A logging dual-chamber furnace main control and safety auxiliary system based on numerical control technology

Abstract

This invention relates to the field of logging equipment and its control technology. The invention provides a main control and safety auxiliary system for a logging dual-chamber furnace based on numerical control technology, including a logging dual-chamber furnace, an enhanced thermal field control module, a cooling module, and an energy efficiency optimization module. The enhanced thermal field control module and the cooling module are respectively installed in the logging dual-chamber furnace. The enhanced thermal field control module collects operating data from the logging dual-chamber furnace, analyzes the operating status of the furnace based on the operating data to generate analysis results, and controls the thermal field data of the furnace based on the analysis results. The cooling module uses supercritical fluid cooling technology to rapidly cool different areas, forming a gradient cooling distribution. The energy efficiency optimization module dynamically adjusts energy efficiency optimization based on the analysis results.

Description

Technical Field

[0001] This invention relates to the field of logging equipment and its control technology, and in particular to a logging dual-chamber furnace main control and safety auxiliary system based on numerical control technology. Background Technology

[0002] A casting furnace is an industrial device used to heat and melt recycled metals or other fusible materials for casting. Its basic principle is to use electrical energy, heat energy generated by fuel combustion, etc., to heat the material in the furnace above its melting point, turning it into a liquid state. Then, the liquid material is poured into a mold through a specific casting system, and after cooling and solidification, the desired casting is formed.

[0003] The current dual-chamber logging furnace mainly has the following problems:

[0004] 1. Traditional PID control is affected by thermal inertia, resulting in temperature fluctuations and large logging errors;

[0005] 2. Relying solely on passive alarms makes it impossible to predict faults in advance and make proactive adjustments;

[0006] 3. Traditional thermocouples or infrared temperature measurement have relatively large errors (±0.5℃), affecting experimental stability;

[0007] 4. The heating power adjustment relies on experience settings, resulting in large energy losses and uneven heat distribution in the furnace cavity.

[0008] This invention was made to address the common problems in the field, such as poor control precision, inability to actively adjust, insufficient monitoring capabilities, uneven heat distribution, and low intelligence. Summary of the Invention

[0009] The purpose of this invention is to address the shortcomings of current systems by proposing a main control and safety auxiliary system for a dual-chamber well logging furnace based on numerical control technology.

[0010] To overcome the shortcomings of the prior art, the present invention adopts the following technical solution:

[0011] A main control and safety auxiliary system for a well logging dual-chamber furnace based on CNC technology is disclosed. The system includes a well logging dual-chamber furnace, an enhanced thermal field control module, a cooling module, an energy efficiency optimization module, and an evaluation module. The enhanced thermal field control module and the cooling module are respectively installed within the well logging dual-chamber furnace. The enhanced thermal field control module collects operational data from the well logging dual-chamber furnace, analyzes the operational status of the furnace based on the data to generate analysis results, and controls the thermal field data of the furnace based on these results. The cooling module uses supercritical fluid cooling technology to rapidly cool different areas, creating a gradient cooling distribution. The energy efficiency optimization module dynamically adjusts energy efficiency based on the analysis results.

[0012] Optionally, the enhanced thermal field control module includes an intelligent sensing unit, a thermal field analysis unit, and a temperature field optimization unit. The intelligent sensing unit is used to form a distributed temperature monitoring network inside the dual-chamber furnace and monitor the furnace cavity temperature distribution data in real time. The thermal field analysis unit analyzes the operating status of the dual-chamber furnace based on the furnace cavity temperature distribution data to generate analysis results. The temperature field optimization unit dynamically adjusts the heating power distribution based on the analysis results to make the furnace cavity temperature distribution of the dual-chamber furnace more uniform.

[0013] Optionally, the cooling module includes a supercritical cooling unit, a microchannel fluid distribution unit, an intelligent cooling control unit, and a cooling performance monitoring unit. The supercritical cooling unit uses supercritical fluid as the cooling medium and cools the cooling areas in the logging dual-chamber furnace to achieve temperature gradient control in different areas. The microchannel fluid distribution unit lays a high-density microchannel cooling pipe network inside the logging dual-chamber furnace. The cooling performance monitoring unit monitors the cooling data of the cooling fluid, and the intelligent cooling control unit adjusts the supercritical fluid flow rate in different areas according to the cooling data. Optionally, the energy efficiency optimization module includes a heating power control unit, a long-term cooling flow rate optimization unit, an intelligent energy efficiency calculation unit, and an energy consumption assessment and prediction unit. The heating power control unit compares the analysis results with a set monitoring threshold. If the set monitoring threshold is exceeded, the power output of each heating unit is calculated, and the calculation result is transmitted to the intelligent energy efficiency calculation unit. The long-term cooling flow rate optimization unit calculates the cooling flow rate of different cooling zones based on historical data and the long-term optimization results of the energy consumption assessment and prediction unit, and transmits the optimized cooling flow rate to the intelligent energy efficiency calculation unit and the heating power control unit to collaboratively optimize temperature control. The intelligent energy efficiency calculation unit receives the power output of the heating power control unit and the cooling flow rate of the long-term cooling flow rate optimization unit, calculates the real-time energy efficiency ratio of the well logging dual-chamber furnace, and transmits the real-time energy efficiency ratio to the energy consumption assessment and prediction unit. The energy consumption assessment and prediction unit dynamically adjusts the long-term control strategy of heating power and cooling gradient based on historical data, real-time energy efficiency ratio, and long-term cooling optimization strategy. Optionally, the analysis unit calculates the operating status index I of the well logging dual-chamber furnace according to the following formula. R :

[0014] ;

[0015] In the formula, S T η is the thermal stability coefficient of the furnace cavity. E S is the thermal energy utilization efficiency coefficient. C R is the cooling uniformity coefficient. H Let ε be the thermal stress risk factor, and ε be the equilibrium parameter, whose value is a very small number greater than 0.

