A high-precision adaptive positioning solution method for port vehicles based on RTK

Abstract

A high-precision adaptive positioning and solving method for port vehicles based on RTK, comprising: step 1, installing a base station; step 2, obtaining base station data; step 3, a cloud solving platform storing base station data, receiving satellite data observed by the base station, classifying and deduplicating the data, decoding the data, and storing the data in a database; step 4, obtaining mobile station data, connecting to the cloud solving platform through a 5G private network, and uploading the satellite observation data of the mobile station; step 5, adaptive position solving, sending the base station satellite observation data to the mobile station, the mobile station decoding the data after verification, and adaptively selecting a position solving platform; step 6, data forwarding and storage, if the positioning result is directly calculated by the mobile station, the mobile station uploads it to the cloud solving platform, and the cloud solving platform stores the positioning result uploaded by the mobile station in the database after receiving it; if the cloud solving platform is used for positioning, the positioning result is sent to the mobile station hardware platform, and the current positioning result is stored locally.

Description

Technical Field

[0001] The present invention relates to the technical field of high-precision port positioning platforms, and in particular to an adaptive high-precision positioning method for port vehicles based on open-air scenes. Background Art

[0002] There are many types of mobile machinery involved in production operations in ports, including a large number of container trucks, crawler cranes, loaders, forklifts, trailers, etc. Due to its geographical characteristics and the stacking of goods, ports require extremely high operating order and precise operating positions, otherwise there will be low loading efficiency or even accidents during the loading process. How to perform stable and accurate centimeter-level positioning of movable mechanical equipment in ports has become an urgent problem that major ports need to solve.

[0003] At present, the positioning of port transport loading machinery is mainly carried out by satellite positioning modules through GNSS-RTK (Global Navigation Satellite System-Real-time kinematic global navigation satellite system real-time dynamic carrier phase difference technology). Due to the complex port environment, high stacked containers and loaders will cause altitude shielding and partial shielding of satellite signals, resulting in serious interference of some received satellite observations, serious jumps in pseudo-range observations, and frequent cycle jumps in carrier phase observations, resulting in poor satellite positioning effects and unable to meet the needs of port machinery and equipment for high-precision position information. At present, the main methods to improve positioning accuracy are to select high-quality satellites or improve the position solution algorithm. However, accurate satellite selection or high-precision position solution is generally more complex and requires higher hardware computing platforms. Due to the limited computing power of the positioning module mounted on the mechanical equipment, it is very challenging to implement the satellite selection strategy and high-precision position solution algorithm locally. Summary of the invention

[0004] The present invention aims to overcome the above-mentioned deficiencies in the prior art and provide a high-precision adaptive positioning solution method for port vehicles based on RTK.

[0005] The present invention provides a high-precision adaptive positioning method for port vehicles based on RTK, using the following equipment:

[0006] A set of GNSS-RTK base stations, providing the base station observation data required for RTK positioning, a cloud server, on which an adaptive cloud solution platform and a set of 5G private network base stations are installed, for data transmission and exchange between the mobile station to be positioned and the cloud solution platform; the communication module and satellite signal receiving device installed on the engineering equipment form a mobile station, which can upload satellite observation data to the cloud solution platform for RTK solution, and can also obtain the differential data of the base station through the communication module, and use the mobile station computing chip to solve the position of the mobile station locally, and then upload the solution result to the cloud solution platform; the cloud solution platform stores the high-precision positioning data of all currently connected field vehicles;

[0007] The steps include:

[0008] Step 1: Install the base station. The GNSS-RTK base station is placed in a place with a good view of the sky. Determine the exact location of the base station installation, fix the base station mechanically, and then solve the exact coordinates of the base station through PPP (precision point positioning) or RTK smoothing to determine the ECEF coordinates of the base station.

[0009] Step 2: Obtain the base station data. The GNSS-RTK base station is connected to the cloud solution platform through a wired network or 5G to transmit the satellite observation data received by the base station.

[0010] Step 3: The cloud solution platform stores the base station data. The cloud solution platform receives the satellite data observed by the base station, classifies and removes the duplicate data, decodes the data according to the corresponding compression format, saves it in the database, and waits for the mobile station hardware platform to connect to the cloud solution platform for solution.

