle enables the temperature control system to accurately respond to wide temperature range changes. The temperature adjustment execution unit then quickly adjusts the temperature inside the cabin according to the optimized parameters, effectively compensating for the influence of extreme temperatures on the lithium battery capacity and sensor accuracy. Using this temperature-controlled charging cabin not only improves the charging efficiency, but also extends the service life of the equipment and maintains high sensitivity, thus meeting the all-weather and high-density inspection requirements of urban power grids and overcoming the limitations of traditional manual battery replacement mode and the threat of extreme temperature environment to the charging system.

[0032] Exemplarily, as Figure 5 shown, the temperature-controlled charging cabin provided in this embodiment includes a charging cabin body 1; the charging cabin body is divided into an inner layer and an outer layer, with a heat-insulating layer filled in the middle. Both the inner and outer shells are made of 304 stainless steel with a thermal conductivity of 16.3 W / (m·K), and the middle heat-insulating layer is made of rock wool with a thermal conductivity of 0.04 W / (m·K). This structural design can effectively reduce the heat exchange between the inside and outside of the cabin and keep the temperature inside the cabin stable. A rolling gate 2 is installed on the charging cabin body 1, which can realize the opening and closing of the charging cabin.

[0033] Inside the charging cabin body, the hydrogen energy storage unit can be converted into a 220V power supply required for device charging and a continuous 12V power supply for the robot through a voltage conversion module, enabling long-term tunnel inspection. As a clean energy source, hydrogen energy storage is not only environmentally friendly but also provides stable energy support for the charging cabin and the robot.

[0034] As Figure 6 shown, the hardware part of this control system takes the Arduino controller as the core. The host computer control system issues instructions, and the single-chip microcomputer controls each module according to the instructions.

[0035] As Figure 7 shown, the main tasks of the Arduino controller include: data processing, temperature control, information communication, etc. After the power-on initialization of each module is completed, the OpenMV module recognizes the QR code on the robot. While the single-chip microcomputer transmits the data information back to the host computer, it adjusts the forward and reverse rotation of the motor inside the rolling gate according to the instructions of the host computer to complete the opening and closing. The DHT22 module measures the temperature inside the temperature-controlled charging cabin. When the temperature is lower than the preset value, it transmits the data information to the electric tracing heating module through the single-chip microcomputer to start heating, and stops heating when the temperature reaches the predicted value.

[0036] As Figure 8 and Figure 9 shown, the host computer control system consists of a PC. The PC is installed with host computer software for monitoring various data of the temperature-controlled charging cabin and controlling the movement of the tunnel inspection robot. Through the software, it can receive the data transmitted back by the temperature-controlled charging cabin and send instructions to control the movement of the robot. It can display the pictures taken by the OpenMV in real time on the software interface and also display the navigation trajectory of the inspection robot. Among them, the design of the single-chip microcomputer is specifically shown in Figure 10 as shown in (a), (b), and (c) therein.

[0037] The identity verification module includes an image recognition unit for collecting the identity information and real-time location of the inspection robot; the identity information of the inspection robot is used to match with a preset identity library. If the match is successful, it means that the inspection robot has passed the identity verification.

[0038] The image recognition unit uses an OpenMV camera, which is installed outside the charging cabin body. It controls the opening and closing of the rolling gate by recognizing the QR code on the robot. The main control chip of OpenMV is STM32F7, with a main frequency of 216MHZ, a frame rate of up to 85 - 90 frames, a photosensitive element of OV7725, a resolution of 640*480, and a working temperature range of -40°C - 125°C. It can work stably in the harsh environment of the cable tunnel. It has a built-in QR code recognition algorithm, adopts the four-element detection algorithm used on Apriltag, and corrects barrel distortion through the built-in algorithm lens_corr(). OpenMV is compatible with 3.3V / 5V levels, occupies a UART serial port to communicate with the single-chip microcomputer, has eight data format statements, and transmits the recognized current QR code image information to the single-chip microcomputer for processing. The advantages of using QR code recognition include extremely high accuracy, high privacy, strong security, low cost, etc. It has a significant effect in the common low-temperature and harsh environment in winter in the cable tunnel.

[0039] The rolling gate is controlled by a DM35S AC motor. The rated torque of the DM35S AC motor is 3 N·M, the output speed is 28rpm, and the rated power is 98 W. The single-chip microcomputer controls the opening and closing of the rolling gate by accessing the TKS-M2A AC motor drive board and the relay, realizing the control of 220 V strong electricity with 5 V weak electricity. After receiving the signal, the single-chip microcomputer transmits the opening signal to the rolling gate.

