Intelligent Positioning, Navigation and Mapping System Based on Driverless

Abstract

The present invention discloses an intelligent positioning and navigation mapping system based on driverless technology, belonging to the field of mapping technology, which includes a mapping analysis module, an action module and a mapping module; the mapping analysis module is used to analyze the mapping area to determine the target mapping area; the action module is used to determine the driverless device according to the target mapping area, and control the driverless device to reach the target mapping area, and collect data of the target mapping area through the mapping equipment equipped on the driverless device to obtain corresponding mapping data; the mapping module is used to analyze the mapping data to obtain the verification data of the target mapping area; receive the mapping data, process the abnormal mapping feature data of the mapping data to obtain corresponding mapping standard data; compare the verification data with the mapping standard data to determine the corresponding mapping update data, and perform security processing on the mapping update data; adjust the verification data according to the mapping update data.

Description

Technical Field

[0001] The present invention belongs to the field of surveying and mapping technology, and specifically relates to an intelligent positioning and navigation surveying and mapping system based on unmanned driving. Background Art

[0002] With the rapid development of technology, positioning and navigation technology has been increasingly widely used in the field of surveying and mapping. Traditional surveying and mapping methods usually rely on manual operation and complex equipment, which are not only inefficient but also easily affected by human factors, resulting in inaccurate surveying and mapping results. In recent years, with the development of high-precision sensor technology, intelligent positioning and navigation surveying and mapping systems have gradually emerged. Existing intelligent positioning and navigation surveying and mapping systems mainly rely on a variety of sensors and positioning technologies to achieve high-precision surveying and mapping. For example, lidar can obtain terrain information by measuring the distance between the laser beam and the target object; optical cameras and depth cameras can obtain high-resolution image data for identifying and measuring target objects; while the Global Positioning System (GPS) and Inertial Navigation System (INS) can provide accurate navigation and positioning information for unmanned devices. The common method is to load surveying and mapping equipment on a vehicle and conduct surveying and mapping in the surveying area by manually driving the vehicle; however, this method has certain limitations. For example, in some special areas, it is impossible to drive a vehicle, making it difficult to conduct surveying and mapping; moreover, when subsequent surveying and mapping updates are required, the efficiency is low. Therefore, it is still of great practical significance and market demand to develop a more advanced, efficient, and stable intelligent positioning and navigation surveying and mapping system. Based on this, the present invention provides an intelligent positioning and navigation surveying and mapping system based on unmanned driving. Summary of the Invention

[0003] In order to solve the problems existing in the above solutions, the present invention provides an intelligent positioning and navigation surveying and mapping system based on unmanned driving.

[0004] The object of the present invention can be achieved by the following technical solutions:

[0005] An intelligent positioning and navigation surveying and mapping system based on unmanned driving includes a surveying and mapping analysis module, an action module, and a surveying and mapping module;

[0006] The surveying and mapping analysis module is used to analyze the surveying and mapping area to determine the target surveying and mapping area.

[0007] Further, the method for determining the target surveying and mapping area includes:

[0008] Identify the surveying and mapping area and obtain the surveying and mapping map corresponding to the surveying and mapping area; obtain the surveying and mapping correlation data corresponding to each area in the surveying and mapping map in real time, mark each piece of the surveying and mapping correlation data in the surveying and mapping map, and form a correlation data set corresponding to each area;

[0009] Identify the associated data sets corresponding to each region in real time according to the surveying and mapping map, analyze the associated data sets, and determine the target surveying and mapping area.

[0010] Furthermore, the method for obtaining surveying and mapping associated data includes:

[0011] Establish an information retrieval model, perform real-time information retrieval through the information retrieval model, and obtain the regional retrieval information corresponding to the surveying and mapping area;

[0012] Perform real-time evaluation on the regional retrieval information through a preset information evaluation model to obtain the corresponding surveying and mapping associated data;

[0013] The expression of the information evaluation model is In the formula: x is the regional retrieval information; the regional retrieval information corresponding to XL(x)=1 is the surveying and mapping associated data.

