Highway engineering intelligent compaction equipment and method based on multi-parameter cooperative control

Abstract

The invention discloses highway engineering intelligent compaction equipment and method based on multi-parameter cooperative control, belongs to the technical field of highway engineering construction, and aims to solve the problems of single parameter control, insufficient real-time feedback and poor adaptability in the prior art. Comprising a sensor module, a central control module, a driving module, an adaptive adjustment module, a man-machine interaction module and a power management module, various working parameters such as pavement compaction degree, vibration frequency, compaction speed, temperature and humidity are monitored in real time through the sensor module, the central control module calculates comprehensive control parameters based on a multi-parameter cooperative control model by combining the weight coefficient of each sensor and real-time data, and the working state of equipment is regulated and controlled. The driving module synchronously adjusts parameters such as the running speed, the compaction force and the vibration frequency, multi-parameter cooperative control is achieved, the compaction effect fluctuation caused by single-parameter control is effectively avoided, and the stable compaction quality is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of highway engineering construction, and specifically relates to a highway engineering intelligent compaction device and method based on multi-parameter collaborative control. BACKGROUND

[0002] The pavement compaction operation in highway engineering is a key link to ensure the quality and performance of the pavement, directly affecting the strength, durability and long-term use effect of the pavement. With the expansion of engineering scale and the improvement of quality requirements, compaction technology is continuously developing and innovating. In the process of modern highway construction, compaction operation needs to cope with various complex soil and weather conditions, so higher requirements are put forward for the precision and efficiency of compaction equipment. In order to achieve higher quality compaction effect, researchers and engineers are exploring more intelligent equipment control technology.

[0003] With the progress of science and technology, intelligent and automatic compaction technology has gradually become a trend to improve the quality and efficiency of highway construction. In recent years, some intelligent compaction equipment based on sensors and automatic control has begun to be applied in the market, but there are still the following shortcomings:

[0004] 1. Single parameter control: Most existing compaction equipment relies on single control mode, such as driving speed, vibration intensity, etc., which cannot comprehensively consider factors such as soil, weather and pavement conditions, resulting in poor stability of compaction effect;

[0005] 2. Insufficient real-time feedback: The monitoring system of existing equipment is mostly single sensor monitoring, lacking comprehensive feedback of multiple compaction-related parameters, making it difficult to achieve accurate adjustment and dynamic optimization, and prone to uneven compaction or overcompaction;

[0006] 3. Poor adaptability: Many intelligent compaction equipment cannot automatically adjust according to different soil and construction environment changes, especially in complex or special geological conditions, it is difficult to provide the best compaction effect, affecting the construction quality.

[0007] Therefore, it is necessary to provide a highway engineering intelligent compaction device and method based on multi-parameter collaborative control to solve the problems of single parameter control, insufficient real-time feedback and poor adaptability in the prior art. SUMMARY

[0008] The purpose of the present application is to provide a highway engineering intelligent compaction device and method based on multi-parameter collaborative control to solve the problems raised in the background art.

[0009] To achieve the above purpose, the present application provides the following technical solution: a highway engineering intelligent compaction device and method based on multi-parameter collaborative control, comprising:

[0010] Sensor module: used for real-time monitoring of pavement compaction, vibration frequency, compaction speed, temperature, humidity and other working parameters, and transmitting the collected data to the central processing unit;

[0011] Central control module: used for receiving real-time data from the sensor module, real-time regulation of the compaction equipment based on the preset algorithm and model, adjusting the working state of the equipment according to the real-time data and working condition requirements;

[0012] Drive module: according to the instruction of the central control unit, adjust the driving speed, compaction force and vibration frequency of the compactor, ensure the coordinated control of various parameters in the compaction process, so as to achieve the best compaction effect;

[0013] Self-adaptive adjustment module: can automatically adjust the working parameters of the compaction equipment according to different soil, climate conditions and construction environment, to ensure the best compaction effect in complex or special environment;

[0014] Man-machine interaction module: used for displaying the working state, monitoring parameters and compaction progress of the equipment, and allowing the operator to manually intervene or adjust the equipment, providing a friendly operation experience;

[0015] Power management module: provides stable power support for the equipment, ensures the stable operation of the equipment in long-time operation.