[0040] In this embodiment, in order to overcome the challenges such as high complexity of parameter adjustment and insufficient adaptability faced by the traditional PID temperature control system when adjusting the temperature of the charging cabin, a dynamic adaptive temperature control platform is designed. The purpose of this platform is to ensure that the inspection robot parked in it can charge and operate in a stable and suitable temperature environment, thereby significantly improving the overall efficiency and reliability of the system.

[0041] Exemplarily, the adaptive temperature control platform specifically adopts the following design: As Figure 2 shown, a BS-RWB high-precision temperature transmitter is used to accurately monitor the real-time temperature inside the charging cabin, and the data is transmitted to the control system through the EM235 analog input and output module. The system dynamically constructs a variable universe according to the temperature of the charging cabin, intelligently optimizes the variable universe using a multi-population genetic algorithm, performs fuzzy reasoning under fuzzy control logic, outputs control parameters such as proportional, differential, and integral, and after adjustment, sends them to the PID controller to achieve precise control of the temperature of the charging cabin and form a closed-loop temperature control system.

[0042] Under the fuzzy control logic, the system performs fuzzy inference on the optimized variable universe and outputs control parameters such as proportional, derivative, and integral according to preset rules. These parameters are finely adjusted according to the set initial value of the charging cabin temperature to ensure the accuracy and stability of temperature control. Finally, the optimized PID control parameters are sent to a professional PID controller to achieve precise regulation of the charging cabin temperature. As Figure 6 shown, it is the hardware structure of the charging cabin temperature control system, which integrates sensors, intelligent algorithm processing units, and efficient controllers, jointly constituting a closed-loop temperature control system to ensure that the temperature inside the charging cabin is always maintained within the ideal range and guarantee the safety and efficiency of the charging process.

[0043] For the control module of the charging cabin temperature control system, a fuzzy PID controller is adopted, and the inputs are the temperature deviation tem and the temperature error change rate tc. This control strategy combines the good following effect of PID control with the excellent anti-interference ability of fuzzy control, so as to achieve fast and stable control of the system.

[0044] Fuzzification processing is carried out on the actual temperature quantity: the temperature variable of the charging cabin is scaled and transformed into the variable universe interval. Linear scaling is adopted. Let the temperature control quantity of the charging cabin be g, and the change interval be [Zmax, Zmin]. The expression of the transformed value Z is:

[0045] In the formula, Z represents the control quantity after fuzzification processing, g represents the temperature control quantity of the charging cabin, Z max 、Z min are the maximum and minimum values of the temperature control quantity of the charging cabin respectively.

[0046] According to the above formula, the scale transformation result of the temperature control quantity of the charging cabin can be obtained. Based on this result, the fuzzy space of the temperature control of the charging cabin is divided. Suppose the factors of the temperature deviation control of the charging cabin are composed of 5 elements, namely positive large, positive small, 0, negative small, and negative large. The above 5 elements are described in a simple way, represented by {PL, PS, ZE, NS, NL} respectively. The fuzzy space segmentation result of the scale variable of the temperature control quantity of the charging cabin is shown in Table 1.

[0047] Table 1 Fuzzy space division result of the scale variable of the temperature control quantity

[0048] After the fuzzification of tem and tc and the division of the fuzzy space of the temperature control quantity, under the condition that the rules and forms remain constant, the variable universe of the charging cabin temperature control will be dynamically adjusted according to the temperature error. At this time, the input and output of the variable universe directly correspond to the current temperature value and need to be expanded and contracted in a timely manner according to the temperature control requirements. Therefore, when the variable universe fuzzy PID controller controls the charging cabin, it only follows the rules of the general trend. Let x and y be the temperature variables of the charging cabin, then the variable universe expression is:

[0049]

[0050] In the formula, 、 are both universe scaling factors; 、 are the variable universes of x and y respectively; 、 are both initial universes; x and y are the temperature variables of the charging cabin.

[0051] Assume that the fuzzy rule of the charging cabin temperature variable x is 7 gears under the initial universe , represented by [NB, NM, NS, ZE, PS, PM, PB], then the original universe can be converted into the variable universe through the scaling factor. Through the scaling of the variable universe, the deviation and the rate of change of the deviation of the charging cabin temperature can be obtained and used as the input of the PID controller.