[0014] Furthermore, the method for analyzing the associated data sets includes:

[0015] Identify each surveying and mapping associated data in the associated data set, and identify the candidate time and information source corresponding to the surveying and mapping associated data;

[0016] Match the corresponding initial trust value according to the information source of each surveying and mapping associated data; mark the surveying and mapping associated data with the same initial trust value and candidate time as associated classification data; determine the corresponding representative trust value and target time according to each associated classification data;

[0017] Establish a time evaluation model, and the expression of the time evaluation model is In the formula: a is the target time;

[0018] Calculate the corresponding surveying and mapping evaluation value according to the formula PGL = ST(a)×XW;

[0019] In the formula: PGL is the surveying and mapping evaluation value; ST(a) is the time evaluation model; a is the target time; XW is the representative trust value;

[0020] Mark the area where the surveying and mapping evaluation value is greater than the threshold X1 as the target surveying and mapping area.

[0021] Furthermore, the method for determining the corresponding representative trust value and target time according to each associated classification data includes:

[0022] Identify the number of surveying and mapping associated data in the associated classification data, and mark it as the common number;

[0023] According to the formula XW = XA×1.08 LG Calculate the classification trust value corresponding to each associated classification data; in the formula: XW is the classification trust value; XA is the initial trust value; LG is the common number;

[0024] Select the highest classification trust value as the representative trust value, and mark the candidate time corresponding to the representative trust value as the target time.

[0025] The action module is used to determine an unmanned device according to the target survey area, control the unmanned device to reach the target survey area, and collect data of the target survey area through the survey equipment equipped on the unmanned device to obtain corresponding survey data.

[0026] Further, the method for determining the unmanned device includes:

[0027] Establish an equipment information library, which is used to store various unmanned device information and the equipment survey characteristics corresponding to each unmanned device;

[0028] Collect the area information of the target survey area, generate corresponding target area characteristics according to the area information; compare the equipment survey characteristics in the equipment information library according to the obtained target area characteristics to obtain each candidate device that meets the survey requirements;

[0029] Evaluate each candidate device to obtain the applied unmanned device.

[0030] Further, the method for evaluating the candidate device includes:

[0031] Estimate the required time for surveying the target survey area according to the unmanned device information of the candidate device, and mark it as the survey duration; and set the corresponding survey accuracy value according to the unmanned device information of the candidate device, and the value range of the survey accuracy value is [60, 100];

[0032] Compare each pair of candidate devices, and the comparison formula is:

[0033]

[0034] Where: QU1 and QU2 are the comparison values of two compared candidate devices; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; CL1 and CL2 are the survey durations of two compared candidate devices respectively; CP1 and CP2 are the survey accuracy values of two compared candidate devices respectively;

[0035] Compare QU1 and QU2, and the one with the larger comparison value has a higher priority; retain the candidate device with a higher priority, and eliminate the other compared candidate device, and so on, until there is only one candidate device left, and regard the remaining single selected device as the applied unmanned device.

[0036] The surveying and mapping module is used to analyze surveying and mapping data, including a front-end unit and a back-end unit. The front-end unit is arranged on the unmanned device; obtain the verification data of the target surveying and mapping area, and store the verification data in the front-end unit after security processing;

[0037] The front-end unit receives the surveying and mapping data, processes the abnormal surveying and mapping feature data of the surveying and mapping data, and obtains the corresponding surveying and mapping standard data;

[0038] Compare the verification data with the surveying and mapping standard data to determine the corresponding surveying and mapping update data, and perform security processing on the surveying and mapping update data;

[0039] Transmit the surveying and mapping update data after security processing to the back-end unit according to the preset transmission method, and the back-end unit adjusts the verification data according to the received surveying and mapping update data.

[0040] Furthermore, the method for processing abnormal surveying and mapping feature data of surveying and mapping data includes:

[0041] Define abnormal surveying and mapping feature data, establish a corresponding abnormal surveying and mapping recognition model according to the defined abnormal surveying and mapping feature data, analyze the surveying and mapping data through the abnormal surveying and mapping recognition model, identify each abnormal surveying and mapping feature data, and remove the identified abnormal surveying and mapping feature data from the surveying and mapping data.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Through the mutual cooperation among the surveying and mapping analysis module, the action module and the surveying and mapping module, intelligent positioning and navigation surveying and mapping based on unmanned driving are realized; the surveying and mapping tasks can be completed independently without manual intervention, greatly improving the work efficiency; it can adapt to different environments and application scenarios, such as land, water, air, etc., and has wide applicability; and accurate positioning of detailed surveying and mapping update data is realized, corresponding security processing is carried out, and data leakage is avoided. Through the setting of the surveying and mapping analysis module, intelligent analysis of the surveying and mapping area is realized, and it is automatically determined which areas are the target surveying and mapping areas, greatly reducing the manual burden, and at the same time having extremely high positioning efficiency, facilitating timely update of the surveying and mapping information, and ensuring the accuracy of positioning and navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1This is the principle block diagram of the present invention. Detailed implementation manners

