Intelligent regulation and control method and system for tunnel concrete spraying

Abstract

The invention discloses an intelligent regulation and control method and system for tunnel concrete spraying. The method comprises the steps that key influence factors of the adhesion and solidification process of concrete are determined; according to the determined adhesion and solidification process influence factors, material proportioning parameters are optimized in combination with cement grout fluidity and additive effect change characteristics, and based on an optimized proportioning scheme, the requirements of construction area differences for injection parameter adjustment are analyzed, and pressure and flow set values of injection equipment are determined; according to the determined pressure and flow set values, injection parameter adjusting instructions for different areas are generated, equipment operation configuration suitable for current temperature distribution is obtained, and a corrected operation instruction is generated; and the corrected operation instruction is transmitted to a spraying equipment control system, the equipment operation state is adjusted in real time, meanwhile, adjusted concrete solidification process control data are collected, and whether the final performance parameters meet the construction requirements or not is determined. The construction quality and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building construction, and in particular to an intelligent control method and system for tunnel concrete spraying. Background Art

[0002] Tunnel concrete spraying technology, a crucial component of infrastructure construction, plays an irreplaceable role in ensuring the safety and durability of tunnel projects. Research in this field is directly related to project quality and construction efficiency. The application of intelligent control technologies is particularly crucial under complex geological and climatic conditions. Tunnel projects often face diverse environmental challenges, and concrete spraying, as a core process, has a direct impact on the stability and service life of the structure.

[0003] However, existing methods often lack the ability to cope with environmental fluctuations. Many technologies focus primarily on the mechanical properties of construction equipment or the optimization of single material ratios, overlooking the dynamic interaction between the construction environment and material properties. In tunnel environments, where temperature fluctuations are significant, traditional control methods struggle to adapt to temperature variations between different areas, leading to instability in concrete properties and, in turn, compromising construction results and project quality.

[0004] Against this backdrop, intelligent control of tunnel concrete spraying faces significant technical challenges. Ambient temperature is a key factor influencing concrete performance, and its fluctuations can directly interfere with the material's adhesion and setting process. Failure to adjust spraying parameters in real time based on temperature distribution can lead to premature setting or insufficient strength in certain areas. Furthermore, because temperature fluctuations affect the fluidity of the cement slurry and the effectiveness of additives, a single mix ratio or pressure setting is unlikely to meet the construction requirements of different locations, leading to fluctuations in overall project quality.

[0005] Therefore, how to dynamically adjust the spraying parameters and material ratios based on the real-time changes in ambient temperature during the tunnel concrete spraying process to ensure the stable performance of concrete under different temperature difference conditions has become a key issue that needs to be urgently addressed in this study. Summary of the Invention

[0006] In order to solve the technical problems existing in the above-mentioned prior art, the present invention proposes an intelligent control method and system for tunnel concrete spraying to achieve long-term stable control of concrete performance during tunnel construction.

[0007] On the one hand, to achieve the above-mentioned purpose, the present invention provides a method for intelligent control of tunnel concrete spraying, comprising:

[0008] Acquire real-time data on ambient temperature changes in the tunnel construction area to obtain temperature distribution characteristic values, and determine key influencing factors of concrete adhesion and solidification process based on the temperature distribution characteristic values;

[0009] Based on the determined factors affecting adhesion and solidification, combined with the characteristics of cement slurry fluidity and the changing effects of additives, the material ratio parameters are optimized to obtain an optimized ratio scheme. Based on the optimized ratio scheme, the need for injection parameter adjustment due to differences in construction areas is analyzed to determine the pressure and flow setting values ​​of the injection equipment;

[0010] Based on the determined pressure and flow setpoints, injection parameter adjustment instructions are generated for different areas to obtain the equipment operating configuration suitable for the current temperature distribution. The system also continuously verifies whether the performance of the concrete after the optimized material ratio meets the expectations. If the performance of a certain area deviates from the preset standard, the parameter fine-tuning mechanism is triggered and the corrected operating instructions are generated.

[0011] The corrected operating instructions are transmitted to the spraying equipment control system to adjust the equipment operating status in real time. At the same time, the adjusted concrete solidification process control data is collected to determine whether the final performance parameters meet the construction requirements.