[0011] Step 4, obtain the mobile station data, start the mobile station installed on the field vehicle, and connect the mobile station to the cloud solution platform through the 5G private network to perform authority verification, registration service, etc. After the verification is passed, the base station satellite observation data can be obtained from the cloud solution platform and the mobile station satellite observation data can be uploaded to the cloud solution platform.

[0012] Step 5, adaptive position solution, sends the base station satellite observation data stored in the cloud solution platform to the mobile station, the mobile station verifies the sent data, decodes the data after verification, and adaptively selects the position solution platform.

[0013] The adaptive position solution method proposed in step 5 includes the following sub-steps:

[0014] Step 5.1, time synchronization, time synchronization of the base station data and the mobile station data, and calculation of the time difference ΔT.

[0015] ΔT=|T R -T B | (1)

[0016] In the above formula, T R Represents the satellite observation data time of the mobile station, T B Represents the satellite observation data time of the base station.

[0017] Check whether the message differential time meets the set maximum message differential time T threshold .

[0018] ΔT <T threshold (s) (2)

[0019] If the above formula is satisfied, the observation data of the base station and the mobile station are considered valid, the time verification is passed, and go to step 5.2. Otherwise, it is considered that there is a problem with the communication with the cloud solution platform, and the received base station satellite observation data is discarded, and the NTRIP service is reconnected when the next communication is successful, and this positioning is completed.

[0020] Step 5.2, check whether the current satellite observation data matches the computing capability of the mobile station hardware platform, count the observation data of the mobile station, and check whether the number of satellites in the memory of the mobile station hardware platform satisfies the following formula:

[0021]

[0022] In the above formula, Indicates the minimum number of satellites required for a single-point positioning solution to meet the positioning accuracy requirements of construction machinery in the GPS and BDS dual systems. If this condition is met, perform single-point positioning, and go to step 5.3 after obtaining the single-point positioning result. If the number of satellites in the mobile station memory satisfies the following formula:

[0023]

[0024] The positioning result is no solution, an error message is sent to the cloud solution platform, and the positioning ends.

[0025] Step 5.3: After successful single-point positioning, perform relative positioning to obtain high-precision RTK position solution results. Filter the base station and mobile station satellite observation information stored in the mobile station hardware platform to select the common view satellites of the base station and service station. Put the satellite numbers of the common-view satellites into the preparation list for relative positioning In addition, the signal-to-noise ratio screening and satellite elevation angle screening are performed to determine the satellite minimum signal-to-noise ratio threshold. and the satellite minimum altitude angle threshold EL Min , the minimum signal-to-noise ratio threshold of the above satellites and the satellite minimum altitude angle threshold EL Min It is determined by the environment in which the mobile station receiver is located. If the signal-to-noise ratio of the satellite numbered i is Or satellite elevation angle EL i Satisfy the following formula:

[0026]

[0027] EL i <EL Min (6)

[0028] The satellite number of the satellite numbered i is deleted from the relative positioning list.

[0029] If the number of common-view satellites after elevation angle and signal-to-noise ratio screening satisfies the following formula:

[0030]

[0031] is the maximum number of satellites in common view that the hardware platform can process, and its value is determined by the computing power of the hardware platform and the complexity of the algorithm. If it exceeds the computing power of the mobile station hardware platform, the positioning data will be sent to the cloud computing platform, which will perform positioning and go to step 5.4.

[0032] If the number of satellites in common view satisfies the following formula:

[0033]

[0034] Then the relative positioning calculation is performed in the mobile station hardware platform. The baseline lengths of the base station and the mobile station are solved by the relative positioning algorithm, and the integer ambiguity of the common view satellite is fixed by the integer ambiguity fixing algorithm. If the fixation is successful, that is, the solution result is an RTK integer solution, the precise coordinates of the mobile station are obtained based on the base station coordinates and the baseline vector between the above two stations, and the solution result is sent to the cloud solution platform, and the solution is completed. If the integer ambiguity fixation fails, that is, the solution result is an RTK floating point solution, the data of this positioning is uploaded to the cloud solution platform, and a secondary solution is performed in the cloud solution platform, and then go to step 5.5.