[0016] It should be noted that the sensor module includes compaction degree sensor, acceleration sensor, temperature and humidity sensor, GPS positioning sensor.

[0017] Further, it should be noted that the central control module adopts a multi-parameter collaborative control model, which includes the following model formula:

[0018]

[0019] In the formula, is the comprehensive control parameter of the compaction equipment, is the weight coefficient of the i-th sensor, is the real-time data of the i-th sensor, and n is the number of sensors.

[0020] Further, it should be noted that the central control module is provided with a processor, a data storage module and a communication interface.

[0021] As a preferred embodiment, the self-adaptive adjustment module of the equipment adjusts the working parameters automatically through the following formula:

[0022]

[0023] In the formula, adjusted device operating parameters, weighting coefficient for the i-th sensor, real-time monitoring data for the i-th sensor, reference target value, adjustment coefficient, n is the number of sensors.

[0024] As a preferred embodiment, the highway engineering intelligent compaction equipment based on multi-parameter collaborative control comprises the following steps:

[0025] S1, sensor data acquisition: In the highway engineering construction site, multiple sensor modules are laid out to monitor real-time soil moisture, soil pressure, soil temperature, vibration frequency, climate temperature, humidity and other related parameters, and the data collected by each sensor module is transmitted to the control unit through wireless network or wired network;

[0026] S2, data processing and analysis: the control unit receives real-time data from the sensor module, and processes and analyzes the data using a multi-parameter collaborative control algorithm, considering the correlation of multiple parameters, to generate comprehensive control parameters for guiding the working state of the intelligent compaction equipment;

[0027] S3, compaction effect optimization: according to the comprehensive control parameters obtained by analysis, the working state of the equipment is adjusted to optimize the compaction effect of the equipment, ensuring uniform compaction of the soil and the required density during construction;

[0028] S4, dynamic adjustment control: during construction, the working state of the environment and the equipment is monitored in real time, the difference between the target parameters and the actual measurement data is compared, and the working parameters of the equipment are dynamically adjusted. According to the real-time feedback data, the collaborative control algorithm automatically adjusts the parameters such as the running speed and vibration intensity of the equipment to meet the construction requirements;

[0029] S5, machine learning optimization: based on the real-time feedback data of the equipment during construction, machine learning algorithm is used to analyze and learn the historical data, optimize the control strategy and working parameters, and gradually improve the adaptability of the equipment to different soil, climate and working environment by learning the rules in the construction process;

[0030] S6, automatically adapt to different construction conditions: in the construction site, according to the real-time acquired soil parameters, climate conditions and other information, the operation mode of the compaction equipment is automatically adjusted to ensure that the equipment can adapt to different soil, different humidity and changing climate conditions, and achieve the best compaction effect;

[0031] S7, intelligent evaluation and feedback: after the construction is completed, the control unit automatically generates a compaction quality evaluation report according to the real-time performance of the equipment during construction;

[0032] S8, continuous optimization and adaptive adjustment: through continuous data monitoring and analysis, the multi-parameter collaborative control algorithm is continuously optimized to enhance the adaptive ability of the equipment, and the control strategy is adjusted in real time according to the specific requirements and construction environment of different engineering projects.

[0033] As a preferred embodiment, the intelligent evaluation and feedback step further comprises data storage and feedback.

[0034] As a preferred embodiment, the continuous optimization and adaptive adjustment step further comprises cross-project experience accumulation.