[0012] On the other hand, to achieve the above-mentioned purpose, the present invention also provides a tunnel concrete spraying intelligent control system, comprising:

[0013] A characteristic analysis module is used to obtain real-time data on ambient temperature changes in the tunnel construction area, obtain temperature distribution characteristic values, and determine key influencing factors of concrete adhesion and solidification process based on the temperature distribution characteristic values;

[0014] The injection parameter decision module is used to optimize the material ratio parameters based on the determined factors affecting the adhesion and solidification process, combined with the characteristics of cement slurry fluidity and the changing effects of additives, to obtain an optimized ratio scheme. Based on the optimized ratio scheme, the module analyzes the demand for injection parameter adjustment due to differences in construction areas and determines the pressure and flow setting values ​​of the injection equipment;

[0015] The performance verification module is used to generate injection parameter adjustment instructions for different areas based on the determined pressure and flow set points, obtain the equipment operating configuration suitable for the current temperature distribution, and continuously verify whether the concrete performance after the optimized material ratio meets the expected performance. If the performance of a certain area deviates from the preset standard, the parameter fine-tuning mechanism is triggered and the corrected operating instructions are generated;

[0016] The cycle control module is used to transmit the corrected operating instructions to the spraying equipment control system, adjust the equipment operating status in real time, and collect the adjusted concrete solidification process control data to determine whether the final performance parameters meet the construction requirements.

[0017] Compared with the prior art, the present invention has the following advantages and technical effects:

[0018] This invention deploys a multi-point sensor network throughout the construction area to monitor ambient temperature distribution in real time and analyze its potential impact on concrete performance. Integrating this real-time data, the invention dynamically adjusts the pressure and flow parameters of the injection equipment, generating an operating configuration tailored to the current temperature distribution. Through continuous verification and fine-tuning, this ensures that concrete performance meets expected standards. This invention achieves long-term, stable control of concrete performance during tunnel construction, improving construction quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0020] Figure 1 This is a flow chart of an intelligent control method for tunnel concrete spraying according to an embodiment of the present invention;

[0021] Figure 2 The figure is a schematic structural diagram of an intelligent control system for tunnel concrete spraying according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0024] This embodiment proposes a tunnel concrete spraying intelligent control method, such as Figure 1 ,include:

[0025] Acquire real-time data on ambient temperature changes in the tunnel construction area to obtain temperature distribution characteristic values, and determine key influencing factors of concrete adhesion and solidification process based on the temperature distribution characteristic values;

[0026] Based on the determined factors affecting adhesion and solidification, combined with the characteristics of cement slurry fluidity and the changing effects of additives, the material ratio parameters are optimized to obtain an optimized ratio scheme. Based on the optimized ratio scheme, the need for injection parameter adjustment due to differences in construction areas is analyzed to determine the pressure and flow setting values ​​of the injection equipment;

[0027] Based on the determined pressure and flow setpoints, injection parameter adjustment instructions are generated for different areas to obtain the equipment operating configuration suitable for the current temperature distribution. The system also continuously verifies whether the performance of the concrete after the optimized material ratio meets the expectations. If the performance of a certain area deviates from the preset standard, the parameter fine-tuning mechanism is triggered and the corrected operating instructions are generated.

[0028] The corrected operating instructions are transmitted to the spraying equipment control system to adjust the equipment operating status in real time. At the same time, the adjusted concrete solidification process control data is collected to determine whether the final performance parameters meet the construction requirements.

[0029] Furthermore, the temperature distribution characteristic values ​​of each region are obtained, including:

[0030] By deploying a multi-point sensor network within the tunnel construction area, real-time ambient temperature data is acquired to determine the initial temperature distribution of each area. Based on this initial temperature distribution, a time series analysis method is used to process the real-time collected temperature data to determine the temperature change trend of each area over time.

[0031] Based on the temperature change trend over time, the preset temperature range threshold is used for comparison. If the temperature change in a certain area exceeds the threshold range, the area is marked as an abnormal area. By deeply analyzing the temperature data of the abnormal area, the temperature distribution characteristic value of the abnormal area is obtained.