[0046] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] As Figure 1 shown, the intelligent positioning navigation and mapping system based on driverless includes a mapping analysis module, an action module, and a mapping module;

[0048] The mapping analysis module is used to determine the target mapping area, and the process is as follows:

[0049] Identify the mapping area. The mapping area is an area that needs to be positioned and navigated for mapping, such as a city, a town, a custom marked area, etc.; that is, an overall area; the target mapping area is the area marked in the mapping area that needs to be mapped this time; the present invention mainly aims at the subsequent update and maintenance after the overall mapping of the mapping area, but can also be applied to the previous overall mapping. The mapping area can be directly marked as the target mapping area, and then a comprehensive mapping can be carried out later. This part will not be described in detail in the present invention; mainly for the embodiments of the subsequent update and maintenance after the overall mapping of the mapping area.

[0050] Establish an information retrieval model. The information retrieval model is used to retrieve various relevant data within the mapping area, and is established by using existing retrieval technologies. The main retrieval direction is whether there are changes in mapping information within the mapping area, such as building, road and other relevant information. Therefore, corresponding retrieval keywords will be set according to the mapping requirements to narrow the retrieval scope and improve the retrieval efficiency; real-time information retrieval is carried out through the information retrieval model to obtain the corresponding regional retrieval information.

[0051] Simulate and set a large number of retrieval simulation information. The retrieval simulation information is the regional retrieval information simulated and set. An information evaluation model is established according to the retrieval simulation information. The retrieval simulation information that meets the mapping change requirements is regarded as abnormal data, and other data is regarded as normal data. Therefore, an information evaluation model is established based on the existing isolation forest algorithm, and the expression is In the formula: x is the input data, that is, the regional retrieval information.

[0052] Real-time evaluation of regional retrieval information is carried out through an information evaluation model to obtain corresponding abnormal data, and the abnormal data is marked as surveying and mapping related data; identify the location area corresponding to the surveying and mapping related data; obtain the surveying and mapping map of the surveying and mapping area, and mark the corresponding surveying and mapping related data in the surveying and mapping map according to the location area corresponding to the surveying and mapping related data to form an associated data set corresponding to each area; generally represented by relevant features such as codes, and associate the corresponding access link; specifically, set the corresponding marking method according to actual needs.

[0053] Identify the associated data set corresponding to each area in real time according to the surveying and mapping map, analyze the associated data set, and determine whether the area needs to be updated in surveying and mapping. If it is determined that surveying and mapping update is required, mark the corresponding area as the target surveying and mapping area; otherwise, no corresponding operation is performed.

[0054] Analyze the associated data set, mainly based on the corresponding time and information credibility for analysis; the specific process is as follows:

[0055] Identify each surveying and mapping related data in the associated data set, and identify the candidate time and information source corresponding to the surveying and mapping related data; the candidate time is the time of surveying and mapping changes reflected in the surveying and mapping related data, mainly including changes on a certain future day, already changed, etc. For example, although the time is not indicated in the released data, but construction has already started, it is regarded as already changed;

[0056] Match the corresponding initial trust value according to the information source of each surveying and mapping related data; that is, the platform aggregates various possible information sources, such as individuals, a certain platform, website, official website, etc., and the platform sets the corresponding initial trust value for each information source according to the credibility of various information sources. The value range of the initial trust value is [0, 100];

[0057] Identify each surveying and mapping related data with the same initial trust value and candidate time, mark it as associated classification data, identify the quantity of surveying and mapping related data in the associated classification data, and mark it as the common quantity;

[0058] According to the formula XW = XA × 1.08 LG Calculate the classification trust value corresponding to each associated classification data; in the formula: XW is the classification trust value; XA is the initial trust value; LG is the common quantity;

[0059] Select the highest classification trust value and mark it as the representative trust value, and mark the candidate time corresponding to the representative trust value as the target time.