[0052] In the process of using the PID controller to regulate the charging cabin temperature, to achieve precise control according to the fuzzy rules, it is necessary to have good completeness, consistency and cross-over to ensure the control effect. Given that both the charging cabin temperature control scale variable and the fuzzy control rule language value are 5, the fuzzy control rule expression of the PID controller is

[0053] In the formula: is the charging cabin temperature control scale variable; is the language value of the fuzzy control rule; is the k-th fuzzy control rule. Then the corresponding relationship expression between the fuzzy relationship and the control statement of the PID control of the charging cabin temperature is

[0054] In the formula: The fuzzy relationship for the fuzzy control of the charging compartment. The fuzzy control rules for the charging compartment temperature are presented in tabular form. Based on the fuzzy control rules for the charging compartment temperature, after calculating them using the parallel operation principle, the fuzzy control rules for the entire PID control of the charging compartment temperature can be obtained. For the fuzzy control rules of the PID control of the charging compartment temperature, the expression of the rule output result during its control process is

[0055] In the formula: is the composition operator; 、 are the fuzzy rule language values of the fuzzy control of the charging compartment box temperature respectively. After obtaining the fuzzy control rules for the PID control of the charging compartment temperature, the key lies in converting the fuzzy quantity into an actual operable quantity to accurately regulate the temperature. The weighted average method is used to achieve this conversion, so as to ensure that the actual quantity output by the fuzzy PID controller can accurately and effectively control the charging compartment temperature.

[0056] After the variable universe fuzzy PID controller receives the charging compartment temperature deviation and deviation change rate, it adjusts the values of the temperature deviation and deviation change rate through the change of the variable universe scaling factor, and then obtains the proportional, integral and differential parameters of the PID controller. Although fuzzy PID control has the advantages of strong robustness and strong ability to handle nonlinear systems, its computational complexity is high, parameter adjustment is difficult, and interpretability is poor. In practical applications, it is often difficult to obtain ideal control effects through empirical methods and trial-and-error methods. And the multi-population genetic algorithm, as an optimization algorithm that simulates the natural evolution process, has extremely strong global search ability and adaptability. Therefore, using the multi-population genetic algorithm to optimize the fuzzy PID parameters is an efficient and feasible approach.

[0057] Such as Figure 3 shown, there are many optimization object numbers that can be selected for the fuzzy PID controller, so there are also various optimization strategies. The segmented optimization strategy is selected: First, use the multi-population genetic algorithm to optimize the traditional PID controller to find the optimal 、 as the initial parameter values of the fuzzy PID controller; Second, on the premise of determining the fuzzy rules and membership function parameters of the fuzzy PID, use the multi-population genetic algorithm again to find the best control quantity proportionality factor, and the optimization process is as shown in Figure 3.

[0058] In order to evaluate the advantages and disadvantages of each individual, it is necessary to calculate its error integral index and fitness. The error integral index adopts the integral performance index of the product of the absolute value of the error and time, and its function is:[[]]

[0059] Where τ is the sampling time and tem(τ) is the temperature error. The optimization objective is to minimize the integral of the temperature error. During the genetic iteration process, the higher the fitness of an individual, the higher its survival rate. Therefore, a fitness function Fit{J(i)} is constructed:

[0060] Where c is a conservative estimate of the boundary of the objective function, c > 0 and c + J(i) > 0. The termination condition of the least number of generations for maintaining the optimal individual is adopted. That is, after each iteration, the optimal individuals of each population are compared to find the optimal individual of the entire population. After multiple iterations, if the optimal individual of the entire population remains unchanged, the iteration is determined to terminate. After the iteration terminates, the algorithm obtains the gene of the optimal individual and outputs the optimal solution through decoding. The scaling factors of the charging cabin temperature deviation and deviation change rate, and the proportional, integral, and differential parameters of the PID controller are respectively used as the optimization objectives to obtain the best parameter values to reduce the overshoot of its temperature control.

[0061] Exemplarily, in this embodiment, the charging module adopts a wireless charging system; the specific design is as follows: As Figure 12 shown, in order to enable the long-term scientific research work of the inspection robot, a wireless charging system is proposed to provide power for it. The key point of this system is to provide an efficient, reliable, and convenient lithium battery charging method in the charging cabin. The charging method of the power lithium battery of the inspection robot generally adopts "constant current first and then constant voltage" charging, that is, the system first enters the constant current charging mode. In this stage, the battery voltage continuously increases. When the battery voltage reaches a rated value, the system is switched to the constant voltage charging mode. In this stage, the current flowing through the battery continuously decreases until the battery is fully charged and the charging ends. This system consists of a 24VDC power supply, a transmitting module, a receiving module (wireless coil), and a digital display current and voltage meter.