[0032] Specifically, for time series analysis methods that process real-time temperature data, the temperature trend of a certain area within 24 hours can be analyzed by recording the temperature changes every hour. Suppose the initial temperature in a certain area is 22 degrees, it rises to 25 degrees after 6 hours, and reaches 27 degrees after 12 hours, showing a continuous upward trend. This analysis helps to capture the pattern of temperature changes and provide data support for abnormal judgment. In the preset threshold comparison, if the normal temperature change range is set to 2 degrees, when the temperature in a certain area rises from 22 degrees to 26 degrees, it exceeds the threshold range and is marked as an abnormal area.

[0033] Furthermore, key factors influencing the concrete adhesion and setting process were identified, including:

[0034] Processing the temperature distribution eigenvalues, using a pre-established feature extraction method to separate key temperature change patterns, and obtaining preliminary eigenvalue analysis results;

[0035] Based on the eigenvalue analysis results and combined with the pre-built performance mapping model, the potential impact of temperature changes on concrete properties is analyzed, and the key influencing factors of concrete adhesion and solidification process are determined.

[0036] Specifically, when processing the collected temperature distribution characteristic values, the data can be cleaned and normalized to ensure accuracy and consistency. For feature extraction, time series decomposition can be used to separate temperature change data into three components: trend, cycle, and random fluctuation, allowing for the initial extraction of key temperature change patterns. For example, if the temperature data for a certain area shows a clear upward trend over 24 hours, rising from an initial 21.5 degrees Celsius to 24.8 degrees Celsius, this trend characteristic value can serve as the basis for subsequent analysis.

[0037] When analyzing the potential impact of temperature changes on concrete performance using a performance mapping model, a pre-built mapping table based on historical data can be used to correlate temperature changes with indicators such as concrete strength and adhesion. For example, if temperatures in a certain area consistently exceed 23°C, which could lead to a decrease in concrete adhesion, model analysis can identify temperature as a key variable influencing performance stability. This analysis helps identify key parameters that require attention.

[0038] Furthermore, the optimized ratio scheme is obtained, including:

[0039] According to the temperature distribution characteristic value, the fluidity data of the cement slurry and the usage of the additive in the corresponding area are obtained to determine the correlation characteristics between the current material ratio and the temperature anomaly;

[0040] Based on the determined correlation characteristics, the support vector machine model was used to classify the factors affecting adhesion performance and solidification process, and the priority ranking results of each factor on performance were obtained;

[0041] If the priority ranking results show that a certain influencing factor has a greater impact on the adhesion performance, the corresponding adjustment parameters are extracted from the pre-established material ratio database to obtain a preliminary ratio adjustment plan;

[0042] Based on the preliminary mix ratio adjustment plan, simulate and analyze the regional temperature data and fluidity data to determine whether the adjustment plan meets the stability requirements of the solidification process and determine the final optimized mix ratio parameters;

[0043] By matching the final optimized ratio parameters with the characteristics of the additives, dynamic control instructions are generated for temperature abnormality areas, and material ratio solutions suitable for different areas are obtained;

[0044] According to the generated dynamic control instructions, the parameters of the material ratio are automatically updated, the adhesion performance and solidification process are optimized and adjusted, and the optimized ratio scheme is obtained.

[0045] Specifically, when using a support vector machine model to classify factors affecting adhesion and solidification, temperature, additive dosage, and fluidity are used as input variables. The model output shows that temperature has the highest priority for affecting adhesion, followed by additive dosage. This ranking indicates that the first priority is to address temperature anomalies, which can then be adjusted based on the material ratio.

[0046] If the priority ranking indicates that temperature has a significant impact on adhesion performance, extracting adjustment parameters from the pre-established material ratio database may reveal a record suggesting increasing the additive dosage to 3.2 kg per cubic meter when the temperature exceeds 25°C, forming a preliminary ratio adjustment plan. This plan, based on historical data, provides a certain degree of reliability. A simulation analysis of this preliminary ratio adjustment plan can be conducted, combining the current data of a regional temperature of 26.5°C and a fluidity of 18 cm. The simulation will examine whether the adjusted fluidity can be restored to above 20 cm and whether the setting time remains within the expected range. Assuming the simulation results show that the adjusted stability requirements are met, the final optimized ratio parameter is determined to be 3.2 kg per cubic meter. This simulation analysis ensures the feasibility of the plan.