[0035] Compared with the prior art, the highway engineering intelligent compaction equipment and method based on multi-parameter collaborative control provided by the present application at least has the following beneficial effects:

[0036] (1) Solve the problem of single parameter control and improve the stability of compaction effect: The present scheme monitors multiple working parameters such as road compaction degree, vibration frequency, compaction speed, temperature, humidity, etc. in real time through the sensor module, and the central control module calculates the comprehensive control parameter based on the multi-parameter collaborative control model, combines the weight coefficient of each sensor and the real-time data to control the equipment working state, and drives the module to adjust the parameters such as driving speed, compaction force and vibration frequency, etc. to realize multi-parameter collaborative control, effectively avoid the compaction effect fluctuation caused by single parameter control, and ensure the stability of compaction quality.

[0037] (2) Make up for the defect of insufficient real-time feedback and reduce abnormal compaction situations: In the present scheme, the sensor module continuously collects multi-dimensional real-time data and transmits them to the central control module, the control unit processes and analyzes the data through the multi-parameter collaborative control algorithm to generate comprehensive control parameters to guide the equipment working, and at the same time, in the construction process, the dynamic adjustment control step monitors the environment and equipment state in real time, compares the differences between the target and actual data, automatically adjusts the equipment parameters, forms a complete real-time feedback and dynamic adjustment closed loop, and greatly reduces the probability of abnormal situations such as uneven compaction and over-compaction.

[0038] (3) Improve the poor adaptability of the equipment and adapt to complex construction scenes: The adaptive adjustment module of the present scheme can automatically adjust the equipment working parameters according to different soil, climate conditions and construction environment through specific formulas combined with sensor weighting coefficient, real-time data, reference target value and adjustment coefficient to calculate the adjusted parameters; and the machine learning optimization step can learn the rules based on historical construction data to continuously improve the adaptability of the equipment to different working conditions, and the automatic adaptation to different construction conditions step can adjust the operation mode according to real-time soil parameters and climate information to ensure that the equipment can still achieve the best compaction effect in complex or special environments, significantly enhancing the adaptability of the equipment to diversified construction scenes. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 System block diagram of the present application;

[0040] Figure 2 Method flow chart of the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0042] The present application will be further described below in combination with the embodiments.

[0043] Please refer to Figures 1-2 The present application provides a highway engineering intelligent compaction equipment and method based on multi-parameter cooperative control, including a sensor module for real-time monitoring of multiple working parameters such as pavement compaction degree, vibration frequency, compaction speed, temperature, humidity, etc., and transmitting the collected data to a central processing unit;

[0044] A central control module for receiving real-time data from the sensor module, real-time regulation and control of the compaction equipment based on preset algorithms and models, adjustment of the working state of the equipment according to real-time data and working condition requirements;

[0045] A drive module for adjusting the driving speed, compaction force and vibration frequency of the compactor according to the instructions of the central control unit, to ensure the cooperative control of various parameters in the compaction process and achieve the best compaction effect;

[0046] An adaptive adjustment module capable of automatically adjusting the working parameters of the compaction equipment according to different soil, climate conditions and construction environment to ensure the best compaction effect in complex or special environments;

[0047] A man-machine interaction module for displaying the working state, monitoring parameters and compaction progress of the equipment, and allowing the operator to manually intervene or adjust the equipment, providing a friendly operation experience;

[0048] A power management module for providing stable power support for the equipment to ensure stable operation of the equipment during long-time operation.

[0049] Further as Figure 1As shown, it is worth noting that the sensor module includes a compaction sensor, an acceleration sensor, a temperature and humidity sensor, and a GPS positioning sensor. The compaction quality, equipment operation, environmental conditions, and location information collected by the four types of sensors are transmitted synchronously to the central control module to provide complete input for the multi-parameter collaborative control model. The GPS positioning enters the clay area, combined with the previous soil data, the temperature and humidity sensor monitors the high humidity, and the compaction sensor displays the current compaction deficiency. The central module can comprehensively calculate, synchronously adjust the driving module to reduce the driving speed and increase the vibration intensity, and verify the vibration parameter adjustment effect in real time through the acceleration sensor, forming a real-time closed loop of monitoring, analysis, control, and verification, greatly reducing the problems of uneven compaction and parameter lag.