[0062] The transmitting end is connected to a 24V / 2A power adapter. The transmitting module mainly consists of three parts: a power amplification circuit, an oscillation circuit, and a coupling coil. It is equipped with a TX-24VHS wireless charging module, and the LM358 operational amplifier can output signals to the load. The oscillation circuit adopts an NE555 to form a frequency-adjustable multivibrator, and the NE555 chip outputs a PWM wave to drive the switching of the MOS switch. The design of the receiving circuit must consider the voltage stability limit for charging the lithium battery and the lithium battery charging protection problem, mainly to prevent excessive current and overcharging during charging. The 292304-1 type lithium battery charging management chip is selected. The receiving end uses an integrated MBRD-10100CT rectifier diode to output 12.6V, and a digital display voltage and ammeter is connected in series to display the charging current and voltage of the current lithium battery. The control board also integrates an indicator light. The red light is always on during charging, the blue light is on when the battery level reaches 95%, and it automatically disconnects after being fully charged. The circuit of the liquid crystal display is specifically as Figure 11 shown.

[0063] In actual tests, the lithium battery voltage readings on the digital display voltage and ammeter are observed every 30 minutes, and the obtained test points are plotted into a relatively smooth curve. The final lithium battery wireless charging voltage-time curve graph is as Figure 13 shown. After testing, the effective distance of the wireless charging of this wireless charging module reaches 2.5 cm. At the same time, both the transmitting module and the receiving module have circuit protection functions, and the receiving module also has a charging protection function, thus ensuring the safety of the circuit. The specific wireless charging system parameters are shown in Table 2.

[0064] Table 2 shows the parameters of the wireless charging system

[0065] Exemplarily, the following improvements are made to the software of this temperature-controlled charging cabin: In order to monitor the internal data of the temperature-controlled charging cabin and the images collected by the OpenMV camera in real time, upper computer software is designed. The upper computer software is Arduino IDE and OpenMV IDE. OpenMV IDE is written by Qt Creator and can be used across platforms. It is programmed in the Python language.

[0066] On the left side of the software interface IDE are common document editing operations and a code editing area, which continues the functions of Qt, such as code highlighting and auto-completion. In the upper right corner of the interface is an image area and a frame buffer viewer for the images of the OpenMV lens, which can be saved or captured. In the lower right corner of the interface is the three-color RGB histogram of the image, which can perform image operations such as feature extraction and threshold processing. The window in the upper right of the software shows the situation captured by the current camera. The information after identifying the QR code and the instructions and responses for communicating with Arduino will be displayed on the serial terminal interface in the lower left.

[0067] Exemplarily, in order to verify the performance of the temperature-controlled charging cabin for the cable tunnel inspection robot provided in this embodiment, a field verification is now carried out on this temperature-controlled charging cabin, and the specific process is as follows: In order to study the heat transfer and heat distribution inside and outside the temperature-controlled charging cabin in the Antarctic environment, so as to realize the temperature prediction of the temperature-controlled charging cabin in the future and better control the temperature of the temperature-controlled charging cabin. Using ANSYS software, a thermal model is established, combined with parameters such as the on-site temperature measured by the intelligent underground pipe gallery of a certain city's cable operation and maintenance center, to simulate the temperature change of the inside of the temperature-controlled charging cabin in the underground cable pipe gallery environment, and monitor whether the internal equipment of the temperature-controlled charging cabin can work normally in this environment, so as to establish a system for predicting the internal temperature environment of the temperature-controlled charging cabin and ensure the normal use and endurance of the inspection robot. The steady-state thermal analysis of the new energy temperature-controlled charging cabin using ANSYS is roughly divided into three steps: preprocessing, solving, and postprocessing. The basic steps are as Figure 4 shown. The finite element model is obtained by importing the model built by SOLIDWORKS into ANSYS, and the definition and assignment of materials, mesh generation, application of loads and boundary conditions, solution, and evaluation of results are carried out. After the boundary conditions and load conditions are set, ANSYS can enter the calculation process. After the software calculation is completed, the areas with the same temperature will be automatically represented by the same color and displayed on the model, that is, the contour plot. Through the contour plot, the temperature distribution in the model can be intuitively viewed. It can be seen from the simulation that the internal temperature of the temperature-controlled charging cabin remains at about 25°C, which can ensure the normal operation of the internal equipment and can provide a normal operating temperature for the inspection robot's sensor equipment, meeting the design objectives of the temperature-controlled charging cabin. The thermal insulation material used in the temperature-controlled charging cabin is rock wool. It can be seen that the temperature difference between the thermal insulation layer and the outer shell is 18.45°C, and the thermal insulation performance is good. The temperature of the rolling gate is close to the external environment temperature, and the temperature difference is 24.98°C, indicating that the thermal insulation performance of the stainless steel material of the rolling gate is far inferior to that of rock wool. In order to improve the thermal insulation performance of the temperature-controlled charging cabin, a layer of rock wool needs to be added to the rolling gate.