[0035] Step 5.4 The cloud solution platform performs the first solution, selects satellites for the satellite observation data uploaded by the mobile station, and selects high-quality, reliable observation satellites without cycle slips. Taking the GPS and BDS dual systems as an example, if the number of satellites after selection satisfies the following formula:

[0036]

[0037] The selected satellites are used for single-point positioning to obtain single-point positioning results.

[0038] If the number of satellites after selection satisfies the following formula:

[0039]

[0040] Then gradually reduce the threshold of the satellite selection algorithm until the number of satellites after selection satisfies the following formula:

[0041]

[0042] Perform single-point positioning and obtain single-point positioning results.

[0043] After successful single-point positioning, the satellite observation information of the base station and mobile station stored in the cloud solution platform is screened to select the common-view satellites of the base station and the service station. Put the satellite numbers of the common-view satellites into the preparation list for relative positioning In addition, the signal-to-noise ratio screening and satellite elevation angle screening are performed to determine the satellite minimum signal-to-noise ratio threshold. and the satellite minimum altitude angle threshold EL Min , the minimum signal-to-noise ratio threshold of the above satellites and the satellite minimum altitude angle threshold EL Min It is determined by the environment in which the mobile station receiver is located. If the signal-to-noise ratio of the satellite numbered i is Or satellite elevation angle EL i Satisfy the following formula:

[0044]

[0045] EL i <EL Min (6)

[0046] The satellite number of the satellite numbered i is deleted from the relative positioning list.

[0047] If the number of common-view satellites after elevation angle and signal-to-noise ratio screening satisfies the following formula:

[0048]

[0049] Existence The common-view satellites that meet the above conditions represent sufficient current observation quantities. However, positioning fails due to the limited processing capability of the mobile station hardware platform or the wrong selection of observation quantities. The above common-view satellites are sorted by the star selection algorithm, using the signal-to-noise ratio-elevation angle joint model:

[0050]

[0051] In the formula, a and b are model parameters, and their values ​​are given by experience. 0 is the signal-to-noise ratio of the satellite signal received by the receiver, and E is the altitude angle between the receiver and the current satellite.

[0052] Since the common-view satellite is observed by two receivers together, there are two observation values. Therefore, the two observation values ​​are solved separately. The base station data and the mobile station data are respectively substituted into the above formula to calculate

[0053] σ i =σ ri +σ ui (10)

[0054] In the formula, σ ri is the value obtained by the signal-to-noise ratio-elevation angle joint model of the base station data of the i-th satellite, σ ui It is the value obtained by using the signal-to-noise ratio-elevation angle joint model for the mobile station data of the i-th satellite.

[0055] For the above σ i Sort and delete the values ​​that are greater than the threshold A.

[0056] Relative positioning of the remaining satellites is performed to solve the floating-point solution of their integer ambiguity. Based on the floating-point solution, the number of satellites is checked again. If the number of common-view satellites that meet the conditions is greater than is the lower limit of the common view satellite for the partial ambiguity fixation algorithm, and the threshold is determined by the number of enabled satellite systems. The integer ambiguity is solved by the partial ambiguity fixation algorithm. Otherwise, the integer ambiguities of all common view satellites are fixed by the full ambiguity fixation algorithm, and the precise coordinates of the mobile station are calculated on the basis of the coordinates of the reference station ECEF determined in step 1, and the current positioning result is saved, and this solution is completed.

[0057] Step 5.5, the cloud solution platform performs a second solution, analyzes the mobile station hardware platform positioning solution results and the solution data uploaded by the mobile station hardware platform positioning, deletes satellites with large variances during the mobile station hardware platform positioning process, reselects satellites using the above algorithm, and then performs relative positioning in conjunction with the base station satellite observations in the cloud solution platform. If positioning still fails, relax the threshold of the star selection algorithm, try different satellite combinations, obtain the positioning result with the smallest positioning variance, save the current positioning result, and end this solution.

[0058] Step 6, data forwarding and storage, if the positioning result is directly calculated by the mobile station hardware platform, the mobile station hardware platform will upload it to the cloud solution platform, and the cloud solution platform will store it in the database after receiving the positioning result uploaded by the mobile station hardware platform. If the positioning is performed by the cloud solution platform, the positioning result will be sent to the mobile station hardware platform and the current positioning result will be stored locally.

