Intelligent monitoring method and system for temperature of insulating working bucket

Abstract

The invention discloses an intelligent monitoring method and system for the temperature of an insulating working bucket, and relates to the technical field of temperature control and safety monitoring of the insulating working bucket, and the method comprises the steps: S1, collecting the temperature data of the interior and exterior of the insulating working bucket, S2, judging whether an insulating material is in a brittle risk state or not based on the temperature data, and S3, if the insulating material is in the brittle risk state, judging whether the insulating material is in the brittle risk state. S4, the heating effect is monitored in real time, and working parameters of the heating system are fed back and adjusted; according to the intelligent monitoring method and system for the temperature of the insulating working bucket, the embrittlement phenomena of cracking, damage and the like of the insulating material in a low-temperature environment are effectively avoided, potential safety hazards are reduced, the safety and the operation stability of power maintenance operators in outdoor operation in severe cold areas or winter are ensured, and the working efficiency is improved. The problem that in the prior art, the operation mode of a heating system is adjusted in a low-temperature environment to solve potential safety hazards caused by embrittlement of an insulating material is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control and safety monitoring of an insulating working bucket, and in particular to an intelligent monitoring method and system for the temperature of an insulating working bucket. Background Art

[0002] Insulated work buckets are widely used in live work in power maintenance operations to ensure the safety of workers. However, in low-temperature environments, the insulation materials of insulated work buckets are prone to performance degradation, especially the problem of brittle insulation materials, which leads to a decrease in insulation strength and easily causes safety hazards. For example, when the temperature is too low, the insulation material becomes brittle, and cracks or damage may occur, making it impossible to effectively provide insulation protection. This situation is particularly obvious in cold areas or outdoor operations in winter, which seriously threatens the personal safety and operation stability of power workers.

[0003] In the prior art, the temperature of the insulation bucket is usually maintained by adding a simple heating system to the insulation working bucket. However, these systems often adopt a fixed operating mode and lack intelligent adjustment functions. They cannot dynamically adjust the heating intensity and operating mode according to the actual temperature environment and the state of the insulation material. This not only leads to energy waste, but also may not effectively prevent the embrittlement of the insulation material in extremely low temperature environments. In addition, some heating systems fail to monitor the temperature distribution in real time, resulting in local temperatures that are too low or too high, further affecting the performance of the insulation material. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent monitoring method and system for the temperature of an insulating working bucket, so as to solve the problem of how to adjust the operation mode of the heating system in a low temperature environment to solve the safety hazard caused by the brittleness of the insulating material in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for intelligently monitoring the temperature of an insulating working bucket, the method comprising:

[0006] S1. Collect temperature data inside and outside the insulating working bucket;

[0007] S2. Based on the temperature data, determine whether the insulating material is at risk of becoming brittle;

[0008] S3. If the insulation material is at risk of becoming brittle, adjust the operation mode of the heating system to increase the temperature of the insulation material, including calculating the temperature difference between the target temperature and the current temperature. The specific formula is: ΔT = T target -T geo ;

[0009] Where ΔT represents the temperature difference, T target Indicates the target temperature, T geo Indicates the current temperature;

[0010] The power output of the heating system is adjusted according to the temperature difference. The specific formula is:

[0011] P = η × ΔT;

[0012] Where P represents the output power of the heating system, η represents the heating efficiency coefficient, and ΔT represents the temperature difference;

[0013] S4. Monitor the heating effect in real time and provide feedback to adjust the working parameters of the heating system.

[0014] Preferably, the S1 comprises:

[0015] The temperature sensors inside and outside the insulated working bucket collect data and calculate the comprehensive temperature data. The specific formula is:

[0016] Among them, T geo Indicates the current temperature, T1, T2, ..., T n represents temperature data, and n represents the number of sampled temperatures.