[0060] Establish a time evaluation model. The time evaluation model is used to evaluate whether the target time meets the updated surveying and mapping timing standard. For example, if there is a change on a certain future day, but it has not changed yet, there is no need to update, which does not meet the standard; if the time has changed before the last resurvey and update, since it has already been resurveyed and updated, it does not meet the requirements, and so on. According to the evaluation common sense, establish a time evaluation model and manually set the corresponding time evaluation criteria; the expression is In the formula: a is the input data, that is, the target time;

[0061] Calculate the corresponding surveying and mapping evaluation value according to the formula PGL = ST(a) × XW; in the formula: PGL is the surveying and mapping evaluation value; ST(a) is the time evaluation model; a is the target time; XW is the representative trust value, that is, the corresponding classification trust value.

[0062] When the surveying and mapping evaluation value is greater than the threshold X1, it is determined that surveying and mapping update is required, that is, the area where the surveying and mapping evaluation value is greater than the threshold X1 is marked as the target surveying and mapping area; otherwise, no surveying and mapping update is performed.

[0063] Through the settings of the surveying and mapping analysis module, realize the intelligent analysis of the surveying and mapping area, automatically determine which areas are the target surveying and mapping areas, greatly reduce the manual burden, and at the same time have extremely high positioning efficiency, facilitate the timely update of surveying and mapping information, and ensure the accuracy of positioning and navigation.

[0064] The action module is used to intelligently determine the unmanned driving device according to the target surveying and mapping area, control the unmanned driving device to reach the target surveying and mapping area, and collect corresponding surveying and mapping data through the surveying and mapping equipment equipped on the unmanned driving device.

[0065] Among them, the control of the unmanned driving device and the data collection of the surveying and mapping equipment are both realized by using existing technologies. The unmanned driving device includes various devices such as unmanned aerial vehicles, unmanned vehicles, and unmanned ships.

[0066] The method for determining the unmanned driving device includes:

[0067] The platform party determines each unmanned driving device it has, obtains the information of each unmanned driving device, and sets the device surveying and mapping characteristics of each unmanned driving device, such as various relevant data such as which surveying and mapping environments it is suitable for, surveying and mapping accuracy, and device performance; after corresponding summarization, establish a corresponding device information library, that is, the device information library is used to store the information of various unmanned driving devices and the corresponding device surveying and mapping characteristics; the platform party establishes, maintains, and updates the device information library.

[0068] Identify the corresponding area information of the target survey area, such as data on target information to be surveyed, road conditions, various influencing factors, etc.; specifically, the platform party presets a corresponding data collection template to collect information on the target survey area, and subsequently extracts features according to each collection item in the data collection template to form the target area features corresponding to the target survey area; mainly various data related to the use of unmanned devices; such as whether the surveying ability can meet the surveying requirements and whether the surveying work can be carried out normally;

[0069] Compare the surveying features of each device in the device information library with the obtained target area features to obtain each unmanned device that can meet the surveying requirements, and mark them as candidate devices;

[0070] Evaluate each candidate device to obtain the applied unmanned device.

[0071] Among them, various methods can be used to evaluate each candidate device, such as random assignment, manual assignment, assignment according to a preset schedule, etc. It can also be evaluated in the following way. The method includes:

[0072] Estimate the required time for surveying the target survey area based on the unmanned device information corresponding to the candidate device, and mark it as the surveying duration; and set the corresponding surveying accuracy value according to the unmanned device information of the candidate device. The surveying accuracy value is evaluated according to the surveying accuracy of this candidate device and the comprehensiveness of the surveying target; it can be summarized by the platform party for the surveying accuracy values of various unmanned devices under different surveying targets and then matched later; it can also be based on neural networks such as CNN network or DNN network to establish a corresponding surveying analysis model, and a corresponding training set is established and trained manually. The training set includes input data and output data. The input data includes target area features and unmanned device information, and the output data is the surveying accuracy value. The value range of the surveying accuracy value is [60, 100]; analyze through the successfully trained surveying analysis model to obtain the corresponding surveying accuracy value.

[0073] Compare each pair of candidate devices, and retain the candidate device with a higher priority, and eliminate the other candidate device being compared. By analogy, until there is only one candidate device left, regard the remaining single selected device as the applied unmanned device.

[0074] The comparison formula is: In the formula: QU1 and QU2 are the comparison values of the two candidate devices being compared; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; CL1 and CL2 are the surveying durations of the two candidate devices being compared; CP1 and CP2 are the surveying accuracy values of the two candidate devices being compared;

[0075] Compare QU1 and QU2, and the one with a larger comparison value has a higher priority.