[0047] Further, determining the pressure and flow setting values ​​of the injection equipment includes:

[0048] By obtaining real-time data from the on-site monitoring system and combining it with the pre-established database of mix ratios, we can compare the data and obtain information on the distribution of different characteristics between construction areas.

[0049] Based on the difference characteristic distribution information, a preset difference distribution range is used for analysis. If the difference characteristic of a certain area exceeds the preset difference distribution range, the corresponding injection parameter adjustment requirement is extracted to determine a preliminary adjustment direction.

[0050] Based on the preliminary adjustment direction, the specific environmental data of each area is obtained from the regional division information. Combined with the difference characteristic distribution information, the matching degree of the pressure setting and the flow setting is determined to obtain the adaptive parameter range;

[0051] Connect the adapted parameter range with the equipment control system to obtain the current equipment operating status data, analyze whether there is a parameter conflict, and determine the pressure and flow setting values ​​of the injection equipment.

[0052] Specifically, suppose the humidity data for a certain area is 85%, while the preset threshold range is 70% to 80%, significantly exceeding the upper limit. By comparing it with the mix solution database, differential characteristic distribution information is generated, marking this area as a region of abnormal humidity, concentrated in the deep tunnel, where ventilation is poor. This method helps quickly identify problem areas and provides a basis for subsequent adjustments. For example, when analyzing the differential characteristic distribution information using the preset threshold range, it can be found that the abnormal humidity area may affect the adhesion of the sprayed material. The initial adjustment direction is to reduce the spray flow rate to reduce moisture accumulation while increasing the pressure to ensure spray uniformity. Combined with the regional division information, the wind speed data obtained in this area is 1.2 meters per second, far lower than the normal range of 2.5 meters per second. It is determined that the current pressure and flow settings are poorly matched. The adaptation parameter range should be increased to 5.5 bar and reduced to 3.0 liters per minute. This analysis provides a clear direction for equipment adjustments.

[0053] When connecting the adaptation parameter range to the equipment control system, the current equipment operating status data was obtained and it was found that the pressure was 4.8 bar and the flow rate was 3.5 liters / minute, which conflicted with the adaptation range. By analyzing the equipment load, it was determined that the final pressure setting was 5.3 bar and the flow rate was 3.2 liters / minute to avoid equipment overload. This parameter adjustment ensures the stability of equipment operation. For example, after generating equipment control instructions for each area, combined with real-time data verification, it was found that the adhesion effect of the humidity abnormality area improved after adjustment, but further monitoring is still required. The optimized control scheme matches the pressure and flow setting values ​​with the regional characteristics to ensure that the instructions meet actual needs. This verification method improves the applicability of the scheme.

[0054] Furthermore, obtaining a device operating configuration suitable for the current temperature distribution includes:

[0055] Obtain the current pressure setting and flow setting data, combine them with temperature distribution information, analyze the environmental characteristics of each area, and determine the preliminary injection parameter range;

[0056] Based on the preliminary injection parameter range, the tunnel control algorithm is called to dynamically generate adjustment instructions for different areas to obtain instruction sets for different areas;

[0057] If there is a deviation between the generated instruction set and the current equipment operating status, the adaptation degree of the injection parameters is adjusted by comparing the temperature distribution and area division information to obtain a corrected instruction set;

[0058] Based on the revised instruction set and combined with the equipment operation configuration data, the parameter adaptation of each area is analyzed to determine whether the temperature distribution requirements are met and the final adjustment instructions are determined;

[0059] Based on the final adjustment instructions, a control signal for the equipment operation is generated. By connecting with the equipment control system, the parameters are sent down to obtain the equipment operation configuration suitable for the current temperature distribution.