[0050] Further as Figure 1 shown, it is worth noting that the central control module adopts a multi-parameter collaborative control model, which includes the following model formulas:

[0051]

[0052] In the formula, is the comprehensive control parameter of the compaction equipment, is the weight coefficient of the i-th sensor, is the real-time data of the i-th sensor, and n is the number of sensors; traditional compaction equipment relies on single parameter control, which is easy to ignore related factors such as soil quality, temperature and humidity, leading to fluctuations in compaction effect. This model integrates compaction, vibration frequency, temperature and humidity, and GPS positioning related data collected by the sensor module to convert multiple dispersed parameters into a unified comprehensive control parameter, allowing the central control module to regulate the equipment from a holistic perspective; the weight coefficient in the model can be flexibly set according to construction requirements and parameter importance, making the core parameters more significant in the comprehensive control results and improving the control accuracy; highway engineering compaction operation needs to respond to working condition changes in real time. If multiple sensor data is analyzed and regulated one by one, it is easy to cause data processing delay and affect equipment adaptability. This model converts multi-parameter calculation into a single comprehensive control parameter through a linear summation formula, simplifying the data processing process of the central control module and reducing calculation time.

[0053] Further as Figure 1As shown, it is worth noting that the central control module is provided with a processor, a data storage module and a communication interface. The communication interface is a bridge for the linkage of the central control module with other modules and external systems, ensuring smooth data transmission and functional coordination. Internally, it can realize bidirectional data transmission with the sensor module, instruction transmission with the driving module and self-adaptive adjustment module, and data interaction with the human-computer interaction module to form a closed loop of monitoring, calculation, regulation and feedback. Externally, it can transmit construction data to a remote management system through a wireless network or a wired network, so that the management personnel can master the construction progress and quality in real time, and can also receive construction standard parameters issued by the remote system to realize the coordination of the equipment and the external management system, and solve the defects of traditional equipment module isolation and weak external coordination.

[0054] Further as shown in Figure 1 , it is worth noting that the self-adaptive adjustment module of the equipment adjusts the working parameters automatically through the following formula:

[0055]

[0056] In the formula, is the adjusted equipment working parameter, is the weighting coefficient of the i th sensor, is the real-time monitoring data of the i th sensor, is the reference target value, is the adjustment coefficient, and n is the number of sensors; the weighting coefficient in the formula can be flexibly set according to the influence weight of the parameter on the compaction effect, so that the deviation of the core parameter can be preferentially driven to adjust and improve the targeting of regulation and control; for example, in the roadbed compaction stage, the compaction degree is a core quality index, and the weighting coefficient of the compaction degree sensor can be set to a higher value, and the temperature and humidity sensor can be set to a lower value. When the compaction degree is much lower than , the deviation has a higher proportion in the summation term, and the calculated will preferentially point to the adjustment direction of increasing vibration intensity and reducing driving speed, which is strongly related to the compaction degree, to avoid interference of non-core parameters with the core quality target and ensure that the adjustment logic is consistent with the construction focus.

[0057] Further as shown in Figure 2 , it is worth noting that the highway engineering intelligent compaction equipment based on the above-mentioned multi-parameter collaborative control includes the following steps:

[0058] S1, sensor data acquisition: multiple sensor modules are arranged at the highway engineering construction site to monitor soil humidity, soil pressure, soil temperature, vibration frequency, climate temperature, humidity and other related parameters in real time, and the data collected by each sensor module is transmitted to the control unit through a wireless network or a wired network;

[0059] S2, data processing and analysis: the control unit receives real-time data from the sensor module and processes and analyzes the data using a multi-parameter collaborative control algorithm, considering the correlation of multiple parameters to generate comprehensive control parameters for guiding the working state of the intelligent compaction equipment;

[0060] S3, compaction effect optimization: based on the comprehensive control parameters obtained by analysis, the working state of the equipment is adjusted to optimize the compaction effect of the equipment, ensuring uniform compaction of the soil and the required density during construction;