[0059] The method of the present invention uses the solution system of the embedded hardware platform-cloud solution platform to design different solution methods according to the observation information and solution results. According to the different amounts of observation information, different platforms are selected for positioning and solution, which makes full use of the observation information and improves the success rate of the positioning solution result being a fixed solution. According to the positioning results of the hardware platform, a secondary solution mode of the cloud platform is designed to avoid the occurrence of erroneous positioning results due to the performance constraints of the hardware platform. Through the above content, the method of the present invention can increase the proportion of fixed solutions in real-time positioning compared with general methods.

[0060] Compared with the existing real-time processing and post-processing methods, the method of the present invention has the following advantages:

[0061] 1. The present invention improves the proportion of fixed solutions compared to traditional real-time processing methods through the solution system of the embedded hardware platform-cloud solution platform. Traditional real-time processing methods are limited by hardware platform performance, processing time and other reasons. When performing positioning and solving, if the solution result is not a fixed solution, no secondary solution is performed. The method of the present invention performs secondary solution on the points whose positioning is not a fixed solution, which can increase the proportion of fixed solutions.

[0062] 2. Compared with the traditional post-processing method, the method of the present invention can output the positioning solution results in real time. The traditional post-processing solution method cannot provide the positioning results in real time, and the positioning results have a lag. The present invention can provide the positioning results in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 It is a schematic diagram of the present invention in which a cloud platform receives satellite differential message information and calculates the position information of mechanical equipment;

[0065] Figure 2 It is a schematic diagram of the process of positioning by the cloud solution platform in the adaptive position solution method of the present invention;

[0066] Figure 3 It is a schematic diagram of the present invention in which a mobile station hardware platform receives satellite differential message information and calculates the position information of mechanical equipment;

[0067] Figure 4 It is a schematic diagram of the process of positioning by the mobile station hardware platform in the adaptive position solving method of the present invention. DETAILED DESCRIPTION

[0068] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0069] A high-precision adaptive positioning method for port vehicles based on RTK of the present invention comprises the following steps:

[0070] Step 1: Install the base station. The GNSS-RTK base station is placed in a place with a good view of the sky. The antenna should be as unobstructed as possible. Pay attention to lightning protection when installing the antenna. Ensure that the antenna is clear for more than 20°. Determine the exact location of the base station installation and fix the base station mechanically. Continuously collect satellite data observed by the receiver for more than 48 hours for PPP (precision point positioning) or continuously collect satellite data observed by the receiver and RTK base station telegram information provided by the RTK service provider for RTK positioning. The accurate coordinates of the base station are solved in a smooth manner. Repeat the positioning at least once a month to determine the ECEF coordinates of the base station.

[0071] Step 2: Obtain the base station data. The GNSS-RTK base station is connected to the cloud solution platform through a wired network or 5G. The cloud solution platform verifies the authority of the base station and registers the service. After the verification is passed, the satellite observation data received by the base station is allowed to be transmitted.

[0072] Step 3: The cloud computing platform stores the base station data. The cloud computing platform receives the satellite data observed by the base station, classifies and removes the data, and decodes it according to the corresponding compression format. The base station data is saved in the database and waits for the call of step 5. The observation data is converted into RTCM3.2 format and broadcast through the NTRIP service on the cloud computing platform. For example, for GPS and BDS dual systems, 1005 (base station antenna coordinates), 1033 (base station antenna type), 1074 (GPS satellite system observations), 1019 (GPS satellite ephemeris), 1114 (BDS satellite system observations), and 1044 (BDS satellite ephemeris) in RTCM3.2 are broadcast, and the broadcast frequency is generally 1HZ.

[0073] Step 4, obtain the mobile station data. When the construction machinery is started, start the mobile station installed on the construction machinery. The mobile station connects to the cloud solution platform through the 5G private network to perform authority verification, registration service, etc. After the verification is passed, the base station satellite observation data can be obtained from the cloud solution platform and the mobile station satellite observation data can be uploaded to the cloud solution platform.