[0016] Optionally, the intelligent energy efficiency calculation unit calculates the real-time energy efficiency ratio (COP) according to the following formula:

[0017] ;

[0018] In the formula, M represents the number of independent electric heating elements used for heating in the well logging dual-chamber furnace, and Q... useful,i P represents the effective heat actually transferred to the furnace cavity by the i-th heating unit. heat,i Δt represents the electrical energy consumed by the heating element of the logging dual-chamber furnace per unit time, and Δt represents the evaluation cycle time.

[0019] Optionally, the operational data collected by the intelligent sensing unit includes temperature data, heating data, cooling data, and energy consumption data.

[0020] Optionally, the intelligent sensing unit includes a carbon nanotube temperature sensor array, an infrared thermal imaging sensor, an intelligent power meter, a flow meter, and a pressure sensor. The carbon nanotube temperature sensor array is arranged inside the well logging dual-chamber furnace cavity and measures the internal temperature of the well logging dual-chamber furnace cavity. The infrared thermal imaging sensor is positioned directly facing the well logging dual-chamber furnace cavity and remotely measures the furnace wall and thermal field distribution. The intelligent power meter collects the power of the heating elements in each heating zone inside the well logging dual-chamber furnace cavity, the flow meter detects the flow rate of the cooling pipe network of the cooling module, and the pressure sensor detects the pressure of the cooling pipe network of the cooling module.

[0021] Optionally, the supercritical fluid is set to H2O.

[0022] Optionally, the intelligent cooling control unit acquires the current cooling data and calculates the local flow velocity v according to the following formula. cool,i :

[0023] ;

[0024] In the formula, v base The reference flow rate is T, α is the temperature deviation correction factor, and T is the reference flow rate. i -T target This refers to temperature deviation.

[0025] The beneficial effects achieved by this invention are:

[0026] 1. By enhancing the cooperation between the thermal field control module and the cooling module, the temperature distribution inside the furnace cavity is made more uniform, and the temperature gradient is controlled to ensure that the entire system has higher temperature stability, reduce the impact of thermal stress, and improve the heat treatment quality of logging tools.

[0027] 2. By enhancing the cooperation between the thermal field control module and the energy efficiency optimization module, the heating power can be dynamically adjusted according to the temperature distribution, and the energy efficiency optimization module can reduce unnecessary energy consumption, ensuring that the entire system reduces energy consumption and improves energy utilization while maintaining the target temperature.

[0028] 3. By working together with the cooling module and the energy efficiency optimization module, the cooling system can adjust the cooling flow rate according to the real-time temperature and heat field distribution, and avoid overcooling or undercooling, ensuring that the entire system can quickly stabilize the furnace temperature while saving energy and improving cooling efficiency.

[0029] 4. By enhancing the synergistic cooperation of the thermal field control module, cooling module, and energy efficiency optimization module, the heating and cooling system is always in a dynamic equilibrium state, and the temperature gradient control of the heat treatment process is optimized, ensuring that the entire system has higher temperature stability, energy efficiency, and equipment operation reliability. Attached Figure Description

[0030] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0031] Figure 1 This is a schematic diagram of the overall block shape of the present invention.

[0032] Figure 2 This is a block diagram of the enhanced thermal field control module of the present invention.

[0033] Figure 3 This is a block diagram of the cooling module of the present invention.

[0034] Figure 4 This is a block diagram of the heating power control unit of the present invention.

[0035] Figure 5 This is a block diagram of the energy efficiency optimization module of the present invention. Detailed Implementation

[0036] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0037] Example 1: According to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5As shown, this embodiment provides a main control and safety auxiliary system for a well logging dual-chamber furnace based on numerical control technology. The system includes a dual-chamber furnace, an enhanced thermal field control module, a cooling module, an energy efficiency optimization module, and an evaluation module. The enhanced thermal field control module and the cooling module are respectively installed in the well logging dual-chamber furnace. The enhanced thermal field control module collects operating data from the well logging dual-chamber furnace, analyzes the operating status of the furnace based on the data to generate analysis results, and controls the thermal field data of the furnace based on these results. The cooling module uses supercritical fluid cooling technology to rapidly cool different areas, forming a gradient cooling distribution. The energy efficiency optimization module dynamically adjusts energy efficiency optimization based on the analysis results.

[0038] Optionally, the enhanced thermal field control module includes an intelligent sensing unit, a thermal field analysis unit, and a temperature field optimization unit. The intelligent sensing unit is used to form a distributed temperature monitoring network inside the dual-chamber furnace and monitor the furnace cavity temperature distribution data in real time. The thermal field analysis unit analyzes the operating status of the dual-chamber furnace based on the furnace cavity temperature distribution data to generate analysis results. The temperature field optimization unit dynamically adjusts the heating power distribution based on the analysis results to make the furnace cavity temperature distribution of the dual-chamber furnace more uniform.

[0039] Optionally, the intelligent sensing unit includes a carbon nanotube temperature sensor array, an infrared thermal imaging sensor, an intelligent power meter, a flow meter, and a pressure sensor. The carbon nanotube temperature sensor array is arranged inside the well logging dual-chamber furnace cavity and measures the internal temperature of the well logging dual-chamber furnace cavity. The infrared thermal imaging sensor is positioned directly facing the well logging dual-chamber furnace cavity and remotely measures the furnace wall and thermal field distribution. The intelligent power meter collects the power of the heating elements in each heating zone inside the well logging dual-chamber furnace cavity, the flow meter detects the flow rate of the cooling pipe network of the cooling module, and the pressure sensor detects the pressure of the cooling pipe network of the cooling module.

[0040] Optionally, the thermal field analysis unit calculates the operating status index I of the logging dual-chamber furnace according to the following formula. R :

[0041] ;

[0042] In the formula, S T η is the thermal stability coefficient of the furnace cavity. E S is the thermal energy utilization efficiency coefficient. C R is the cooling uniformity coefficient. H ε is the thermal stress risk factor, and ε is the equilibrium parameter, which is a very small number greater than 0.