[0060] Specifically, when calling the tunnel control algorithm to generate dynamic adjustment instructions, for high-temperature areas, the algorithm generates an instruction set based on the temperature distribution and area division information, recommending that the pressure be adjusted to 4.6 bar and the flow rate be reduced to 3.4 liters / minute. However, by comparing the current equipment operating status, it was found that the actual pressure was only 4.3 bar, which was a deviation. Therefore, the degree of adaptation was further adjusted in combination with the temperature distribution data. The revised instruction set set the pressure to 4.5 bar and the flow rate to 3.5 liters / minute to ensure smooth operation of the equipment. For example, when analyzing the adaptation of the revised instruction set and the equipment operation configuration data, for high-temperature areas, the equipment load was checked and it was found that the current configuration supported an upper pressure limit of 4.7 bar and a lower flow limit of 3.3 liters / minute. The revised instruction was within the equipment capacity range. At the same time, combined with the temperature distribution requirements, it was determined that the parameter adjustment could effectively deal with the curing problem caused by high temperature, and the final adjustment instruction was determined to be a pressure of 4.5 bar and a flow rate of 3.5 liters / minute.

[0061] When the control signal is generated and connected to the device control system, the final adjustment instruction is sent to the device to complete the parameter update. The updated operating status information shows that the pressure is stable at 4.5 bar and the flow rate is 3.5 liters / minute, indicating that the device is operating normally. This method ensures the accurate execution of parameter adjustments.

[0062] Furthermore, generating the modified operation instruction includes:

[0063] By obtaining current parameter data from the equipment operation configuration and combining it with temperature distribution monitoring data, the operating status of each area is analyzed in real time to determine the preliminary assessment results of concrete performance;

[0064] Based on the preliminary assessment results, the degree of matching of the concrete performance of each area with the preset standard is compared. If the performance data of a certain area deviates from the preset standard, the specific deviation index is extracted to obtain detailed deviation detection data;

[0065] Based on the detailed data of deviation detection, a pre-established performance analysis model is used to calculate the relationship between deviation index and temperature distribution, and determine the specific influencing factors of regional performance deviation;

[0066] Extract key information related to material ratio from influencing factors, combine it with the optimization and adjustment logic, make targeted corrections to the material ratio parameters, and obtain the adjusted ratio plan;

[0067] According to the adjusted ratio scheme, the corresponding parameter fine-tuning instructions are generated. Combined with the current status of the equipment operation configuration, the executability of the fine-tuning instructions is verified and the revised operation instructions are determined.

[0068] Specifically, a comparison of the concrete's performance against pre-set standards revealed that the strength data in the high-temperature region deviated from the standard by approximately 10%. Information processing extracted the deviation indicators, revealing that the curing time was shortened to 30 minutes, compared to the standard of 35 minutes. Further analysis of the relationship between temperature distribution and curing time revealed that high temperature was the primary influencing factor.

[0069] For example, when using the performance analysis model to calculate the relationship between the deviation index and the temperature distribution, it can be determined by comparing historical data that for every 5 degrees Celsius increase in temperature, the curing time may be shortened by about 3 minutes. Combined with the current environment of 30 degrees Celsius, it is confirmed that high temperature is the key factor causing performance deviation. From this, information related to the material ratio is extracted, and it is found that the current water-cement ratio is too high, which may aggravate the problem of rapid curing under high temperature. Regarding the correction of the material ratio parameters, according to the optimization adjustment logic, it is recommended to adjust the water-cement ratio from 0.5 to 0.45 to slow down the curing speed. Combined with the equipment operation configuration, a fine-tuning instruction is generated to verify that the equipment supports the ratio adjustment, confirm the executableness of the instruction, and finally determine the operation instruction to adjust the ratio to 0.45.

[0070] Furthermore, the corrected operating instructions are transmitted to the spraying equipment control system to adjust the equipment operating status in real time. At the same time, the adjusted concrete solidification process control data is collected to determine whether the final performance parameters meet the construction requirements, including:

[0071] By transmitting the corrected operating instructions to the injection equipment control system, the operating status of the injection equipment is adjusted and relevant data on the equipment status change is obtained;

[0072] Based on the relevant data of the equipment status change, the preset deviation range is used for comparison. If the status is detected to deviate from the preset range, the adjustment mechanism is triggered and the correction instruction is regenerated;

[0073] The correction instructions generated by the adjustment mechanism are transmitted to the spraying equipment, and the equipment operating parameters are updated in real time to obtain dynamic data during the concrete spraying process;

[0074] Based on the dynamic data of the concrete spraying process and the key indicators of concrete solidification, the support vector machine algorithm is used to predict the solidification process and determine the solidification trend;

[0075] The prediction results of solidification trends are used to obtain key parameters for process control. If the key parameters exceed the preset standards, a feedback signal is generated to adjust the operating instructions of the injection equipment.