[0074] Step 5, adaptive position solution, the mobile station sends a request to the cloud solution platform NTRIP service for the base station satellite observation data. After receiving the request, the cloud solution platform sends the base station satellite observation data, including the total number of satellites that can be observed, the number of satellites that can be stably observed, the time of the current receiver observation, the current pseudo-range observation value (multi-frequency), the current carrier phase observation value (multi-frequency), the current Doppler observation value (multi-frequency), the current signal-to-noise ratio (multi-frequency), the current carrier phase observation variance (multi-frequency), the current pseudo-range observation variance (multi-frequency), and the current Doppler observation variance (multi-frequency). After receiving the data, the base station performs a data integrity check on the compressed data, and decodes the data after the check passes.

[0075] Step 5.1, time synchronization, time synchronization of the base station data and the mobile station data, and calculation of the time difference ΔT.

[0076] ΔT=|T R -T B | (1)

[0077] In the above formula, T R Represents the satellite observation data time of the mobile station, T B Represents the satellite observation data time of the base station. When the following formula is satisfied:

[0078] ΔT<20s (2)

[0079] The observation data of the base station and the mobile station are considered valid and the time verification is passed, so go to step 5.2. Otherwise, it is considered that there is a problem with the communication with the cloud solution platform, and the received base station satellite observation data is discarded. Wait for the next successful communication to reconnect to the NTRIP service, and this positioning is completed.

[0080] Step 5.2, check whether the current satellite observation data matches the computing capability of the mobile station hardware platform, and count the observation data of the mobile station. Taking the GPS and BDS dual systems as an example, if the number of satellites in the memory of the mobile station hardware platform satisfies the following formula:

[0081]

[0082] Then perform single point positioning, and go to step 5.3 after obtaining the single point positioning result. If the number of satellites in the mobile station memory satisfies the following formula:

[0083]

[0084] The positioning result is no solution, an error message is sent to the cloud solution platform, and the positioning ends.

[0085] Step 5.3: After successful single-point positioning, perform relative positioning to obtain high-precision RTK position solution results. Filter the base station and mobile station satellite observation information stored in the mobile station hardware platform to select the common view satellites of the base station and service station. Put the satellite numbers of the common-view satellites into the preparation list for relative positioning In the process, the signal-to-noise ratio and satellite elevation angle are screened. If the signal-to-noise ratio of the satellite numbered i is Or satellite elevation angle EL i Satisfy the following formula:

[0086]

[0087] EL i <60° (6)

[0088] The satellite number of the satellite numbered i is deleted from the relative positioning list.

[0089] If the number of common-view satellites after elevation angle and signal-to-noise ratio screening satisfies the following formula:

[0090]

[0091] That is, if the computing capacity of the mobile station hardware platform is exceeded, the positioning data will be sent to the cloud computing platform, which will perform positioning and go to step 5.4.

[0092] If the number of satellites in common view satisfies the following formula:

[0093]

[0094] Then the relative positioning calculation is performed in the mobile station hardware platform. The baseline lengths of the base station and the mobile station are solved by the relative positioning algorithm, and the integer ambiguity of the common view satellite is fixed by the integer ambiguity fixing algorithm. If the fixation is successful, that is, the solution result is an RTK integer solution, the precise coordinates of the mobile station are obtained based on the base station coordinates and the baseline vector between the above two stations, and the solution result is sent to the cloud solution platform, and the solution is completed. If the integer ambiguity fixation fails, that is, the solution result is an RTK floating point solution, the data of this positioning is uploaded to the cloud solution platform, and a secondary solution is performed in the cloud solution platform, and then go to step 5.5.

[0095] Step 5.4 The cloud solution platform performs the first solution, selects satellites for the satellite observation data uploaded by the mobile station, and selects high-quality, reliable observation satellites without cycle slips. Taking the GPS and BDS dual systems as an example, if the number of satellites after selection satisfies the following formula:

[0096]

[0097] The selected satellites are used for single-point positioning to obtain single-point positioning results.

[0098] If the number of satellites after selection satisfies the following formula:

[0099]

[0100] Then gradually reduce the threshold of the satellite selection algorithm until the number of satellites after selection satisfies the following formula:

[0101]

[0102] Perform single-point positioning and obtain single-point positioning results.