[0043] In this embodiment, the furnace cavity thermal stability coefficient S TCalculate according to the following formula:

[0044] ;

[0045] In the formula, T i ∂T / ∂x, ∂T / ∂y, and ∂T / ∂z represent the temperature gradient (spatial rate of change), used to measure the uniformity of temperature inside the furnace cavity. Specifically, ∂T / ∂x is the temperature gradient in the x-direction, ∂T / ∂y is the temperature gradient in the y-direction, and ∂T / ∂z is the temperature gradient in the z-direction.

[0046] In this embodiment, the well logging dual-chamber furnace is set as a regular rectangle, with the length direction of the furnace chamber as the x-axis, the width direction as the y-axis, and the height direction as the z-axis.

[0047] The origin (0,0,0) is set in one corner of the furnace cavity, and the temperature sensors are arranged in different spatial locations, (x i ,y j ,z k ), where i = 1, 2, ..., N x j=1,2,...,N y k=1,2,...,N z Among them, N x N y N z These represent the number of measurement points in the three directions.

[0048] Temperature sensors are installed in key areas of the furnace cavity at different heights, widths, and depths to form a gridded array of temperature measurement points. Carbon nanotube temperature sensors, fiber optic grating (FBG) sensors, and infrared thermal imagers are used to collect temperature distribution data inside the furnace cavity.

[0049] A fixed sampling interval Δt (e.g., 1 second) is used to collect the temperature of all measuring points every second. The temperature data is processed by the data acquisition controller (DAQ) and stored in the time series database (InfluxDB). At the same time, the data is transmitted to the temperature field analysis unit for real-time gradient calculation.

[0050] The temperature field analysis unit performs gradient calculations according to the following formula (using the finite difference method):

[0051] ;

[0052] In the formula, T(x,y,z) is the temperature at position (x,y,z), T(x+Δx,y,z) is the temperature at position (x,y+Δy,z), T(x,y,z+Δz) is the temperature at position (x,y,z), ∂T / ∂x is the temperature gradient in the x-direction, ∂T / ∂y is the temperature gradient in the y-direction, ∂T / ∂z is the temperature gradient in the z-direction, Δx is the spacing in the x-direction, i.e., the distance between two temperature measurement points, Δy is the spacing in the y-direction, and Δz is the spacing in the z-direction.

[0053] Thermal efficiency coefficient η E Calculate according to the following formula:

[0054] ;

[0055] In the formula, Q i useful is the energy effectively transferred to the furnace cavity by the i-th heating unit, Q. i input is the total input energy of the i-th heating unit, and M is the total number of heating units.

[0056] Cooling uniformity coefficient S C Calculate according to the following formula:

[0057] ;

[0058] In the formula, v k This represents the flow rate of the k-th cooling channel, where K is the total number of cooling channels, and its value is set based on actual conditions. k / ∂x,∂v k / ∂y,∂w k / ∂z represents the gradient change of the local velocity field in the x, y, and z directions of the k-th cooling channel, used to evaluate cooling uniformity. A Cartesian coordinate system is established inside the furnace cavity, with the x-axis representing the mainstream fluid direction (usually the flow direction of the cooling fluid), the y-axis representing the lateral diffusion direction, and the z-axis representing the vertical direction (which may include natural convection or auxiliary flow).

[0059] Flow velocity measurement points were set at different locations inside the furnace cavity, and (u, v, w) were recorded. The flow velocity was measured using equipment such as ultrasonic flowmeters, hot-wire anemometers, and fiber optic flow velocity sensors.

[0060] ;

[0061] In the formula, u, v, w are the velocity components of the fluid in the x, y, and z directions, respectively, and Δx, Δy, Δz are the distance between the measuring points.

[0062] If S C A larger reading indicates uneven cooling, which may lead to localized overcooling or overheating.

[0063] If S C A value close to zero indicates stable flow and uniform cooling.

[0064] Thermal stress risk factor R H Calculate according to the following formula:

[0065] ;

[0066] In the formula, σ m thermal is the thermal stress in the m-th region, and its value is calculated by the following formula: σ m thermal=E m *α m *ΔT m E m The elastic modulus of the material is determined based on the known physical properties of the furnace cavity material (experimental data or material database), α. m ΔT is the coefficient of thermal expansion of the furnace cavity material. m The temperature difference within the furnace cavity is measured by deploying temperature sensors (thermocouples, fiber optic sensors, and infrared thermometers). P represents the number of zones within the furnace cavity, and ϵ m As a regional weighting factor, based on the regional heated area A m ,ϵ m =A m / A total ;

[0067] The heating area of ​​the furnace cavity is set as the design area of ​​the furnace cavity.

[0068] The temperature field optimization unit determines the new heating power P according to the following formula. heat,i new:

[0069] ;

[0070] In the formula, P heat,i old represents the original power of the i-th heating unit, k1 is the temperature error correction coefficient, k2 is the operating state index correction coefficient, and T avg The target average temperature, T i Let I be the real-time temperature near the i-th heating unit. R This is the operational status index.

[0071] Among them, the average target temperature T avg Calculate according to the following formula:

[0072] ;

[0073] In the formula, T i Let N be the real-time temperature of the i-th heating zone, and N be the number of heating zones in the furnace cavity.

[0074] In this embodiment, an example of the values ​​for the temperature error correction coefficient k1 and the operating state index correction coefficient k2 is provided:

[0075] 1) In the scenario of heat treatment (strict temperature control) of precision logging tools, k1=1.0; k2=10;

[0076] 2) In the scenario of metal heat treatment (conventional production), k1=0.8; k2=30;

[0077] 3) In the ceramic / glass sintering scenario, k1=0.5; k2=20;

[0078] 4) In scenarios involving rapid heating processes (high capacity requirements), k1=2.5; k2=60;

[0079] 5) In scenarios involving slow heating (to avoid thermal shock), k1=0.3; k2=15;

[0080] Those skilled in the art can select specific temperature error correction coefficient k1 and operating status index correction coefficient k2 according to the actual application scenario.