[0061] S4, dynamic adjustment control: during construction, the working state of the environment and the equipment is monitored in real time, the difference between the target parameters and the actual measured data is compared, and the working parameters of the equipment are dynamically adjusted. According to the real-time feedback data, the speed and vibration intensity of the equipment are automatically adjusted through the collaborative control algorithm to meet the construction requirements;

[0062] S5, machine learning optimization: based on the real-time feedback data of the equipment during construction, machine learning algorithms are used to analyze and learn historical data, optimize control strategies and working parameters, and gradually improve the adaptability of the equipment to different soil, climate and working environment through learning the rules of the construction process;

[0063] S6, automatic adaptation to different construction conditions: in the construction site, according to the real-time acquisition of soil parameters, climate conditions and other information, the operation mode of the compaction equipment is automatically adjusted to ensure that the equipment can adapt to different soil, different humidity and changing climate conditions, and achieve the best compaction effect;

[0064] S7, intelligent evaluation and feedback: after the construction is completed, the control unit automatically generates a compaction quality evaluation report according to the real-time performance of the equipment during construction;

[0065] S8, continuous optimization and adaptive adjustment: through continuous data monitoring and analysis, the multi-parameter collaborative control algorithm is continuously optimized to enhance the adaptive ability of the equipment, and the control strategy is adjusted in real time according to the specific requirements of different engineering projects and construction environment.

[0066] Further as Figure 2As shown, it is worth noting that in the intelligent evaluation and feedback step, data storage and feedback are also included. The data storage function can completely retain key data during the construction process, including real-time parameters collected by the sensor module, comprehensive control parameters generated by the central control module, execution instruction records of the driving module, and the final compaction quality evaluation results. When local road surface settlement, compactness not meeting standards, and other quality problems occur after construction, the corresponding construction period, equipment parameter changes, environmental conditions, and other information of the problem area can be retrieved and stored data to accurately locate the problem causes, and provide objective data basis for quality responsibility definition, avoiding the problem of no data support and difficult responsibility division in traditional operations; The stored historical construction data is the core data set for subsequent machine learning optimization steps. For example, for different soil types such as clay soil and sandy soil, the data storage can store the corresponding sensor parameters, control parameters, and compaction quality evaluation score correlation data for machine learning algorithm analysis. In this way, the weight coefficients of the multi-parameter collaborative control model and the adjustment coefficients of the self-adaptive adjustment formula can be optimized, and the adaptability of the equipment to diversified working conditions can be gradually improved, forming a closed loop of construction, storage, learning, and optimization, and solving the limitations of traditional equipment without data accumulation and optimization without basis.

[0067] Further as Figure 2 shown, it is worth noting that in the continuous optimization and self-adaptive adjustment step, cross-project experience accumulation is also included. Cross-project experience accumulation can integrate key data from different engineering projects, such as roadbed compaction, pavement base compaction, or different regional clay soil, sandy soil, and permafrost construction projects, including adaptive sensor weight coefficients, comprehensive control parameter ranges, self-adaptive adjustment formula adjustment coefficients, and optimal compaction parameter combinations, forming a standardized parameter knowledge base covering multiple soil types, multiple climates, and multiple construction types.

[0068] The present scheme has the following working process:

[0069] Step one: sensor data collection: multiple sensor modules are arranged at the construction site of the highway engineering, which can monitor soil humidity, soil pressure, soil temperature, vibration frequency, climate temperature, humidity and other related parameters in real time, and transmit the data collected by each sensor module to the control unit through wireless network or wired network;

[0070] Step two: data processing and analysis: the control unit receives real-time data from the sensor module, and processes and analyzes the data using a multi-parameter collaborative control algorithm, considering the correlation of multiple parameters, and generates comprehensive control parameters to guide the working state of the intelligent compaction equipment;

[0071] Step three: compaction effect optimization: according to the comprehensive control parameters obtained by analysis, the working state of the equipment is adjusted to optimize the compaction effect of the equipment, ensuring the uniform compaction of the soil and the required compactness during the construction process;

[0072] Step four: Dynamic adjustment control: During the construction process, the working status of the environment and the equipment is monitored in real time, the difference between the target parameters and the actual measurement data is compared, and the working parameters of the equipment are dynamically adjusted. According to the real-time feedback data, the driving speed and vibration intensity of the equipment are automatically adjusted through the collaborative control algorithm to meet the construction requirements.