[0068] Before deploying the temperature-controlled charging cabin and robot, you need to check the status of the equipment. First, ensure the connection between the equipment connection and the control panel interface, then check the current temperature and other data in the host computer software, and check whether the patrol robot, rolling shutter, OpenMV camera and electric heating cable can receive the host computer instructions and execute them correctly.

[0069] In order to verify the working performance of the temperature-controlled charging cabin in the cold winter conditions of the underground tunnel, performance test experiments were carried out, such as Figure 14 As shown in the figure, 120 minutes after the experiment started, the inspection robot was controlled to move to the front of the new energy temperature-controlled charging cabin. The OpenMV camera above the house recognized the QR code on the robot, the shutter door opened, the robot automatically entered the temperature-controlled charging cabin and recognized the location of the wireless charging pile, and moved to the designated location to start charging. After waiting for the robot to enter, the shutter door closed. The host computer continued to detect temperature changes. Since the shutter door was opened for a period of time, the temperature inside the temperature-controlled charging cabin dropped. The DHT22 sensor detected the temperature change, the electric heating belt started to heat, and the internal temperature rose. When it rose to the specified temperature range, it stopped heating. After a period of time, the robot was fully charged, the wireless charging pile automatically disconnected the charging, and the inspection robot drove away from the temperature-controlled charging cabin to complete the experiment. During the experiment, the host computer software monitored the temperature and humidity inside the temperature-controlled charging cabin in real time, and the RVIZ software built into the host computer ROS was used to view the current speed and movement of the robot and the current power of the robot. In order to verify the reliability of the temperature control system, the measured temperatures are shown in the table. The experimental results obtained are in good agreement with the simulated temperature distribution, as shown in Table 3.

[0070] Table 3 Internal temperature of the uncontrolled charging compartment

[0071] After the experiment was completed, the devices and sensors in the insulation room were recovered and inspected. The Openmv camera and AC motor were able to operate normally and in good condition during the experiment. Field experiments showed that the robot completed the charging function according to instructions inside the new energy temperature-controlled charging cabin. The temperature-controlled charging cabin completed the temperature regulation function and controlled the opening and closing of the rolling shutter door, and can work stably for a long time.

[0072] Integrate an intelligent temperature control environment management system inside the charging cabin. This system can monitor the temperature inside and outside the cabin in real time and automatically adjust the temperature inside the cabin according to the preset temperature range to ensure that the robot charges and standby in the most suitable working environment. At the same time, adopt high-efficiency thermal insulation materials and energy-saving technologies to reduce energy consumption. First, long equipment life: The stable temperature environment effectively extends the service life of the robot and its battery, reducing performance degradation and failure rates caused by temperature fluctuations. Second, improve battery performance: At suitable temperatures, the battery charging efficiency and endurance time are significantly improved, ensuring that the robot can complete longer inspection tasks. Third, enhance the reliability of inspection: Even under extreme weather conditions, it can ensure the stable operation of the robot, unaffected by temperature, improving the accuracy and reliability of inspection.

[0073] Exemplarily, the temperature-controlled charging cabin of this embodiment also adopts multi-type compatible charging interfaces and modular design. It uses wireless charging and can be compatible with inspection robots of different brands and models, including wheeled, tracked, quadruped robot dogs, etc. At the same time, the modular design facilitates functional expansion and upgrade according to actual needs. First, wide applicability: Support the charging of multi-type inspection robots to meet the needs of different tunnel inspection scenarios. Second, the unified charging cabin design reduces the cost of purchasing multiple charging devices due to different robot models.