[0081] In this embodiment, if I R If the temperature is too low (uneven temperature field, unstable flow, high thermal stress), increase the heating power in the low-temperature area to compensate for the insufficient temperature, and reduce the heating power in the high-temperature area to prevent local overheating.

[0082] If I R If the temperature is moderate (the furnace cavity is stable), the current heating power distribution will be maintained to ensure a stable temperature field.

[0083] If I R If the power consumption is too high (or the energy efficiency is too low), reduce the total power input and optimize the heating strategy:

[0084] P heat,i new=P heat,i old−k3⋅P heat,i old;

[0085] In the formula, k3 is the global power optimization coefficient, and its value ranges from (0, 1].

[0086] In this embodiment, a corresponding global power optimization coefficient k3 is selected for different usage scenarios:

[0087] 1) In the control scenario of a high-precision well logging dual-chamber furnace, k3 = 0.05;

[0088] 2) In the control scenario of a dual-chamber furnace for long-term stability logging, k3 = 0.2;

[0089] 3) In control scenarios involving rapid pulse temperature switching, k3 = 0.5;

[0090] In this embodiment, the cooling module is installed in the logging dual-chamber furnace and controls the temperature inside the logging dual-chamber furnace, especially controlling the furnace cavity temperature and protecting the logging equipment;

[0091] Optionally, the cooling module includes a supercritical cooling unit, a microchannel fluid distribution unit, an intelligent cooling control unit, and a cooling performance monitoring unit. The supercritical cooling unit uses supercritical fluid as the cooling medium and cools the cooling areas in the logging dual-chamber furnace to achieve temperature gradient control in different areas. The microchannel fluid distribution unit lays a high-density microchannel cooling pipe network inside the logging dual-chamber furnace, and achieves precise cooling of local areas by allowing supercritical fluid at different pressures to flow in the microchannels. The cooling performance monitoring unit monitors the cooling data of the cooling fluid, and the intelligent cooling control unit adjusts the supercritical fluid flow rate in different areas according to the current cooling data.

[0092] In this embodiment, the cooling data includes flow rate, pressure, temperature, and heat exchange efficiency.

[0093] The supercritical cooling unit includes a supercritical fluid supply component, a cooling distribution component, and a cooling performance detection component. The supercritical fluid supply component supplies cooling medium to the cooling area in the logging dual-chamber furnace, and the cooling distribution component distributes the supercritical fluid to cool the cooling area in the logging dual-chamber furnace.

[0094] The supercritical fluid supply component includes a high-pressure storage tank, a booster pump, and a preheater. The high-pressure storage tank stores supercritical fluid, the booster pump is used to maintain the supercritical fluid at a set pressure, and the preheater heats the fluid to a supercritical state.

[0095] The cooling distribution component includes a microchannel cooling pipe network and a high-pressure regulating valve. The microchannel cooling pipe network is arranged inside the furnace cavity, and the high-pressure regulating valve is used to control the fluid flow rate and pressure in different areas.

[0096] For high-heat areas, embedded cooling channels are used to improve heat exchange efficiency, while for transition areas, surface-mounted cooling pipes are used to optimize temperature control.

[0097] The cooling performance detection unit includes the temperature sensor, pressure sensor, and flow meter. The temperature sensor detects the temperature of the cooling area in real time, the pressure sensor measures the pressure change of the cooling fluid, and the flow meter detects the flow rate of the supercritical fluid.

[0098] In this embodiment, the supercritical fluid is set to H2O (water).

[0099] This embodiment employs supercritical water cooling (H2O) technology to achieve efficient and precise cooling of the logging dual-chamber furnace. The supercritical cooling unit includes a supercritical fluid supply component, a cooling distribution component, and a cooling performance detection component. These three components work together to ensure cooling uniformity, temperature control stability, and efficient energy consumption management.

[0100] First, the supercritical fluid supply component provides the supercritical water-cooling medium. A high-pressure tank stores supercritical water (H2O), a booster pump maintains a stable flow of supercritical water at a set pressure, and a preheater heats the fluid to a supercritical state, ensuring the cooling process remains within optimal operating parameters. Subsequently, the cooling distribution component delivers the supercritical water to different cooling zones of the logging dual-chamber furnace. A microchannel cooling network precisely distributes the supercritical water flow, and high-pressure regulating valves control the flow rate and pressure in different zones to meet varying heat load requirements within the furnace chamber. In high-heat zones, embedded cooling channels enhance heat exchange between the supercritical water and high-temperature surfaces, improving cooling efficiency. In temperature transition zones, surface-mounted cooling pipes optimize the cooling gradient, ensuring a uniform temperature field.

[0101] Throughout the cooling process, the cooling efficiency detection component monitors the supercritical water cooling status in real time. Temperature sensors collect the temperature of the cooling zone, pressure sensors monitor the pressure changes of the supercritical water fluid, and flow meters accurately detect the flow rate of the supercritical water, ensuring that the cooling operates under optimal conditions. The cooling data is analyzed by the intelligent cooling control unit, which dynamically adjusts the supercritical water flow rate and distribution strategy according to changes in the furnace cavity temperature to achieve precise cooling control.

[0102] Ultimately, through precise liquid supply, intelligent distribution, and real-time monitoring, this supercritical water cooling system enables the logging dual-chamber furnace to achieve higher temperature uniformity, lower energy consumption, and better cooling stability, ensuring the high efficiency and reliability of the furnace heat treatment process.