[0103] After successful single-point positioning, the satellite observation information of the base station and mobile station stored in the cloud solution platform is screened to select the common-view satellites of the base station and the service station. Put the satellite numbers of the common-view satellites into the preparation list for relative positioning In the process, the signal-to-noise ratio screening and satellite elevation angle screening are performed. If the signal-to-noise ratio of the satellite numbered i is Or satellite elevation angle EL i Satisfy the following formula:

[0104]

[0105] EL i <60° (6)

[0106] The satellite number of the satellite numbered i is deleted from the relative positioning list.

[0107] If the number of common-view satellites after elevation angle and signal-to-noise ratio screening satisfies the following formula:

[0108]

[0109] That is, there are more than 10 common-view satellites that meet the conditions, which means that the current observation quantity is sufficient, but the positioning fails due to the limitation of the processing capacity of the mobile station hardware platform or the error in the selection of observation quantity. The above common-view satellites are sorted by the star selection algorithm, and the signal-to-noise ratio-elevation angle joint model is used:

[0110]

[0111] In the formula, a and b are model parameters, with values ​​of 10 and 22500 respectively, C / N 0 is the signal-to-noise ratio of the satellite signal received by the receiver, and E is the altitude angle between the receiver and the current satellite.

[0112] Since the common-view satellite is observed by two receivers together, there are two observation values. Therefore, the two observation values ​​are solved separately. The base station data and the mobile station data are respectively substituted into the above formula to calculate

[0113] σ i =σ ri +σ ui (10)

[0114] In the formula, σ ri is the value obtained by the signal-to-noise ratio-elevation angle joint model of the base station data of the i-th satellite, σ ui It is the value obtained by using the signal-to-noise ratio-elevation angle joint model for the mobile station data of the i-th satellite.

[0115] For the above σ i Sort and delete the values ​​that are greater than the threshold A.

[0116] The remaining satellites are relatively positioned to solve the floating-point solution of their integer ambiguity. On the basis of solving the floating-point solution, the number of satellites is checked again. If the number of common-view satellites that meet the conditions is greater than 14 satellites, the integer ambiguity is solved by the partial ambiguity fixing algorithm. Otherwise, the integer ambiguity of all common-view satellites is fixed by the full ambiguity fixing algorithm. On the basis of the ECEF coordinates of the reference station determined in step 1, the precise coordinates of the mobile station are calculated, the current positioning result is saved, and this solution is completed.

[0117] Step 5.5, the cloud solution platform performs a second solution, analyzes the mobile station hardware platform positioning solution results and the solution data uploaded by the mobile station hardware platform positioning, deletes satellites with large variances during the mobile station hardware platform positioning process, reselects satellites using the above algorithm, and then performs relative positioning in conjunction with the base station satellite observations in the cloud solution platform. If positioning still fails, relax the threshold of the star selection algorithm, try different satellite combinations, obtain the positioning result with the smallest positioning variance, save the current positioning result, and end this solution.

[0118] Step 6, data forwarding and storage, if the positioning result is directly calculated by the mobile station hardware platform, the mobile station hardware platform will upload it to the cloud solution platform, and the cloud solution platform will store it in the database after receiving the positioning result uploaded by the mobile station hardware platform. If the positioning is performed by the cloud solution platform, the positioning result will be sent to the mobile station hardware platform and the current positioning result will be stored locally.