[0074] In summary, the present invention provides a temperature-controlled charging cabin for a cable tunnel inspection robot, which has the following advantages compared with the prior art: This temperature-controlled charging cabin designs the system's software and hardware based on Arduino. The new energy charging cabin uses clean energy stored in hydrogen tanks as the power source. The inspection robot realizes charging inside the charging and heat preservation room through wireless charging and can identify two-dimensional codes and control the opening and closing of the rolling gate. An automated unattended charging system is formed, and no manual intervention is required during the wireless charging process. At the same time, the charging plan is automatically optimized according to the robot's battery power, inspection task priority, and charging cabin status; adopting this temperature-controlled charging cabin not only improves the inspection efficiency and quality but also reduces the operation and maintenance costs, providing strong support for the intelligent inspection of cable tunnels. First, improve inspection efficiency and continuity: Automated charging reduces manual intervention, ensuring that the robot can seamlessly execute inspection tasks, improving inspection efficiency and continuity. Second, reduce operation and maintenance costs: There is no need for full-time personnel to manually replace the battery, reducing labor costs and management complexity. Third, adapt to large-scale applications: Support the simultaneous charging of multi-type inspection robots to meet the needs of large-scale tunnel inspections. Most importantly, even under extreme weather conditions, it can ensure the stable operation of the robot, unaffected by temperature, improving the accuracy and reliability of inspection.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific embodiments of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A temperature-controlled charging cabin for a cable tunnel inspection robot, characterized in that, Including: An authentication module for collecting the identity information of the inspection robot; An adaptive temperature control platform for dynamically and adaptively adjusting the temperature inside the charging cabin; A charging module for charging the inspection robot that has passed the identity authentication; Wherein, the adaptive temperature control platform includes: A collection unit for collecting the real-time temperature inside the charging cabin; A control decision-making unit for performing variable universe fuzzy reasoning based on the collected real-time temperature inside the charging cabin, and optimizing the parameters of the variable universe fuzzy PID controller using a multi-population genetic algorithm; A temperature regulation execution unit for adjusting the temperature inside the charging cabin according to the optimized parameters.

2. The temperature-controlled charging cabin for the cable tunnel inspection robot according to claim 1, characterized in that, The performing variable universe fuzzy reasoning based on the collected real-time temperature inside the charging cabin includes: Obtaining the control quantity of the charging cabin temperature based on the collected real-time temperature inside the charging cabin; Performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy-processed control quantity; Dividing the fuzzy space of the charging cabin temperature control according to the fuzzy-processed control quantity; Constructing a variable universe for the charging cabin temperature control according to the fuzzy-processed control quantity and the fuzzy space of the charging cabin temperature control; Converting the original universe into a variable universe using a scaling factor to obtain the deviation and the rate of change of the deviation of the charging cabin temperature, and completing the variable universe fuzzy reasoning.

3. The temperature-controlled charging cabin for the cable tunnel inspection robot according to claim 2, wherein The performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy-processed control quantity, and the specific formula is as follows: Wherein, Z represents the controlled quantity after fuzzification processing, g represents the controlled quantity of the charging cabin temperature, Z max and Z min are respectively the maximum value and the minimum value of the controlled quantity of the charging cabin temperature; The constructing a variable universe for the charging cabin temperature control according to the fuzzy-processed control quantity and the fuzzy space of the charging cabin temperature control, wherein the variable universe is expressed as follows: In the formula, and are both domain scaling factors; and are the variable domains of x and y respectively; and are both initial domains; x and y are the temperature variables of the charging compartment.

4. The temperature-controlled charging cabin for the cable tunnel inspection robot according to claim 2, wherein, The optimizing the parameters of the variable universe fuzzy PID controller using a multi-population genetic algorithm includes: Obtaining the initial parameters of the variable universe fuzzy PID controller using a multi-population genetic algorithm; Based on the initial parameters of the variable universe fuzzy PID controller and the fuzzy rules of the preset fuzzy PID, performing iterative optimization using a multi-population genetic algorithm, and outputting the optimal solution as the optimized parameters of the variable universe fuzzy PID controller.

5. The temperature-controlled charging cabin for the cable tunnel inspection robot according to claim 1, characterized in that, The authentication module includes an image recognition unit; The image recognition unit is used for collecting the identity information and the real-time position of the inspection robot; the identity information of the inspection robot is used for matching with a preset identity database, and if the match is successful, it means that the inspection robot has passed the identity authentication.