[0103] The cooling performance monitoring unit collects data on flow rate, pressure, temperature, and heat exchange efficiency. The intelligent cooling control unit calculates the cooling requirements of each zone and dynamically adjusts the fluid pressure and flow rate.

[0104] The intelligent cooling control unit acquires the current cooling data and calculates the cooling power Q according to the following formula. cooling :

[0105] ;

[0106] In the formula, Q cooling Where is the cooling power (W), m is the supercritical fluid mass flow rate (kg / s), and C is the temperature. p ΔT is the specific heat capacity of the supercritical fluid (J / kg·K), and ΔT is the temperature drop of the cooling fluid (K).

[0107] In addition, the intelligent cooling control unit acquires the current cooling data and calculates the local flow velocity v according to the following formula. cool,i :

[0108] ;

[0109] In the formula, v base The reference flow rate is T, α is the temperature deviation correction factor, and T is the reference flow rate. i -T target Temperature deviation;

[0110] Among them, when the temperature in a certain area is higher than the target value T target The intelligent cooling control unit increases the flow rate of the supercritical fluid in that area. When the temperature in a certain area falls below the target value, the flow rate of the supercritical fluid is reduced to decrease the cooling effect.

[0111] In this embodiment, the temperature deviation correction coefficient is selected based on the actual usage scenario and substituted into the above formula, specifically:

[0112] 1) In the application scenarios of heat treatment for precision logging tools (to maintain uniform temperature, avoid excessive local temperature changes, and reduce the impact of thermal stress), then α=0.02;

[0113] 2) In the scenario of metal heat treatment (high temperature alloy) (controlling the cooling rate to avoid cracks in the metal due to excessive temperature difference, but still needing to moderately accelerate the cooling rate), then α=0.1;

[0114] 3) In scenarios involving rapid production (cycle-based cooling) (where cooling to the target temperature is required in a short time to improve production efficiency), α = 0.4;

[0115] In summary, it is necessary to select an appropriate temperature deviation correction factor α based on the specific application scenario.

[0116] By enhancing the cooperation between the thermal field control module and the cooling module, the temperature distribution inside the furnace cavity becomes more uniform, and the temperature gradient is controlled to ensure that the entire system has higher temperature stability, reduce the impact of thermal stress, and improve the heat treatment quality of logging tools.

[0117] Optionally, the energy efficiency optimization module includes a heating power control unit, a long-term cooling flow rate optimization unit, an intelligent energy efficiency calculation unit, and an energy consumption assessment and prediction unit. The heating power control unit compares the analysis results with the set monitoring thresholds. If the set monitoring thresholds are exceeded, the power output of each heating unit is calculated, and the calculation results are transmitted to the intelligent energy efficiency calculation unit. The long-term cooling flow rate optimization unit calculates the cooling flow rate of different cooling zones based on historical data and the long-term optimization results of the energy consumption assessment and prediction unit, and transmits the optimized cooling flow rate to the intelligent energy efficiency calculation unit and the heating power control unit to collaboratively optimize temperature control. The intelligent energy efficiency calculation unit receives the power output of the heating power control unit and the cooling flow rate of the long-term cooling flow rate optimization unit, calculates the real-time energy efficiency ratio of the well logging dual-chamber furnace, and transmits the real-time energy efficiency ratio to the energy consumption assessment and prediction unit. The energy consumption assessment and prediction unit dynamically adjusts the long-term control strategy of heating power and cooling gradient based on historical data, real-time energy efficiency ratio, and long-term cooling optimization strategy.

[0118] The heating power control unit obtains the average target temperature T. avg And based on the average target temperature T avg and target temperature T target The average temperature deviation ΔT in the furnace cavity:

[0119] ΔT=T target -T avg ;

[0120] Simultaneously, the heating power control unit will adjust the average temperature deviation ΔT of the furnace cavity and the system-set temperature error threshold θ. T Comparison, if ΔT > θ T If so, the heating power needs to be adjusted;

[0121] The heating power control unit calculates the heating power ΔP according to the following formula. heat,i :

[0122] ;

[0123] In the formula, k5 is the temperature error correction coefficient, k6 is the operating state index correction coefficient, and T target -T i For the temperature deviation of the heating unit, I R This refers to the operating status index of the well logging dual-chamber furnace;

[0124] The temperature error correction coefficient is calculated according to the following formula:

[0125] ;

[0126] In the formula, P max P min T represents the maximum / minimum heating power.max ,T min These are the upper and lower limits of the furnace cavity temperature.

[0127] In addition, the operating status index correction coefficient k6 is determined according to the following formula:

[0128] ;

[0129] In the formula, P max IR is the maximum heating power of the heating unit. max The highest operating status index (IR) in the historical data of the well logging dual-chamber furnace. min This is the smallest operating status index in the historical data of the well logging dual-chamber furnace.

[0130] The heating power adjustment unit calculates a new power output Pnew based on the heating power. heat，i :

[0131] ;

[0132] In the formula, Pold heat，i For the current power output, ΔP heat,i The calculated heating power;

[0133] At the same time, the new power output needs to meet certain constraints to prevent power overload:

[0134] The limiting conditions are as follows:

[0135] ;

[0136] In the formula, P max P is the maximum allowable power of the heating unit. min This is the minimum allowable power for the heating unit;

[0137] Meanwhile, in order to optimize the overall power distribution of the furnace cavity, the heating power adjustment unit considers global energy efficiency and determines the final power Pfinal. heat,i :

[0138] ;

[0139] In the formula, k7 is the energy efficiency optimization coefficient, and its value is determined according to the following formula:

[0140] ;

[0141] In the formula, η E max represents the system's maximum energy efficiency ratio based on historical data, and η represents the maximum energy efficiency ratio of the system. E min is the minimum energy efficiency ratio of the system in historical data.