[0017] Preferably, S2 includes:

[0018] The embrittlement risk of the insulation material and temperature are modeled as follows:

[0019]

[0020] Among them, R represents the embrittlement risk value, T geo Indicates the current temperature, T critical It represents the critical brittle temperature of the material, e represents the base of the natural logarithm, and k represents the temperature sensitivity coefficient of the material.

[0021] Preferably, the embrittlement risk value R in S2 is determined to be an embrittlement risk state if R>0.5;

[0022] If R≤0.5, it is judged as a risk-free state; the risk judgment result is output, and if there is embrittlement risk, it enters step S3.

[0023] Preferably, S4 includes:

[0024] Collect the temperature change data and corresponding time during the operation of the heating system, build the temperature change trend, and fit the relationship between temperature and time. The specific formula is:

[0025] T t =αt+β;

[0026] Among them, T t represents the temperature at a certain moment, t represents the time, α represents the temperature change rate, and β represents the initial temperature.

[0027] Preferably, S2 also includes determining the minimum temperature inside and outside the working bucket based on the data of the temperature sensor, comparing the minimum temperature with a preset safety threshold, and if the minimum temperature is lower than the preset safety threshold, determining that the insulating material is at risk of becoming brittle, and if the minimum temperature is not lower than the safety threshold, continuing to collect temperature data and re-judging in subsequent steps.

[0028] Preferably, determining the lowest temperature inside and outside the working bucket includes acquiring all temperature data from multiple temperature sensors, sorting all temperature data, selecting a minimum value, and determining the lowest temperature in the current environment based on the minimum value.

[0029] Preferably, determining the lowest temperature in the current environment based on the minimum value includes recording the lowest temperature data collected each time, comparing the current lowest temperature with the lowest temperature collected last time, and judging the temperature change trend based on the two collection results. If the current lowest temperature is lower than the previous lowest temperature, the lowest temperature record is updated to the current lowest temperature.

[0030] Preferably, the calculation formula for the temperature change rate α in S4 is:

[0031] α=(T 后 -T 前 ) / (t 后 -t 前 );

[0032] Where α represents the rate of temperature change, T 前 and T 后 Represents the temperature value at two different time points, t 前 and t 后 They represent the corresponding time points respectively.

[0033] An insulated working bucket temperature intelligent monitoring system is used to implement the steps of the insulated working bucket temperature intelligent monitoring method, the system comprising:

[0034] Temperature acquisition module, used to collect temperature data inside and outside the insulation working bucket;

[0035] A judgment module connected to the temperature acquisition module is used to judge whether the insulating material is in a state of risk of becoming brittle based on the temperature data;

[0036] A heating control module connected to the judgment module is used to adjust the operation mode of the heating system to increase the temperature of the insulating material when it is judged that the insulating material is in a risk state of becoming brittle;

[0037] The feedback adjustment module connected to the heating control module is used to monitor the heating effect in real time and provide feedback to adjust the working parameters of the heating system.

[0038] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0039] The method and system for intelligently monitoring the temperature of an insulating working bucket collects temperature data inside and outside the insulating working bucket, and based on the temperature data, determines whether the insulating material is in a state of risk of becoming brittle. If it is in a state of risk of becoming brittle, the operation mode of the heating system is adjusted to increase the temperature of the insulating material, the heating effect is monitored in real time, and the working parameters of the heating system are adjusted by feedback, so as to ensure that abnormal temperature conditions can be quickly and accurately identified, and targeted adjustments can be made to prevent the brittleness of the insulating material. The operation mode of the heating system can be dynamically adjusted according to the actual temperature environment to achieve intelligent adjustment of heating power and intensity, thereby avoiding the energy waste problem caused by the traditional fixed operation mode and improving the energy utilization efficiency. The working parameters of the heating system can be dynamically optimized based on the temperature change trend to form a closed-loop control to ensure that the temperature of the insulating material is always kept within a safe range, further improving the accuracy and stability of temperature control, ensuring the insulation strength and service life of the insulating working bucket in a low temperature environment, effectively avoiding the brittleness phenomenon of the insulating material such as cracks and breakage in a low temperature environment, reducing safety hazards, ensuring the safety and operation stability of power maintenance operators when working outdoors in cold areas or in winter, and solving the problem of how to adjust the operation mode of the heating system in a low temperature environment in the prior art to solve the safety hazards caused by the brittleness of the insulating material. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of the method of the present invention;