[0119] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0120] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-precision adaptive positioning solution method for port vehicles based on RTK, using the following equipment: A set of GNSS-RTK base stations, providing the base station observation data required for RTK positioning, a cloud server, on which an adaptive cloud solution platform and a set of 5G private network base stations are installed, for data transmission and exchange between the mobile station to be positioned and the cloud solution platform; the communication module and satellite signal receiving device installed on the engineering equipment form a mobile station, which uploads the satellite observation data to the cloud solution platform for RTK solution, and can also obtain the differential data of the base station through the communication module, and use the mobile station computing chip to solve the position of the mobile station locally, and then upload the solution result to the cloud solution platform; the cloud solution platform stores the high-precision positioning data of all currently connected field vehicles; The steps include: Step 1: Install the base station. The GNSS-RTK base station is placed in a place with a good view of the sky. Determine the exact location of the base station. Fix the base station mechanically. Then solve the accurate coordinates of the base station by precise point positioning (PPP) or RTK smoothing to determine the ECEF coordinates of the base station. Step 2: Obtain base station data. The GNSS-RTK base station is connected to the cloud solution platform through a wired network or 5G to transmit the satellite observation data received by the base station. Step 3: The cloud computing platform stores the base station data. The cloud computing platform receives the satellite data observed by the base station, classifies and removes the data, decodes it according to the corresponding compression format, saves it in the database, and waits for the mobile station hardware platform to connect to the cloud computing platform for computing; Step 4: Get the mobile station data, start the mobile station installed on the field vehicle, connect the mobile station to the cloud solution platform through the 5G private network, perform authority verification and registration service, and after verification, get the base station satellite observation data from the cloud solution platform and upload the mobile station satellite observation data to the cloud solution platform; Step 5: Adaptive position solution: Send the base station satellite observation data stored in the cloud solution platform to the mobile station. The mobile station verifies the sent data, decodes the data after verification, and adaptively selects the position solution platform. The adaptive position solution method proposed in step 5 includes the following sub-steps: Step 5.1, time synchronization, time synchronization of the base station data and the mobile station data, and calculation of the time difference ΔT; ΔT=|T R -T B | (1) In the above formula, T R Represents the satellite observation data time of the mobile station, T B Represents the satellite observation data time of the base station; Check whether the message differential time meets the set maximum message differential time T threshold ; ΔT<T threshold (s) (2) If the above formula is satisfied, the observation data of the base station and the mobile station are considered valid, the time verification is passed, and the process goes to step 5.2; otherwise, it is considered that there is a problem with the communication with the cloud solution platform, and the received base station satellite observation data is discarded, and the NTRIP service is reconnected when the next communication is successful, and the positioning is completed; Step 5.2, check whether the current satellite observation data matches the computing capability of the mobile station hardware platform, count the observation data of the mobile station, and check whether the number of satellites in the memory of the mobile station hardware platform satisfies the following formula: In the above formula, Indicates the minimum number of satellites required for a single-point positioning solution to meet the positioning accuracy requirements of construction machinery in the GPS and BDS dual systems; if this condition is met, perform single-point positioning, and go to step 5.3 after obtaining the single-point positioning result; If the number of satellites in the mobile station memory satisfies the following formula: The positioning result is no solution, an error message is sent to the cloud solution platform, and the positioning ends; Step 5.3: After successful single-point positioning, perform relative positioning to obtain high-precision RTK position solution results; filter the base station and mobile station satellite observation information stored in the mobile station hardware platform to select the common view satellites of the base station and service station. Put the satellite numbers of the common-view satellites into the preparation list for relative positioning In addition, the signal-to-noise ratio screening and satellite elevation angle screening are performed to determine the satellite minimum signal-to-noise ratio threshold. and the satellite minimum altitude angle threshold EL Min , the minimum signal-to-noise ratio threshold of the above satellites and the satellite minimum altitude angle threshold EL Min Determined by the environment in which the mobile station receiver is located; if the signal-to-noise ratio of the satellite numbered i is Or satellite elevation angle EL i Satisfy the following formula: Then the satellite number of the satellite numbered i is deleted from the relative positioning list; If the number of common-view satellites after elevation angle and signal-to-noise ratio screening satisfies the following formula: is the maximum number of satellites in common view that the hardware platform can process, and its value is determined by the computing power of the hardware platform and the complexity of the algorithm. If it exceeds the computing power of the mobile station hardware platform, the positioning data will be sent to the cloud computing platform, which will perform positioning and go to step 5.