[0073] Step five: Machine learning optimization: Based on the real-time feedback data of the equipment during the construction process, machine learning algorithms are used to analyze and learn historical data, optimize control strategies and working parameters, and gradually improve the adaptability of the equipment to different soil, climate and working environment through learning the rules in the construction process.

[0074] Step six: Automatic adaptation to different construction conditions: In the construction site, according to the real-time acquisition of soil parameters, climate conditions and other information, the operation mode of the compaction equipment is automatically adjusted to ensure that the equipment can adapt to different soil, different humidity and changing climate conditions, and achieve the best compaction effect.

[0075] Step seven: Intelligent evaluation and feedback: After the construction is completed, the control unit automatically generates a compaction quality evaluation report based on the real-time performance of the equipment during the construction process.

[0076] Step eight: Continuous optimization and adaptive adjustment: Through continuous data monitoring and analysis, the multi-parameter collaborative control algorithm is continuously optimized to enhance the adaptive ability of the equipment. According to the specific requirements of different engineering projects and the construction environment, the control strategy is adjusted in real time.

[0077] In summary: the scheme realizes real-time monitoring of multiple working parameters such as road compaction degree, vibration frequency, compaction speed, temperature, humidity, etc. through the sensor module, the central control module calculates the comprehensive control parameter based on the multi-parameter collaborative control model, combines the weight coefficient of each sensor and the real-time data to control the equipment working state, and drives the module to synchronously adjust the parameters such as driving speed, compaction strength and vibration frequency, realizes multi-parameter collaborative control, effectively avoids the fluctuation of compaction effect caused by single parameter control, and guarantees the stability of compaction quality; in the scheme, the sensor module continuously collects multi-dimensional real-time data and transmits them to the central control module, the control unit processes and analyzes the data through the multi-parameter collaborative control algorithm, generates the comprehensive control parameter to guide the equipment working, and at the same time in the construction process, dynamically adjusts the control steps to monitor the environment and equipment state in real time, compares the difference between the target and actual data, automatically adjusts the equipment parameters, forms a complete real-time feedback and dynamic adjustment closed loop, greatly reduces the probability of abnormal conditions such as uneven compaction and over-compaction; the adaptive adjustment module of the scheme can automatically adjust the equipment working parameters according to different soil, climate conditions and construction environment through specific formula combined with sensor weighting coefficient, real-time data, reference target value and adjustment coefficient; and the machine learning optimization step can learn the law based on historical construction data, continuously improve the adaptability of the equipment to different working conditions, and the automatic adaptation to different construction conditions can adjust the operation mode according to real-time soil parameters and climate information, ensure that the equipment can still achieve the best compaction effect in complex or special environment, and significantly enhance the adaptability of the equipment to diversified construction scenes.

[0078] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. Highway engineering intelligent compaction equipment based on multi-parameter cooperative control, characterized in that, Comprise: Sensor module: for real-time monitoring of pavement compaction, vibration frequency, compaction speed, temperature, humidity and other parameters, and transmit the collected data to the central processing unit; Central control module: for receiving real-time data from the sensor module, based on the preset algorithm and model to control the compaction equipment in real time, according to the real-time data and working condition requirements to adjust the working state of the equipment; Drive module: according to the instruction of the central control unit, adjust the driving speed, compaction force and vibration frequency of the compaction machine, etc. Parameters to ensure the coordinated control of various parameters in the compaction process to achieve the best compaction effect; Adaptive adjustment module: can automatically adjust the working parameters of the compaction equipment according to different soil, climate conditions and construction environment, to ensure the best compaction effect in complex or special environment; Man-machine interaction module: for displaying the working state of the equipment, monitoring parameters and compaction progress, and allowing the operator to manually intervene or adjust the equipment, providing a friendly operation experience; Power management module: provides stable power support for the equipment to ensure stable operation of the equipment in long-time operation.