[0041] Figure 2 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 and Figure 2 As shown, the present invention provides a technical solution: a method for intelligently monitoring the temperature of an insulating working bucket, the method comprising:

[0044] S1. Collect temperature data inside and outside the insulating working bucket;

[0045] S2. Based on the temperature data, determine whether the insulating material is at risk of becoming brittle;

[0046] S3. If the insulation material is at risk of becoming brittle, adjust the operation mode of the heating system to increase the temperature of the insulation material, including calculating the temperature difference between the target temperature and the current temperature. The specific formula is: ΔT = T target -T geo ;

[0047] Where ΔT represents the temperature difference, T target Indicates the target temperature, T geo Indicates the current temperature;

[0048] The power output of the heating system is adjusted according to the temperature difference. The specific formula is:

[0049] P = η × ΔT;

[0050] Where P represents the output power of the heating system, η represents the heating efficiency coefficient, and ΔT represents the temperature difference;

[0051] S4. Monitor the heating effect in real time and provide feedback to adjust the working parameters of the heating system.

[0052] In this embodiment, the temperature sensor collects the temperature data inside and outside the insulating work bucket in real time to form a data input source. In step S2, by analyzing the temperature data, it is determined whether the temperature of the insulating material is close to or enters the brittle risk range, thereby evaluating the state of the insulating material. In order to prevent the insulating material from becoming brittle due to low temperature and affecting the work safety, step S3 increases the temperature by adjusting the operating mode of the heating system, specifically according to the temperature difference between the target temperature and the current temperature (ΔT = T target -T geo ), dynamically calculate the required heating power P, the formula is P = η × ΔT, where η is the system heating efficiency coefficient, reflecting the performance of the heating system. The heating system outputs according to the calculated power to narrow the gap between the current temperature and the target temperature. In step S4, the system monitors the heating effect in real time, and dynamically adjusts the working parameters of the heating system through a feedback mechanism to ensure that the heating effect meets the predetermined target temperature range, thereby ensuring the safety and reliability of the insulating material. By collecting temperature data in real time and performing monitoring and analysis, it is possible to accurately determine whether the insulating material is at risk of becoming brittle. Based on the temperature difference and the heating efficiency coefficient, the power output of the heating system is dynamically adjusted to improve the heating efficiency and ensure that the temperature is quickly maintained within a safe range. By real-time monitoring of the heating effect and performing feedback adjustment, the heating system can always operate in the best operating state, avoid excessive or insufficient temperature, improve system stability, effectively prevent the insulating material from becoming brittle in a low temperature environment, ensure the safety of the insulating work bucket, reduce the occurrence of safety accidents, and improve work efficiency.

[0053] S1 includes temperature sensors inside and outside the insulated working bucket to collect data and calculate comprehensive temperature data. The specific formula is:

[0054] Among them, T geo Indicates the current temperature, T1, T2, ..., T n represents temperature data, and n represents the number of sampled temperatures.

[0055] In this embodiment, temperature data is collected in real time by arranging multiple temperature sensors inside and outside the insulated working bucket. The collected temperature data includes temperature values ​​of multiple measurement points (T1, T2, ..., T n ), through the geometric mean formula:

[0056] Calculate the comprehensive value of the current temperature T geo This geometric mean calculation method can balance the temperature data of each sampling point, reduce the impact of a single temperature anomaly on the overall temperature result, provide a more accurate current temperature assessment result, and provide basic data support for subsequent temperature monitoring and heating adjustment.