[0076] According to the operation instructions adjusted by the feedback signal, the final concrete solidification data is collected to determine whether the final performance meets the construction standards. Based on the final performance judgment result, the adjusted control data is generated and stored for optimization and adjustment of subsequent operation instructions.

[0077] Specifically, when predicting the concrete setting process, the support vector machine algorithm can perform trend analysis based on historical setting data and current environmental parameters. Assuming the current ambient temperature is 28 degrees Celsius and the humidity is 60%, the algorithm predicts that the setting time may be 32 minutes, while the standard value is 35 minutes. This prediction provides a reference for early adjustments, reducing the possibility of performance deviations. When adjusting key parameters based on the setting trend, if the predicted setting time is too short, the system will generate a feedback signal, recommending reducing the injection flow rate to 3.0 liters / minute to slow the setting rate. Such adjustments can effectively avoid insufficient strength caused by excessively rapid setting, ensuring construction quality.

[0078] When collecting the final solidification data and judging whether the performance meets the standards, the effect can be verified by testing the compressive strength of the concrete test block. Assuming that the standard value is 30 MPa, the actual test value is 29.5 MPa, which basically meets the requirements, but can still be further optimized by fine-tuning the parameters. This verification method provides data support for subsequent construction. For the operation of storing the adjusted control data in the database, the pressure, flow rate and corresponding solidification time and other parameters of each adjustment can be recorded to form a historical data set. Assuming that a certain adjustment reduces the flow rate from 3.2 liters / minute to 3.0 liters / minute and the solidification time is extended by 2 minutes, these data can provide a reference for similar scenarios in the future and improve the accuracy of instruction optimization.

[0079] This embodiment also provides a tunnel concrete spraying intelligent control system, such as Figure 2 ,include:

[0080] A characteristic analysis module is used to obtain real-time data on ambient temperature changes in the tunnel construction area, obtain temperature distribution characteristic values, and determine key influencing factors of concrete adhesion and solidification process based on the temperature distribution characteristic values;

[0081] The injection parameter decision module is used to optimize the material ratio parameters based on the determined factors affecting the adhesion and solidification process, combined with the characteristics of cement slurry fluidity and the changing effects of additives, to obtain an optimized ratio scheme. Based on the optimized ratio scheme, the module analyzes the demand for injection parameter adjustment due to differences in construction areas and determines the pressure and flow setting values ​​of the injection equipment;

[0082] The performance verification module is used to generate injection parameter adjustment instructions for different areas based on the determined pressure and flow set points, obtain the equipment operating configuration suitable for the current temperature distribution, and continuously verify whether the concrete performance after the optimized material ratio meets the expected performance. If the performance of a certain area deviates from the preset standard, the parameter fine-tuning mechanism is triggered and the corrected operating instructions are generated;

[0083] The cycle control module is used to transmit the corrected operating instructions to the spraying equipment control system, adjust the equipment operating status in real time, and collect the adjusted concrete solidification process control data to determine whether the final performance parameters meet the construction requirements.

[0084] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A tunnel concrete spraying intelligent control method, characterized in that: include: Acquire real-time data on ambient temperature changes in the tunnel construction area to obtain temperature distribution characteristic values, and determine key influencing factors of concrete adhesion and solidification process based on the temperature distribution characteristic values; Based on the determined factors affecting adhesion and solidification, combined with the characteristics of cement slurry fluidity and the changing effects of additives, the material ratio parameters are optimized to obtain an optimized ratio scheme. Based on the optimized ratio scheme, the need for injection parameter adjustment due to differences in construction areas is analyzed to determine the pressure and flow setting values ​​of the injection equipment; Based on the determined pressure and flow setpoints, injection parameter adjustment instructions are generated for different areas to obtain the equipment operating configuration suitable for the current temperature distribution. The system also continuously verifies whether the performance of the concrete after the optimized material ratio meets the expectations. If the performance of a certain area deviates from the preset standard, the parameter fine-tuning mechanism is triggered and the corrected operating instructions are generated. The corrected operating instructions are transmitted to the spraying equipment control system to adjust the equipment operating status in real time. At the same time, the adjusted concrete solidification process control data is collected to determine whether the final performance parameters meet the construction requirements.