[0142] The cooling flow rate and gradient optimization unit obtain the heat transfer Q of each region. cool,i :

[0143] ;

[0144] In the formula, Q cool,i For the heat exchange in cooling zone i, m i C is the mass flow rate of the cooling fluid (kg / s). p T is the specific heat capacity of the cooling fluid. inlet,i For the fluid inlet, T outlet,i The outlet temperature of the fluid.

[0145] The cooling flow rate and gradient optimization unit optimizes the heat transfer Q of each region. cool,i Calculate the energy efficiency η of the cooling zone cool,i :

[0146] ;

[0147] In the formula, P coolant,i This refers to the power consumption of the cooling pump.

[0148] Cooling flow rate and gradient optimization unit cooling region energy efficiency η cool,i Calculate the new cooling flow rate v cool,i new:

[0149] ;

[0150] In the formula, k v η is the flow rate optimization factor (set based on experimental experience). target The target cooling efficiency is set by the system.

[0151] In this embodiment, a flow rate optimization coefficient k is provided. v Examples of possible values:

[0152] In the scenario of precision logging dual-chamber furnace for parts processing and heat treatment, k v =0.001;

[0153] In the scenario of heat treatment of metals (high-temperature alloys) in a precision logging dual-chamber furnace, k v =0.005;

[0154] In the scenario of ceramic / glass sintering in a precision logging dual-chamber furnace, k v =0.002;

[0155] In the scenario of rapid cooling (cycle-based production) using a precision logging dual-chamber furnace, k v =0.01;

[0156] If ηcool,i <η target This increases the cooling flow rate.

[0157] If η cool,i >η target Reduce the cooling flow rate.

[0158] In this embodiment, the fluid flow rate in each area is adjusted by an intelligent cooling control unit.

[0159] Optionally, the intelligent energy efficiency calculation unit calculates the real-time energy efficiency ratio (COP) according to the following formula:

[0160] ;

[0161] In the formula, M represents the number of independent electric heating elements used for heating in the well logging dual-chamber furnace, and Q... useful,i P represents the effective heat actually transferred to the furnace cavity by the i-th heating unit. heat,i Δt represents the electrical energy consumed by the heating element of the logging dual-chamber furnace per unit time, and Δt represents the evaluation cycle time.

[0162] If the COP is lower than the set threshold, it indicates that the system has low energy efficiency and the energy consumption strategy needs to be optimized.

[0163] If the COP is higher than the set threshold, it indicates that the system is operating well and the control intensity can be reduced.

[0164] After the COP is determined, the result is transmitted to the energy consumption assessment and prediction unit.

[0165] The energy consumption assessment and prediction unit calculates the energy consumption trend η based on historical data. E :

[0166] ;

[0167] In the formula, η E (i) represents the energy efficiency ratio in the i-th evaluation, and N represents the number of evaluations.

[0168] If η E If the power consumption remains below the system's set evaluation threshold for an extended period, it indicates low energy utilization and the need to optimize power allocation.

[0169] If η E If the power consumption remains above the system's set evaluation threshold for an extended period, some heating / cooling power will be reduced, thus saving energy.

[0170] The energy consumption assessment and prediction unit determines to adjust the heating power Pnew heat :

[0171] ;

[0172] In the formula, k H The heating power adjustment coefficient has the following value: k H =ΔP max / Δη E ΔP max The maximum allowable heating power adjustment amount, whose value is set by the system and is equivalent to the default value, Δη E The acceptable adjustment range for the energy efficiency ratio (default is 0.1~0.2). In this embodiment, the value corresponding to the midpoint of the adjustment range can be selected. heat For the current heating power, η target The target energy efficiency ratio;

[0173] The energy consumption assessment and prediction unit determines the cooling flow rate vnew cool :

[0174] ;

[0175] In the formula, k C k is the cooling flow rate adjustment coefficient. C =Δv max / Δη E Δv max The maximum allowable cooling flow rate adjustment, whose value is set by the system and is equivalent to the default value, is Δη. E The acceptable adjustment range for the energy efficiency ratio (default is 0.1~0.2). In this embodiment, the value corresponding to the midpoint of the adjustment range can be selected, vold. cool Given the current cooling flow rate, η target The target energy efficiency ratio is determined by the parameters currently being used by the system, and is equivalent to a known value.

[0176] By enhancing the cooperation between the thermal field control module and the energy efficiency optimization module, the heating power can be dynamically adjusted according to the temperature distribution. Furthermore, the energy efficiency optimization module can reduce unnecessary energy consumption, ensuring that the entire system maintains the target temperature while reducing energy consumption and improving energy utilization.

[0177] By working together with the cooling module and the energy efficiency optimization module, the cooling system can adjust the cooling flow rate according to the real-time temperature and heat field distribution, and avoid overcooling or undercooling, ensuring that the entire system can quickly stabilize the furnace temperature while saving energy and improving cooling efficiency.

[0178] By enhancing the synergy between the thermal field control module, cooling module, and energy efficiency optimization module, the heating and cooling system is always in a dynamic equilibrium state, and the temperature gradient control during the heat treatment process is optimized, ensuring that the entire system has higher temperature stability, energy efficiency, and equipment operational reliability.

[0179] Example 2: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them, according to... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown, the well logging dual-chamber furnace main control and safety auxiliary system also includes an evaluation module. The evaluation module collects energy efficiency data in one operating cycle, evaluates the energy efficiency in one operating cycle to form an evaluation result, and provides intelligent control suggestions and automatically adjusts the system operating status of the well logging dual-chamber furnace based on the evaluation result.

[0180] The evaluation module includes a data acquisition unit, an energy efficiency analysis and evaluation unit, a decision optimization unit, and an adaptive adjustment unit. The data acquisition unit collects the operating data of the well logging dual-chamber furnace and stores historical data. The energy efficiency analysis and evaluation unit calculates the furnace cavity energy efficiency index run of the well logging dual-chamber furnace and evaluates whether the heating / cooling strategy is reasonable. The decision optimization unit combines historical data and the well logging dual-chamber furnace cavity energy efficiency index run to generate intelligent control suggestions. The adaptive adjustment unit automatically adjusts the heating, cooling, and energy consumption management strategies according to the control suggestions.