[0057] The comprehensive temperature is calculated by geometric mean, which reduces the impact of abnormal data from a single temperature sensor and improves the accuracy of the current temperature calculation. At the same time, multiple temperature data are collected to reflect the overall temperature distribution inside and outside the insulating working bucket, which helps to more comprehensively evaluate the temperature status. The distributed temperature sensor sampling data is combined with the geometric mean algorithm to ensure that stable temperature monitoring results can be obtained in complex environments, provide reliable data support for temperature control, and adapt to different numbers of temperature sensor data. As the n value changes, the geometric mean calculation method is still effective and has high adaptability.

[0058] S2 includes modeling the embrittlement risk of insulating materials versus temperature, using the formula:

[0059]

[0060] Among them, R represents the embrittlement risk value, T geo Indicates the current temperature, T critical It represents the critical brittle temperature of the material, e represents the base of the natural logarithm, and k represents the temperature sensitivity coefficient of the material.

[0061] This embodiment evaluates the embrittlement risk of the insulating material at the current temperature Tge by constructing a relationship model between the embrittlement risk value R and the temperature. Specifically, the formula is: In, T geo is the current temperature data collected in real time, T criticalis the critical brittle temperature of the material, that is, the critical value at which the material begins to become brittle. k is the material temperature sensitivity coefficient, which reflects the sensitivity of different materials to temperature changes. By calculating the brittleness risk value R, it is possible to quantitatively analyze whether the insulating material is close to the brittleness risk state, thereby providing data support for further adjusting the heating system, increasing the material temperature, and ensuring the normal operation of the insulating work bucket. When the current temperature T geo With critical temperature T critical When the difference is large, the risk value R is low, indicating that the embrittlement risk is small. geo Gradually approaching T critical , the embrittlement risk value R increases significantly, indicating that the material embrittlement risk increases. As a temperature change sensitive parameter, the k value can be set according to the material properties, so that the model is more in line with the actual embrittlement characteristics of different materials. By modeling the relationship between the embrittlement risk value R and temperature, the quantitative analysis of the embrittlement risk of insulating materials can be realized, providing scientific data support. Combined with real-time temperature data, it can dynamically evaluate the embrittlement risk of insulating materials under different temperature conditions, providing a basis for subsequent temperature control. By adjusting the material temperature sensitivity coefficient k, it can adapt to different types of insulating materials, improve the universality and accuracy of the model, and timely identify the state of the material approaching the embrittlement risk, which helps to take temperature control measures in advance to prevent the degradation of material performance and improve the safety and stability of the insulating work bucket.

[0062] In S2, if the embrittlement risk value R>0.5, it is judged as an embrittlement risk state;

[0063] If R≤0.5, it is judged as a risk-free state; the risk judgment result is output, and if there is embrittlement risk, it enters step S3.

[0064] This embodiment makes a risk determination on the temperature state of the insulating material based on the embrittlement risk value R calculated in claim 3. The specific determination criteria are as follows:

[0065] R>0.5: Indicates that the current temperature of the insulating material is close to or lower than the critical embrittlement temperature, and the material has a high risk of embrittlement. The system will output the embrittlement risk status and automatically enter the S3 step to adjust the operation mode of the heating system, increase the temperature of the insulating material, and ensure the safety of the material.

[0066] R≤0.5: Indicates that the current temperature of the insulating material is within the safe range and the risk of embrittlement is low. The system determines it as a risk-free state and outputs the result.

[0067] By calculating the embrittlement risk value R in real time and automatically classifying the risks according to the judgment conditions, dynamic monitoring and risk warning of the embrittlement state of the insulation material can be achieved, thereby ensuring the normal operation of the insulation working bucket.

[0068] Quantitative judgment is made based on the embrittlement risk value R, which effectively distinguishes the embrittlement risk state and the risk-free state, ensuring that the judgment result is accurate and reliable. When it is determined to be an embrittlement risk state, the system automatically enters the S3 step and starts the temperature adjustment mechanism to achieve automated risk response and reduce human intervention. Through the preset risk threshold (R>0.5), the embrittlement risk of the material can be discovered in time, and emergency heating measures can be taken to avoid further expansion of the risk. In the risk-free state, monitoring is maintained but no additional operation is required to ensure operating efficiency, while reducing energy consumption and extending service life.