6. The temperature-controlled charging cabin for the cable tunnel inspection robot according to claim 5, wherein, The temperature-controlled charging cabin further includes a rolling gate control module; The rolling gate control module is used for opening the rolling gate of the temperature-controlled charging cabin according to the identity authentication result and the real-time position of the inspection robot; wherein, when the inspection robot passes the identity authentication and the real-time position is within the first preset range, the rolling gate is controlled to open automatically; when the inspection robot finishes charging and leaves the second preset range, the rolling gate is controlled to close automatically.

7. The temperature-controlled charging cabin for the cable tunnel inspection robot according to claim 1, characterized in that, The temperature-controlled charging cabin further includes a controller; the controller is respectively connected to the authentication module, the adaptive temperature control platform, and the charging module; The controller is used for verifying the identity information of the inspection robot collected by the authentication module; is also used for controlling the adaptive temperature control platform to perform dynamic and adaptive temperature regulation; and is used for controlling the charging module to charge the inspection robot that has passed the identity authentication. Among them, the controller adopts an Arduino controller.

8. The temperature-controlled charging cabin for the cable tunnel inspection robot according to claim 1, characterized in that, The temperature-controlled charging cabin further includes a charging cabin body; the charging cabin body is arranged in a double-layer structure inside and outside, with an intermediate heat-insulating layer arranged between the inner layer and the outer layer, and the intermediate heat-insulating layer adopts rock wool; a hydrogen energy storage unit for converting into charging energy is arranged inside the charging cabin body; the identity verification module is installed outside the charging cabin body; the charging module is located at the bottom of the charging cabin body and adopts a wireless charging system.

9. A working method of a temperature-controlled charging cabin for a cable tunnel inspection robot, based on the temperature-controlled charging cabin for a cable tunnel inspection robot according to any one of claims 1-8, characterized in that, Including: The adaptive temperature control platform dynamically and adaptively adjusts the temperature inside the charging cabin. The identity verification module collects the identity information of the inspection robot, and the identity information of the inspection robot is used for verifying the identity of the inspection robot. The charging module charges the inspection robot that has passed the identity verification. Among them, the adaptive temperature control platform includes: A collection unit for collecting the real-time temperature inside the charging cabin. A control decision-making unit for performing variable universe fuzzy reasoning based on the collected real-time temperature inside the charging cabin, and optimizing the parameters of the variable universe fuzzy PID controller by using a multi-population genetic algorithm. A temperature adjustment execution unit for adjusting the temperature inside the charging cabin according to the optimized parameters.

10. The on-line monitoring method for partial discharge of transformer bushings according to claim 9, characterized in that, The performing variable universe fuzzy reasoning based on the collected real-time temperature inside the charging cabin includes: Based on the collected real-time temperature inside the charging cabin, obtaining the control quantity of the charging cabin temperature. Performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy-processed control quantity. Dividing the fuzzy space of the charging cabin temperature control according to the fuzzy-processed control quantity. Constructing a variable universe of the charging cabin temperature control according to the fuzzy-processed control quantity and the fuzzy space of the charging cabin temperature control. Using a scaling factor to convert the original universe into a variable universe, obtaining the deviation and deviation change rate of the charging cabin temperature, and completing the variable universe fuzzy reasoning. The performing fuzzy processing on the control quantity of the charging cabin temperature to obtain the fuzzy-processed control quantity, and the specific formula is as follows: Wherein, Z represents the controlled quantity after fuzzification processing, g represents the controlled quantity of the charging cabin temperature, Z max and Z min are respectively the maximum value and the minimum value of the controlled quantity of the charging cabin temperature; The constructing a variable universe of the charging cabin temperature control according to the fuzzy-processed control quantity and the fuzzy space of the charging cabin temperature control, where the variable universe is expressed as follows: Wherein, and are both domain scaling factors; and are the variable domains of x and y respectively; and are both initial domains; x and y are the temperature variables of the charging compartment; The optimizing the parameters of the variable universe fuzzy PID controller by using a multi-population genetic algorithm includes: Using a multi-population genetic algorithm to obtain the initial parameters of the variable universe fuzzy PID controller. Based on the initial parameters of the variable universe fuzzy PID controller and the preset fuzzy rules of the fuzzy PID, using a multi-population genetic algorithm for iterative optimization, and outputting the optimal solution as the optimized parameters of the variable universe fuzzy PID controller.

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