2. The intelligent control method for tunnel concrete spraying according to claim 1 is characterized in that: The temperature distribution characteristic values ​​of each region are obtained, including: By deploying a multi-point sensor network within the tunnel construction area, real-time ambient temperature data is acquired to determine the initial temperature distribution of each area. Based on this initial temperature distribution, a time series analysis method is used to process the real-time collected temperature data to determine the temperature change trend of each area over time. Based on the temperature change trend over time, the preset temperature range threshold is used for comparison. If the temperature change in a certain area exceeds the threshold range, the area is marked as an abnormal area. By deeply analyzing the temperature data of the abnormal area, the temperature distribution characteristic value of the abnormal area is obtained.

3. The intelligent control method for tunnel concrete spraying according to claim 2 is characterized in that: Determine the key factors affecting concrete adhesion and setting, including: Processing the temperature distribution eigenvalues, using a pre-established feature extraction method to separate key temperature change patterns, and obtaining preliminary eigenvalue analysis results; Based on the eigenvalue analysis results and combined with the pre-built performance mapping model, the potential impact of temperature changes on concrete properties is analyzed, and the key influencing factors of concrete adhesion and solidification process are determined.

4. The intelligent control method for tunnel concrete spraying according to claim 3 is characterized in that: Obtaining the optimized ratio scheme includes: According to the temperature distribution characteristic value, the fluidity data of the cement slurry and the usage of the additive in the corresponding area are obtained to determine the correlation characteristics between the current material ratio and the temperature anomaly; Based on the determined correlation characteristics, the support vector machine model was used to classify the factors affecting adhesion performance and solidification process, and the priority ranking results of each factor on performance were obtained; If the priority ranking results show that a certain influencing factor has a greater impact on the adhesion performance, the corresponding adjustment parameters are extracted from the pre-established material ratio database to obtain a preliminary ratio adjustment plan; Based on the preliminary mix ratio adjustment plan, simulate and analyze the regional temperature data and fluidity data to determine whether the adjustment plan meets the stability requirements of the solidification process and determine the final optimized mix ratio parameters; By matching the final optimized ratio parameters with the characteristics of the additives, dynamic control instructions are generated for temperature abnormality areas, and material ratio solutions suitable for different areas are obtained; According to the generated dynamic control instructions, the parameters of the material ratio are automatically updated, the adhesion performance and solidification process are optimized and adjusted, and the optimized ratio scheme is obtained.

5. The intelligent control method for tunnel concrete spraying according to claim 1 is characterized in that: Determine pressure and flow settings for injection equipment, including: By obtaining real-time data from the on-site monitoring system and combining it with the pre-established database of mix ratios, we can compare the data and obtain information on the distribution of different characteristics between construction areas. Based on the difference characteristic distribution information, a preset difference distribution range is used for analysis. If the difference characteristic of a certain area exceeds the preset difference distribution range, the corresponding injection parameter adjustment requirement is extracted to determine a preliminary adjustment direction. Based on the preliminary adjustment direction, the specific environmental data of each area is obtained from the regional division information. Combined with the difference characteristic distribution information, the matching degree of the pressure setting and the flow setting is determined to obtain the adaptive parameter range; Connect the adapted parameter range with the equipment control system to obtain the current equipment operating status data, analyze whether there is a parameter conflict, and determine the pressure and flow setting values ​​of the injection equipment.

6. The intelligent control method for tunnel concrete spraying according to claim 1 is characterized in that: Obtain the device operating configuration appropriate for the current temperature distribution, including: Obtain the current pressure setting and flow setting data, combine them with temperature distribution information, analyze the environmental characteristics of each area, and determine the preliminary injection parameter range; Based on the preliminary injection parameter range, the tunnel control algorithm is called to dynamically generate adjustment instructions for different areas to obtain instruction sets for different areas; If there is a deviation between the generated instruction set and the current equipment operating status, the adaptation degree of the injection parameters is adjusted by comparing the temperature distribution and area division information to obtain a corrected instruction set; Based on the revised instruction set and combined with the equipment operation configuration data, the parameter adaptation of each area is analyzed to determine whether the temperature distribution requirements are met and the final adjustment instructions are determined; Based on the final adjustment instructions, a control signal for the equipment operation is generated. By connecting with the equipment control system, the parameters are sent down to obtain the equipment operation configuration suitable for the current temperature distribution.