[0069] S4 includes collecting temperature change data and corresponding time during the operation of the heating system, constructing temperature change trends, and fitting the relationship between temperature and time. The specific formula is: T t =αt+β;

[0070] Among them, T t represents the temperature at a certain moment, t represents the time, α represents the temperature change rate, and β represents the initial temperature.

[0071] This embodiment collects the temperature changes in the heating system in real time during operation and establishes a linear relationship model between temperature and time in combination with the corresponding time data: t =αt+β;

[0072] Among them, T t represents the temperature at a certain moment, t represents the time, α represents the temperature change rate, and β represents the initial temperature. According to the temperature change data during the heating process, the temperature and time data are fitted and calculated using the least squares method or other fitting algorithms to determine the temperature change rate α and the initial temperature β. Through this formula, the temperature value at any time can be predicted, the operating effect of the heating system can be evaluated, and a basis can be provided for real-time adjustment of the heating parameters. If the temperature change rate α obtained by fitting is low, it means that the current heating power is insufficient, and the temperature rise can be accelerated by adjusting the power output; conversely, if α is too high and the temperature is close to the target temperature, the power can be appropriately reduced to avoid temperature overshoot and ensure the stability and energy saving of the heating system. By fitting the relationship between temperature and time, the temperature change trend of the heating system can be intuitively displayed, providing data support for subsequent temperature adjustment. The temperature change rate α can be used to evaluate the operating efficiency of the heating system in real time, and abnormal conditions in the heating process can be discovered and adjusted in time. The temperature at any time in the future can be predicted, and control measures can be taken in advance to optimize the operating performance of the heating system. According to the temperature change trend, the power output of the heating system can be dynamically adjusted to avoid temperature overshoot or undershoot, reduce energy consumption, and improve the energy-saving effect of the system. Combined with temperature change data and time data, the system can realize real-time monitoring of the heating effect, and adjust the operating parameters of the heating system through feedback to ensure that the temperature steadily rises to the target value.

[0073] S2 also includes determining the minimum temperature inside and outside the working bucket based on the data of the temperature sensor, comparing the minimum temperature with a preset safety threshold, and if the minimum temperature is lower than the preset safety threshold, determining that the insulating material is at risk of becoming brittle. If the minimum temperature is not lower than the safety threshold, continue to collect temperature data and re-judge in subsequent steps.

[0074] In this embodiment, temperature data is collected in real time by temperature sensors arranged inside and outside the insulated working bucket. The system determines the current minimum temperature (T min ), and compare the temperature with the preset safety threshold (T safe ) for comparison:

[0075] When T min <T safe : Indicates that the temperature of the insulating material has fallen below the safe range and there is a risk of becoming brittle. The system determines that the insulating material is in a state of risk of becoming brittle and proceeds to the next step (S3) to adjust the heating system to increase the temperature of the insulating material.

[0076] When T min ≥T safe : Indicates that the temperature of the insulating material is still within the safe range. The system continues to collect temperature data and repeats the judgment in subsequent steps to ensure that the material temperature always remains within the safe range.

[0077] This method can quickly identify the potential risk of brittleness of insulating materials by judging the lowest temperature, prevent the degradation of insulating material performance due to local low temperature, and ensure the safety and working stability of the insulating work bucket.