[0181] Optionally, the operational data collected by the data acquisition unit includes temperature data, heating data, cooling data, and energy consumption data.

[0182] Optionally, the decision optimization suggestions include heating regulation, cooling regulation, energy consumption optimization, and safety adjustment.

[0183] The data acquisition unit includes a data acquisition unit and a data storage unit. The data acquisition unit acquires the operating data of the well logging dual-chamber furnace, and the data storage unit stores the operating data acquired by the data acquisition unit.

[0184] The energy efficiency analysis and evaluation unit calculates the furnace cavity energy efficiency index run of the well logging dual-chamber furnace according to the following formula:

[0185] ;

[0186] In the formula, Q useful For efficient heat transfer, P total The heating and cooling efficiency consumed by the entire system must satisfy: P total =P heat +P cool S T η is the thermal stability coefficient of the furnace cavity. cool For cooling efficiency, the following condition must be met: η cool =Q cool / P cool .

[0187] Among them, the effective heat transfer Q useful Calculate according to the following formula:

[0188] ;

[0189] In the formula, m i C is the mass of the object being heated. p,i For specific heat capacity, ΔT i This refers to temperature changes.

[0190] The decision optimization unit calculates the current operating state deviation of the furnace cavity:

[0191] ;

[0192] In the formula, I run I is the furnace cavity energy efficiency index. target For the target energy efficiency index, ΔI run For energy efficiency deviation, S T Let ΔS be the thermal stability coefficient of the furnace cavity. T To measure the deviation in temperature uniformity, Δη cool S represents the current cooling efficiency deviation. T target is the thermal stability coefficient of the furnace cavity, ηtarget cool The target is cooling efficiency.

[0193] The decision optimization unit calculates the Euclidean distance d between historical data and the current operating condition:

[0194] ;

[0195] In the formula, ΔI run history represents the historical deviation of the furnace energy efficiency index, ΔS. T history represents the historical temperature uniformity deviation, Δη cool Δη represents the current cooling efficiency deviation. cool history refers to the historical cooling efficiency deviation.

[0196] Extract the best heating / cooling strategies from similar cases:

[0197] ;

[0198] In the formula, P heat "Best" represents the optimal heating power, and "K" represents the number of nearest neighbor samples, which is determined by the data provided in the system's database and is equivalent to a known value. P heat history, i represents the heating power of the i-th historical sample, v cool "best" refers to the optimal cooling flow rate, v coolhistory, i represents the cooling flow rate of the i-th historical sample.

[0199] The decision optimization unit determines the optimal heating power based on historical data:

[0200] ;

[0201] In the formula, k H To optimize the heating power coefficient and control the adjustment level, the following condition must be met: k C =ΔP max / ΔI cool max, ω1 is the power weighting factor, which determines the balance between the historical optimal heating power and the current heating adjustment strategy, P heat "best" represents the historical best heating power, ΔI. run This is due to energy efficiency deviation.

[0202] The decision optimization unit determines the optimal cooling flow rate based on historical data:

[0203] ;

[0204] In the formula, k C To optimize the cooling flow rate, the adjustment of the cooling flow rate is controlled, and its value satisfies: k C =Δv max / Δη cool max, ω2 is the cooling weighting factor, which determines the balance between the historical optimal cooling flow rate and the current cooling adjustment strategy, V cool "best" represents the historical best cooling flow rate, Δη. cool This represents the current cooling efficiency deviation.

[0205] In this embodiment, examples of values ​​for the power weighting factor ω1 and the cooling weighting factor ω2 are provided:

[0206] 1) In the production scenario of precision tools, ω1=0.65, ω2=0.67;

[0207] 2) In the production scenario of metal heat treatment (high temperature alloy), ω1=0.57, ω2=0.56;

[0208] 3) In a rapid production scenario (cycle heating), ω1=0.38, ω2=0.40;

[0209] The decision optimization suggestions include:

[0210] 1) Heating power adjustment:

[0211] Increase / decrease heating power P heat new;

[0212] Optimize heating power distribution (adjust the output of heating units in different areas);

[0213] If the temperature in a certain area is abnormal, the heating power will be automatically reduced and an alarm will be issued.

[0214] 2) Cooling strategy optimization;

[0215] Adjust the cooling flow rate v cool new;

[0216] Optimize cooling fluid distribution (cooling gradient in different areas);

[0217] If the cooling system malfunctions, adjust the flow rate or enter emergency mode.

[0218] 3) Energy consumption optimization:

[0219] Automatically adjust energy management strategies to reduce unnecessary power consumption;

[0220] 4) Safety adjustment measures:

[0221] Monitor temperature, equipment, power supply, and cooling status;

[0222] Automatically responds to abnormal temperatures;

[0223] Switch to safe mode in case of a malfunction;

[0224] Trigger a safe shutdown in an emergency;

[0225] The adaptive adjustment unit obtains the decision optimization suggestions and automatically adjusts the heating, cooling, and energy management strategies.

[0226] By working together with the evaluation module and the energy efficiency optimization module, the evaluation module can analyze the system's energy efficiency data in real time and provide feedback to the energy efficiency optimization module for adaptive adjustments. This ensures that the entire system maintains optimal energy consumption management during long-term operation, thereby improving the long-term stability and economy of the well logging dual-chamber furnace.

[0227] By working together with the evaluation module and the enhanced thermal field control module, the evaluation module can optimize the thermal field control strategy based on temperature distribution data, and make temperature regulation more precise. This ensures that the entire system can maintain efficient heating and cooling control under various operating conditions, and improves the processing consistency of logging tools.