2. The multi-parameter synergy based intelligent compaction equipment for highway engineering of claim 1, wherein: The sensor module comprises compaction degree sensor, acceleration sensor, temperature and humidity sensor, GPS positioning sensor.

3. The multi-parameter synergy based intelligent compaction equipment for highway engineering of claim 1, wherein: The central control module adopts multi-parameter collaborative control model, which contains the following model formula: In the formula, is a comprehensive control parameter of the compaction equipment, is a weight coefficient of the i th sensor, is real-time data of the i th sensor, and n is the number of sensors.

4. The multi-parameter synergy based intelligent compaction equipment for highway engineering of claim 1, wherein: The central control module is provided with a processor, a data storage module and a communication interface.

5. The multi-parameter synergy based intelligent compaction equipment for highway engineering of claim 1, wherein: The adaptive adjustment module of the equipment adjusts the working parameters through the following formula: In the formula, is the adjusted equipment working parameter, is the weighting coefficient of the i th sensor, is the real-time monitoring data of the i th sensor, is the reference target value, is the adjustment coefficient, and n is the number of sensors.

6. The multi-parameter synergy based intelligent compaction method for highway engineering of claim 1, wherein: For realizing the intelligent compaction equipment for highway engineering based on multi-parameter collaborative control as claimed in any one of claims 1-5, comprising the following steps: S1, sensor data acquisition: in the construction site of highway engineering, multiple sensor modules are laid out to monitor soil humidity, soil pressure, soil temperature, vibration frequency, climate temperature, humidity and other related parameters in real time, and the data collected by each sensor module is transmitted to the control unit through wireless network or wired network; S2, data processing and analysis: the control unit receives real-time data from the sensor module, and processes and analyzes the data using multi-parameter collaborative control algorithm, considering the correlation of multiple parameters, to generate comprehensive control parameters for guiding the working state of the intelligent compaction equipment; S3, compaction effect optimization: according to the comprehensive control parameters obtained by analysis, the working state of the equipment is adjusted to optimize the compaction effect of the equipment, to ensure uniform compaction of soil and required density during construction; S4, dynamic adjustment control: during construction, the working state of the environment and the equipment is monitored in real time, the difference between the target parameters and the actual measurement data is compared, and the working parameters of the equipment are dynamically adjusted. According to the real-time feedback data, the driving speed, vibration intensity and other parameters of the equipment are automatically adjusted through the collaborative control algorithm to meet the construction requirements; S5, machine learning optimization: based on the real-time feedback data of the equipment during construction, machine learning algorithm is used to analyze and learn the historical data, to optimize the control strategy and working parameters, and to gradually improve the adaptability of the equipment to different soil, climate and working environment by learning the rules in the construction process; S6, automatically adapt to different construction conditions: in the construction site, according to the real-time acquisition of soil parameters, climate conditions and other information, automatically adjust the operation mode of the compaction equipment, ensure that the equipment can adapt to different soil, different humidity and changing climate conditions, realize the best compaction effect; S7, intelligent evaluation and feedback: after the completion of construction, the control unit automatically generates a compaction quality evaluation report according to the real-time performance of the equipment during construction; S8, continuous optimization and adaptive adjustment: through continuous data monitoring and analysis, continuously optimize the multi-parameter collaborative control algorithm, enhance the adaptive ability of the equipment, and adjust the control strategy in real time according to the specific requirements of different engineering projects and construction environment.

7. The multi-parameter synergy based intelligent compaction method for highway engineering of claim 6, wherein: In the intelligent evaluation and feedback step, it also includes data storage and feedback.

8. The multi-parameter synergy based intelligent compaction method for highway engineering of claim 6, wherein: In the step of continuous optimization and adaptive adjustment, it also includes cross-project experience accumulation.

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