[0078] By comparing the minimum temperature with the safety threshold, it is possible to quickly determine whether the insulating material is at risk of becoming brittle, avoiding safety hazards caused by local low temperatures. When the minimum temperature is not lower than the safety threshold, the system continues to collect temperature data to form real-time closed-loop monitoring to ensure the continuity and accuracy of temperature data. Risk assessment based on the minimum temperature can effectively identify the problem of low temperature in a certain area inside or outside the working bucket, thereby improving the judgment accuracy of the system. When the risk of brittleness is detected, the system promptly enters the S3 step to adjust the operation of the heating system to ensure that the temperature of the insulating material returns to a safe range, thereby improving the reliability and stability of the system. When the minimum temperature is not lower than the safety threshold, the system does not take additional heating measures to avoid waste of resources and improve the energy-saving effect of the system.

[0079] Determining the lowest temperature inside and outside the working bucket includes acquiring all temperature data from multiple temperature sensors, sorting all temperature data, selecting a minimum value, and determining the lowest temperature in the current environment based on the minimum value.

[0080] In this embodiment, multiple temperature sensors are arranged inside and outside the insulating working bucket to collect temperature data at different positions in real time to form a temperature data set {T1, T2, ..., T n}. Temperature data sorting: Sort all the collected temperature data to ensure that the data is arranged in ascending order. Select the minimum value: Select the minimum value T from the sorted temperature data min , as the lowest temperature in the current environment. Minimum temperature judgment: based on the selected minimum value T min , and compare it with the preset safety threshold T safe For comparison, if T min <T safe , it is judged to be in embrittlement risk state. If T min ≥T safe , it is judged as a risk-free state, and the system continues to collect temperature data in real time and make subsequent judgments. Through this method, the system can ensure that the lowest temperature inside or outside the insulating work bucket is obtained, and the temperature abnormal area is identified in time to prevent the risk of embrittlement of the insulating material due to local low temperature. By sorting multiple temperature data and selecting the minimum value, the system can accurately judge the lowest temperature in the current environment and avoid missing the local low temperature area. By sorting all sensor data, the interference of high temperature or abnormal data can be effectively eliminated, and the accuracy of the lowest temperature judgment can be improved. Combined with the data collection of multiple temperature sensors, it can fully cover the internal and external areas of the insulating work bucket and provide more precise temperature monitoring. After obtaining the current lowest temperature, it can quickly determine whether there is a risk of embrittlement, and trigger temperature control measures in time to ensure the safety of the insulating material. It can be flexibly expanded according to different work bucket structures and the number of sensors to adapt to various complex temperature monitoring needs and improve applicability and reliability.

[0081] Determining the lowest temperature in the current environment based on the minimum value includes recording the lowest temperature data collected each time, comparing the current lowest temperature with the lowest temperature collected last time, and judging the temperature change trend based on the two collection results. If the current lowest temperature is less than the previous lowest temperature, the lowest temperature record is updated to the current lowest temperature.

[0082] This embodiment selects the lowest temperature of the current collection cycle by continuously collecting temperature data inside and outside the insulating working bucket and combining the sorting method. At the same time, the system retains the lowest temperature of the last collection cycle. By comparing the two lowest temperature data, the temperature change trend is determined. Through the above steps, the lowest temperature change trend of the insulating working bucket can be dynamically tracked to ensure that the lowest temperature record always reflects the real lowest temperature in the current environment, timely identify temperature anomalies and trigger the heating control mechanism to ensure the safety of the insulating material. By recording and comparing the current lowest temperature with the previous lowest temperature, the system can accurately track the temperature change trend, avoid the risk of missing temperature drop, update the lowest temperature record only when the temperature drops, reduce unnecessary data update operations, improve system operation efficiency, quickly identify the abnormal trend of temperature drop, and timely trigger the subsequent temperature control steps (such as heating system adjustment in S3) to prevent the insulating material from entering the embrittlement risk state. By continuously comparing the two lowest temperature data, the system can effectively eliminate the interference caused by short-term temperature fluctuations, ensure the accuracy and reliability of the lowest temperature record, and continuously record the lowest temperature data. The system can provide a historical trend of temperature change, which is convenient for operators to perform temperature monitoring analysis and system maintenance.