4. If the number of satellites in common view satisfies the following formula: Then, the relative positioning calculation is performed in the mobile station hardware platform; the baseline lengths of the base station and the mobile station are solved by the relative positioning algorithm, and the integer ambiguity of the common view satellite is fixed by the integer ambiguity fixing algorithm; if the fixation is successful, that is, the solution result is an RTK integer solution, the precise coordinates of the mobile station are obtained based on the base station coordinates and the baseline vector between the above two stations, and the solution result is sent to the cloud solution platform, and the solution is completed; if the integer ambiguity fixation fails, that is, the solution result is an RTK floating point solution, the data of this positioning is uploaded to the cloud solution platform, and a secondary solution is performed in the cloud solution platform, and then go to step 5.5; Step 5.4 The cloud solution platform performs the first solution, selects satellites for the satellite observation data uploaded by the mobile station, and selects high-quality, reliable observations, and satellites without cycle slips. If the number of satellites after selection satisfies the following formula: Then use the selected satellites to perform single point positioning and obtain the single point positioning result; If the number of satellites after selection satisfies the following formula: Then gradually reduce the threshold of the satellite selection algorithm until the number of satellites after selection satisfies the following formula: Perform single point positioning and obtain single point positioning results; After successful single-point positioning, the satellite observation information of the base station and mobile station stored in the cloud solution platform is screened to select the common-view satellites of the base station and the service station. Put the satellite numbers of the common-view satellites into the preparation list for relative positioning In addition, the signal-to-noise ratio screening and satellite elevation angle screening are performed to determine the satellite minimum signal-to-noise ratio threshold. and the satellite minimum altitude angle threshold EL Min , the minimum signal-to-noise ratio threshold of the above satellites and the satellite minimum altitude angle threshold EL Min Determined by the environment in which the mobile station receiver is located; if the signal-to-noise ratio of the satellite numbered i is Or satellite elevation angle EL i Satisfy the following formula: Then the satellite number of the satellite numbered i is deleted from the relative positioning list; If the number of common-view satellites after elevation angle and signal-to-noise ratio screening satisfies the following formula: Existence The common-view satellites that meet the above conditions represent sufficient current observation quantities. However, positioning fails due to the limited processing capability of the mobile station hardware platform or the wrong selection of observation quantities. The above common-view satellites are sorted by the star selection algorithm, using the signal-to-noise ratio-elevation angle joint model: Where a and b are model parameters, and their values ​​are given by experience. C / N0 is the signal-to-noise ratio of the satellite signal received by the receiver, and E is the altitude angle between the receiver and the current satellite. Since the common-view satellite is observed by two receivers together, there are two observation values. Therefore, the two observation values ​​are solved separately. The base station data and the mobile station data are respectively substituted into the above formula to calculate s i =s ri +s ui (10) In the formula, σ ri is the value obtained by the signal-to-noise ratio-elevation angle joint model of the base station data of the i-th satellite, σ ui is the value obtained by the signal-to-noise ratio-elevation angle joint model for the mobile station data of the i-th satellite; For the above σ i Sort and delete the values ​​that are greater than the threshold A; Relative positioning of the remaining satellites is performed to obtain floating-point solutions for their integer ambiguities; After solving the floating point solution, check the number of satellites again. If the number of common-view satellites that meet the conditions is greater than is the lower limit of the common-view satellite for the partial ambiguity fixation algorithm, and the threshold is determined by the number of enabled satellite systems; the integer ambiguity is solved by the partial ambiguity fixation algorithm; otherwise, the integer ambiguities of all common-view satellites are fixed by the full ambiguity fixation algorithm, and the precise coordinates of the mobile station are calculated on the basis of the coordinates of the reference station ECEF determined in step 1, and the current positioning result is saved, and this solution is completed; Step 5.5, the cloud solution platform performs a second solution, analyzes the mobile station hardware platform positioning solution results and the solution data uploaded by the mobile station hardware platform positioning, deletes the satellites with large variance in the mobile station hardware platform positioning process, reselects the satellites through the above algorithm, and then performs relative positioning in conjunction with the reference station satellite observations in the cloud solution platform. If positioning still fails, relax the threshold of the star selection algorithm, try different satellite combinations, obtain the positioning result with the smallest positioning variance, save the current positioning result, and end this solution; Step 6: Data forwarding and storage: if the positioning result is directly calculated by the mobile station hardware platform, the mobile station hardware platform will upload it to the cloud solution platform. The cloud solution platform will store the positioning result uploaded by the mobile station hardware platform in the database after receiving it. If the positioning is performed by the cloud solution platform, the positioning result will be sent to the mobile station hardware platform and the current positioning result will be stored locally.

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