7. The intelligent control method for tunnel concrete spraying according to claim 1, characterized in that: Generating the modified operation instruction includes: By obtaining current parameter data from the equipment operation configuration and combining it with temperature distribution monitoring data, the operating status of each area is analyzed in real time to determine the preliminary assessment results of concrete performance; Based on the preliminary assessment results, the degree of matching of the concrete performance of each area with the preset standard is compared. If the performance data of a certain area deviates from the preset standard, the specific deviation index is extracted to obtain detailed deviation detection data; Based on the detailed data of deviation detection, a pre-established performance analysis model is used to calculate the relationship between deviation index and temperature distribution, and determine the specific influencing factors of regional performance deviation; Extract key information related to material ratio from influencing factors, combine it with the optimization and adjustment logic, make targeted corrections to the material ratio parameters, and obtain the adjusted ratio plan; According to the adjusted ratio scheme, the corresponding parameter fine-tuning instructions are generated. Combined with the current status of the equipment operation configuration, the executability of the fine-tuning instructions is verified and the revised operation instructions are determined.

8. The intelligent control method for tunnel concrete spraying according to claim 1 is characterized in that: The corrected operating instructions are transmitted to the spraying equipment control system to adjust the equipment operating status in real time. At the same time, the adjusted concrete solidification process control data is collected to determine whether the final performance parameters meet the construction requirements, including: By transmitting the corrected operating instructions to the injection equipment control system, the operating status of the injection equipment is adjusted and relevant data on the equipment status change is obtained; Based on the relevant data of the equipment status change, the preset deviation range is used for comparison. If the status is detected to deviate from the preset range, the adjustment mechanism is triggered and the correction instruction is regenerated; The correction instructions generated by the adjustment mechanism are transmitted to the spraying equipment, and the equipment operating parameters are updated in real time to obtain dynamic data during the concrete spraying process; Based on the dynamic data of the concrete spraying process and the key indicators of concrete solidification, the support vector machine algorithm is used to predict the solidification process and determine the solidification trend; The prediction results of solidification trends are used to obtain key parameters for process control. If the key parameters exceed the preset standards, a feedback signal is generated to adjust the operating instructions of the injection equipment. According to the operation instructions adjusted by the feedback signal, the final concrete solidification data is collected to determine whether the final performance meets the construction standards. Based on the final performance judgment result, the adjusted control data is generated and stored for optimization and adjustment of subsequent operation instructions.

9. An intelligent control system for tunnel concrete spraying, characterized in that: include: A characteristic analysis module is used to obtain real-time data on ambient temperature changes in the tunnel construction area, obtain temperature distribution characteristic values, and determine key influencing factors of concrete adhesion and solidification process based on the temperature distribution characteristic values; The injection parameter decision module is used to optimize the material ratio parameters based on the determined factors affecting the adhesion and solidification process, combined with the characteristics of cement slurry fluidity and the changing effects of additives, to obtain an optimized ratio scheme. Based on the optimized ratio scheme, the module analyzes the demand for injection parameter adjustment due to differences in construction areas and determines the pressure and flow setting values ​​of the injection equipment; The performance verification module is used to generate injection parameter adjustment instructions for different areas based on the determined pressure and flow set points, obtain the equipment operating configuration suitable for the current temperature distribution, and continuously verify whether the concrete performance after the optimized material ratio meets the expected performance. If the performance of a certain area deviates from the preset standard, the parameter fine-tuning mechanism is triggered and the corrected operating instructions are generated; The cycle control module is used to transmit the corrected operating instructions to the spraying equipment control system, adjust the equipment operating status in real time, and collect the adjusted concrete solidification process control data to determine whether the final performance parameters meet the construction requirements.

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