[0083] The calculation formula of temperature change rate α in S4 is: α=(T 后 -T 前 ) / (t 后 -t 前 );

[0084] Where α represents the rate of temperature change, T 前 and T 后 Represents the temperature value at two different time points, t 前 and t 后 They represent the corresponding time points respectively.

[0085] In this embodiment, the temperature change rate α is calculated by real-time acquisition of temperature and time data during the operation of the heating system to reflect the degree of temperature change over time. The specific steps include:

[0086] The temperature sensor collects temperature data T before and T after at two different time points t before and t after. According to the formula: α=(T 后 -T 前 ) / (t 后 -t 前 );

[0087] Where α represents the rate of temperature change, T 前 and T 后 Represents the temperature value at two different time points, t 前 and t 后 They represent the corresponding time points respectively.

[0088] By continuously monitoring the rate of temperature change, the heating effect can be evaluated in real time and the operating status of the heating system can be optimized.

[0089] Through the temperature data of two points in time, the temperature change rate per unit time can be accurately calculated to provide quantitative data support for system control. Based on the temperature change rate α, the system can monitor the operating efficiency of the heating system in real time and promptly discover problems with insufficient or excessive heating power. When the temperature rise rate is too high or close to the target value, the system can appropriately reduce the heating power to avoid temperature overshoot, improve the energy efficiency of the heating process, and reduce energy consumption. When the temperature change rate is abnormal (such as the rate is too low), the system can quickly judge and trigger temperature adjustment to ensure the temperature of the insulating material is stable. By updating the temperature change rate in real time, the system can dynamically adjust the operating parameters of the heating system to ensure the stability and reliability of the temperature control process.

[0090] Also provided is an insulated working bucket temperature intelligent monitoring system for implementing the steps of the insulated working bucket temperature intelligent monitoring method, the system comprising:

[0091] Temperature acquisition module, used to collect temperature data inside and outside the insulation working bucket;

[0092] A judgment module connected to the temperature acquisition module is used to judge whether the insulating material is in a state of risk of becoming brittle based on the temperature data;

[0093] A heating control module connected to the judgment module is used to adjust the operation mode of the heating system to increase the temperature of the insulating material when it is judged that the insulating material is in a risk state of becoming brittle;

[0094] The feedback adjustment module connected to the heating control module is used to monitor the heating effect in real time and provide feedback to adjust the working parameters of the heating system.

[0095] The system monitors the temperature data inside and outside the insulation working bucket in real time through the temperature acquisition module, and the judgment module determines the temperature state of the insulation material according to the temperature data and the embrittlement risk model. When it is determined that the insulation material is in a state of embrittlement risk, the heating control module starts the heating system, calculates the heating demand and outputs the appropriate heating power. The feedback regulation module monitors the temperature change effect in real time during the heating process, fits the relationship between temperature and time, calculates the temperature change rate, and dynamically adjusts the heating power through the feedback mechanism to ensure that the temperature is stable and reaches a safe range to prevent the insulation material from becoming brittle. The temperature acquisition, judgment, heating control and feedback regulation are integrated into one to realize the full-process intelligent monitoring of the temperature of the insulation working bucket. Through the temperature acquisition module and the judgment module, the current temperature state can be accurately judged, the embrittlement risk can be identified in time, and the safety of the system can be guaranteed. The heating control module dynamically calculates the output power according to the temperature difference and heating efficiency to ensure that the temperature of the insulation material quickly returns to the target value to prevent the temperature from being too low or too high. The feedback regulation module monitors the heating effect in real time, and realizes the intelligent optimization of the heating system parameters through the temperature change rate and trend analysis to improve the accuracy and stability of the regulation. The system can flexibly adjust the parameters according to different temperature conditions, material properties and environmental requirements to adapt to complex and changing working environments.