[0228] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A logging dual-chamber furnace main control and safety auxiliary system based on numerical control technology, characterized in that, The well logging dual-chamber furnace main control and safety auxiliary system includes a well logging dual-chamber furnace, an enhanced thermal field control module, a cooling module, and an energy efficiency optimization module. The enhanced thermal field control module and the cooling module are respectively installed in the well logging dual-chamber furnace. The enhanced thermal field control module collects operating data from the well logging dual-chamber furnace, analyzes the operating status of the furnace based on the data to generate analysis results, and controls the thermal field data of the furnace based on these results. The cooling module uses supercritical fluid cooling technology to rapidly cool different areas, forming a gradient cooling distribution. The energy efficiency optimization module dynamically adjusts energy efficiency based on the analysis results. The energy efficiency optimization module includes a heating power control unit, a long-term cooling flow rate optimization unit, an intelligent energy efficiency calculation unit, and an energy consumption assessment and prediction unit. The heating power control unit will... The analysis results are compared with the set monitoring thresholds. If the thresholds are exceeded, the power output of each heating unit is calculated, and the results are transmitted to the intelligent energy efficiency calculation unit. The long-term cooling flow rate optimization unit calculates the cooling flow rate of different cooling zones based on historical data and the long-term optimization results of the energy consumption assessment and prediction unit, and transmits the optimized cooling flow rate to the intelligent energy efficiency calculation unit and the heating power control unit to coordinate and optimize temperature control. The intelligent energy efficiency calculation unit receives the power output of the heating power control unit and the cooling flow rate of the long-term cooling flow rate optimization unit, calculates the real-time energy efficiency ratio of the well logging dual-chamber furnace, and transmits the real-time energy efficiency ratio to the energy consumption assessment and prediction unit. The energy consumption assessment and prediction unit dynamically adjusts the long-term control strategy of heating power and cooling gradient based on historical data, real-time energy efficiency ratio, and long-term cooling optimization strategy.

2. The well logging dual-chamber furnace main control and safety auxiliary system based on CNC technology according to claim 1, characterized in that, The enhanced thermal field control module includes an intelligent sensing unit, a thermal field analysis unit, and a temperature field optimization unit. The intelligent sensing unit is used to form a distributed temperature monitoring network inside the well logging dual-chamber furnace and monitor the furnace cavity temperature distribution data in real time. The thermal field analysis unit analyzes the operating status of the well logging dual-chamber furnace based on the furnace cavity temperature distribution data and generates analysis results. The temperature field optimization unit dynamically adjusts the heating power distribution of the well logging dual-chamber furnace based on the analysis results to make the furnace cavity temperature distribution more uniform.

3. The well logging dual-chamber furnace main control and safety auxiliary system based on CNC technology according to claim 2, characterized in that, The cooling module includes a supercritical cooling unit, a microchannel fluid distribution unit, an intelligent cooling control unit, and a cooling performance monitoring unit. The supercritical cooling unit uses supercritical fluid as the cooling medium to cool the cooling areas in the logging dual-chamber furnace, thereby achieving temperature gradient control in different areas. The microchannel fluid distribution unit lays out a high-density microchannel cooling pipe network inside the logging dual-chamber furnace. The cooling performance monitoring unit monitors the cooling data of the cooling fluid, and the intelligent cooling control unit adjusts the supercritical fluid flow rate in different areas based on the cooling data.

4. The well logging dual-chamber furnace main control and safety auxiliary system based on CNC technology according to claim 3, characterized in that, The thermal field analysis unit calculates the operating status index I of the logging dual-chamber furnace according to the following formula. R : ； In the formula, S T η is the thermal stability coefficient of the furnace cavity. E S is the thermal energy utilization efficiency coefficient. C R is the cooling uniformity coefficient. H ε is the thermal stress risk factor, and ε is the equilibrium parameter, which is a very small number greater than 0.

5. The well logging dual-chamber furnace main control and safety auxiliary system based on CNC technology according to claim 4, characterized in that, The intelligent energy efficiency calculation unit calculates the real-time energy efficiency ratio (COP) according to the following formula: ； In the formula, M represents the number of independent electric heating elements used for heating in the well logging dual-chamber furnace, and Q... useful,i P represents the effective heat actually transferred to the furnace cavity by the i-th heating unit. heat,i Δt represents the electrical energy consumed by the heating element of the logging dual-chamber furnace per unit time, and Δt represents the evaluation cycle time.

6. The well logging dual-chamber furnace main control and safety auxiliary system based on CNC technology according to claim 5, characterized in that, The intelligent sensing unit collects operational data including temperature data, heating data, cooling data, and energy consumption data.

7. The well logging dual-chamber furnace main control and safety auxiliary system based on CNC technology according to claim 6, characterized in that, The intelligent sensing unit includes a carbon nanotube temperature sensor array, an infrared thermal imaging sensor, an intelligent power meter, a flow meter, and a pressure sensor. The carbon nanotube temperature sensor array is deployed inside the well logging dual-chamber furnace cavity and measures the internal temperature of the furnace cavity. The infrared thermal imaging sensor is positioned directly opposite the well logging dual-chamber furnace cavity and remotely measures the furnace wall and thermal field distribution. The intelligent power meter collects the power of the heating elements in each heating zone inside the well logging dual-chamber furnace cavity, the flow meter detects the flow rate of the cooling pipe network of the cooling module, and the pressure sensor detects the pressure of the cooling pipe network of the cooling module.

8. The well logging dual-chamber furnace main control and safety auxiliary system based on numerical control technology according to any one of claims 3 or 7, characterized in that, The supercritical fluid is set to H2O.

9. The well logging dual-chamber furnace main control and safety auxiliary system based on CNC technology according to claim 8, characterized in that, The intelligent cooling control unit acquires the current cooling data and calculates the local flow velocity v according to the following formula. cool,i : ； In the formula, v base The reference flow rate is T, α is the temperature deviation correction factor, and T is the reference flow rate. i -T target This refers to temperature deviation.

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