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The effective engineering data survey algorithm is an important basis for engineering construction surveying, design, construction and management. In order to improve the survey accuracy of engineering data, the sensor survey method is researched, and the fusion detection and survey of engineering data is carried out. The traditional method used infrared and acoustic pulse detection method for frequency discrimination characteristic control and engineering data survey, when the engineering exploration stayed in a strong magnetic field, the engineering data survey curve achieved saturation, measuring performance is not good. An optimization method of engineering data survey algorithm is proposed based on nonlinear compensation control, and it is applied in the engineering surveying practice, quantitative analysis method is taken for realizing distributed engineering data survey nodes positioning and sensor information fusion, quantitative information fusion is taken in low dimensional state space, introducing end crosstalk sinusoidal pulse width modulation control of piezoelectric type permanent, and the magnetic detection circuit is designed, phase frequency characteristic survey method is presented. The system design is obtained. The adaptive noise cancellation method is taken for filtering, and the multi sensor data fusion filter structure model is designed. On the basis of the information distribution principle and nonlinear compensation, the fusion center is updated, and engineering data survey algorithm is improved. Simulation results show that, the engineering data survey algorithm is applied in the surveying and mapping of geographic Information calibration, the survey accuracy is improved, and the error is small, it has superior application performance in practice. Introduction Engineering Survey technology is one of the fundamental disciplines of surveying and mapping industry, all the surveying and mapping work in engineering construction are referred to as the engineering survey. In fact, it includes various survey works such as construction project survey, design, construction, installation, completion, monitoring and operation management and a series of engineering process service. It can be said, if there is no survey provides data and drawings for the construction of the project, any construction would not be processed and completed, thus, engineering data survey technology has important value and significance in the basic geographic information exploration and engineering construction fields. Generally speaking, engineering survey usually refers to the construction engineering survey and design, construction and management phase need use various survey theory, it is very important[1]. Traditional engineering survey technology service areas include building, water conservancy, transport, mining and other departments, and the basic contents have two parts as drawing and data sampling. The concept of modern engineering survey not only includes the static, dynamic geometry and physical quantity project determination, but also includes the analysis of the survey results, so the engineering data survey has important significance in the field of surveying and mapping. Accurate and effective engineering data survey algorithm design is an important basis for engineering construction surveying, design, construction and management[2], in order to improve the survey accuracy of engineering data, the sensor survey method is researched, the sensor measuring method, fusion engineering data and accurate detection and survey are researched, the traditional method used infrared and acoustic pulse detection method for frequency discrimination characteristic control and engineering data survey, when the engineering exploration stayed in a strong magnetic field, the engineering data survey curve achieved saturation, measuring International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2015) © 2015. The authors Published by Atlantis Press 1324 performance is not good[3]. The reference [4] proposed the improved algorithm, a survey fusion algorithm for sensor engineering data energy management is proposed based on tracking differential delay and error compensation, but the algorithm is affected by noise is big, the accuracy is not high. In reference [5], a wireless tracking sensor engineering data analysis method is proposed based on priori information estimation, the condition has a good node transceiver channel equalization effect under the situation of priori information is unknown, but the algorithm need the decomposition and phase space rearrangement of signal time-frequency signal, calculation is large. According to the problems as above, an optimization method of engineering data survey algorithm is proposed based on nonlinear compensation control, and it is applied in the engineering surveying practice, quantitative analysis method is taken for realizing distributed engineering data survey nodes positioning and sensor information fusion, quantitative information fusion is taken in low dimensional state space, introducing end crosstalk sinusoidal pulse width modulation control of piezoelectric type permanent, and the magnetic detection circuit is designed, phase frequency characteristic survey method is presented. The system design is obtained. The adaptive noise cancellation method is taken for filtering, and the multi sensor data fusion filter structure model is designed. On the basis of the information distribution principle and nonlinear compensation, the fusion center is updated, and engineering data survey algorithm is improved. Simulation results show that, the engineering data survey algorithm is applied in the surveying and mapping of geographic Information calibration, the survey accuracy is improved, and the error is small, it has superior application performance in future. Model construction of engineering data survey system and problem description Multi sensor engineering data fusion model In order to optimize engineering data survey accuracy, it needs for sensor data fusion in survey project, accurate state estimation of the engineering data is realized, the base is to establish the multi sensor data fusion model. In the multi-sensor system, taking into account the adaptive transversal filter, in case the parameters are real, the survey error e can be expressed as: ( ) ( ) ( ) ( ) T e n d n u n w n = − , where, n shows the n th iterative, u is the input vector, w is the weighted vector. The LMS estimates the squared error 2 e of i time , the gradient representation takes the∇ as symbol. If the weight vectorW is recorded as k W at k moment, in order to solve a weight vector make the mean square error be minimum, it can start from a set of initial value 0 W , the gradient value is computed, and then select a new value 1 W , let it be equal to initial value 0 W , increment of the engineering data is proportional to the negative of the gradient. The next value is calculated 2 W from the gradient of 1 W , using the same method to derive linear time invariant system, impulse response ( ) H n in the signal processing is fixed. The weight 1k w is continuously adjusted to error 2 [ ] k E ε in the adaptive processing, and it can reach the minimum value[6]. So, the response adaptive system can continuously reduce expectations and its own difference, the engineering data can be gauged, so that the system can gradually adapt to a given engineering data signal environment, survey model of engineering data can be expressed as follows: ( , ) , 1,2,..., k k i i i k k k z h x u v i M = + = (1) The crosstalk sinusoidal pulse width modulation control is introduced, and the iezoelectric type permanent magnetic detection circuit is designed, phase frequency characteristic survey method is improved, the mathematical expression of engineering data vector is obtained: Single input system: [ ] 0 1 2 , , , ,.... T k k X x x x x = (2) Multi input system: [ ] 0 1 2 , , , ,.... T k k k k lk X x x x x = (3) According to the input of engineering data as above, we can get the relationship between system output and input as:

Optimization and Application of Engineering Data Survey Based on Nonlinear Compensation Algorithm