[0096] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring method for the temperature of an insulating working bucket, characterized in that: The method comprises: S1. Collect temperature data inside and outside the insulating working bucket; S2. Based on the temperature data, determine whether the insulating material is at risk of becoming brittle; S3. If the insulation material is at risk of becoming brittle, adjust the operation mode of the heating system to increase the temperature of the insulation material, including calculating the temperature difference between the target temperature and the current temperature. The specific formula is: ΔT = T target -T geo ; Where ΔT represents the temperature difference, T target Indicates the target temperature, T geo Indicates the current temperature; The power output of the heating system is adjusted according to the temperature difference. The specific formula is: P = η × ΔT; Where P represents the output power of the heating system, η represents the heating efficiency coefficient, and ΔT represents the temperature difference; S4. Monitor the heating effect in real time and provide feedback to adjust the working parameters of the heating system.

2. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 1, characterized in that: The S1 includes: The temperature sensors inside and outside the insulated working bucket collect data and calculate the comprehensive temperature data. The specific formula is: Among them, T geo Indicates the current temperature, T1, T2, ..., T n represents temperature data, and n represents the number of sampled temperatures.

3. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 1, characterized in that: The S2 includes: The embrittlement risk of the insulation material and temperature are modeled as follows: Among them, R represents the embrittlement risk value, T geo Indicates the current temperature, T critical It represents the critical brittle temperature of the material, e represents the base of the natural logarithm, and k represents the temperature sensitivity coefficient of the material.

4. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 3 is characterized in that: The embrittlement risk value R in S2 is determined to be an embrittlement risk state if R>0.5; If R≤0.5, it is considered as risk-free state; Output the risk judgment result. If there is embrittlement risk, enter step S3.

5. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 1, characterized in that: The S4 includes: Collect the temperature change data and corresponding time during the operation of the heating system, build the temperature change trend, and fit the relationship between temperature and time. The specific formula is: T t =αt+β; Among them, T t represents the temperature at a certain moment, t represents the time, α represents the temperature change rate, and β represents the initial temperature.

6. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 1, characterized in that: The S2 also includes determining the minimum temperature inside and outside the working bucket based on the data of the temperature sensor, comparing the minimum temperature with a preset safety threshold, and if the minimum temperature is lower than the preset safety threshold, determining that the insulating material is at risk of becoming brittle. If the minimum temperature is not lower than the safety threshold, continuing to collect temperature data and re-judging in subsequent steps.

7. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 6, characterized in that: Determining the lowest temperature inside and outside the working bucket includes acquiring all temperature data from multiple temperature sensors, sorting all temperature data, selecting a minimum value, and determining the lowest temperature in the current environment based on the minimum value.

8. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 7, characterized in that: Determining the lowest temperature in the current environment based on the minimum value includes recording the lowest temperature data collected each time, comparing the current lowest temperature with the lowest temperature collected last time, and judging the temperature change trend based on the two collection results. If the current lowest temperature is less than the previous lowest temperature, the lowest temperature record is updated to the current lowest temperature.

9. The method for intelligently monitoring the temperature of an insulating working bucket according to claim 5, characterized in that: The calculation formula of the temperature change rate α in S4 is: α=(T 后 -T 前 ) / (t 后 -t 前 ); Where α represents the temperature change rate, T 前 and T 后 Represents the temperature value at two different time points, t 前 and t 后 They represent the corresponding time points respectively.

10. An intelligent monitoring system for the temperature of an insulating working bucket, used to implement the steps of the intelligent monitoring method for the temperature of an insulating working bucket according to any one of claims 1 to 9, characterized in that: The system comprises: Temperature acquisition module, used to collect temperature data inside and outside the insulation working bucket; A judgment module connected to the temperature acquisition module is used to judge whether the insulating material is in a state of risk of becoming brittle based on the temperature data; A heating control module connected to the judgment module is used to adjust the operation mode of the heating system to increase the temperature of the insulating material when it is judged that the insulating material is in a risk state of becoming brittle; The feedback adjustment module connected to the heating control module is used to monitor the heating effect in real time and provide feedback to adjust the working parameters of the heating system.

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