[0076] The mapping module is used to analyze mapping data, including a front-end unit and a back-end unit. Among them, the front-end unit is set on the unmanned device; obtain the verification data of the target mapping area, that is, the mapping data of the current target mapping area, regarded as verification data; after performing security processing on the verification data, store it in the front-end unit, that is, process it using existing data encryption security measures.

[0077] The front-end unit obtains mapping data, processes the mapping data for abnormal mapping feature data, and obtains corresponding mapping standard data; that is, identify and mark the data not related to mapping, such as the data of surrounding pedestrians and passing vehicles collected, and define it as abnormal mapping feature data; specifically, establish a corresponding abnormal mapping recognition model based on existing technologies to facilitate identification according to the preset abnormal mapping feature data, and eliminate the identified abnormal mapping feature data; commonly, establish an abnormal mapping recognition model based on neural networks, and other technologies can also be applied for establishment.

[0078] Compare the obtained verification data and mapping standard data to determine the corresponding mapping update data, and perform security processing on the mapping update data, including security processing measures such as data encryption; because only the mapping update data is involved, the data volume is greatly reduced, which is convenient for encryption processing, and more adaptable methods can be used to delete the non-mapping update data; that is, all the above steps are carried out on the unmanned device.

[0079] Transmit the mapping update data to the back-end unit according to the preset transmission method, and the back-end unit adjusts the verification data according to the received mapping update data. The preset transmission method is set by the platform party.

[0080] Among them, compare the obtained verification data and mapping standard data, that is, compare the corresponding data one by one to identify the part of the mapping update; extract it as the mapping update data.

[0081] Through the mutual cooperation among the mapping analysis module, the action module, and the mapping module, realize intelligent positioning and navigation mapping based on unmanned driving; can independently complete the mapping task without manual intervention, greatly improving the work efficiency; can adapt to different environments and application scenarios, such as land, water area, air, etc., with wide applicability; and realize accurate positioning of detailed mapping update data, perform corresponding security processing, and avoid data leakage.

[0082] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through a large amount of data simulation.

[0083] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. Unmanned driving-based intelligent positioning, navigation and mapping system, characterized by: It includes surveying and mapping analysis module, action module and surveying and mapping module; The surveying and mapping analysis module is used to analyze the surveying and mapping area and determine the target surveying and mapping area; The action module is used to determine the unmanned driving device according to the target surveying and mapping area, and control the unmanned driving device to reach the target surveying and mapping area, collect data of the target surveying and mapping area through the surveying and mapping equipment equipped by the unmanned driving device, and obtain corresponding surveying and mapping data; The surveying and mapping module is used to analyze the surveying and mapping data, and includes a front-end unit and a back-end unit, wherein the front-end unit is arranged on the unmanned driving device; obtains the verification data of the target surveying and mapping area, and stores the verification data in the front-end unit after security processing; The front-end unit receives the surveying and mapping data, performs abnormal surveying and mapping feature data processing on the surveying and mapping data, and obtains corresponding surveying and mapping standard data; Comparing the verification data with the surveying and mapping standard data, determining corresponding surveying and mapping update data, and performing security processing on the surveying and mapping update data; The surveying and mapping update data after security processing is transmitted to the back-end unit according to the preset transmission method, and the back-end unit adjusts the verification data according to the received surveying and mapping update data.

2. The unmanned intelligent positioning, navigation and mapping system according to claim 1 is characterized in that: Methods for determining the target surveying and mapping area include: Identify the surveying and mapping area, and obtain the surveying and mapping map corresponding to the surveying and mapping area; obtain the surveying and mapping related data corresponding to each area in the surveying and mapping map in real time, mark each of the surveying and mapping related data in the surveying and mapping map, and form a related data set corresponding to each area; According to the surveying and mapping map, the associated data sets corresponding to each area are identified in real time, and the associated data sets are analyzed to determine the target surveying and mapping area.

3. The unmanned intelligent positioning, navigation and mapping system according to claim 2 is characterized in that: Methods for obtaining surveying and mapping related data include: Establishing an information retrieval model, performing real-time information retrieval through the information retrieval model, and obtaining regional retrieval information corresponding to the surveying and mapping area; The regional retrieval information is evaluated in real time through the preset information evaluation model to obtain the corresponding surveying and mapping related data; The expression of the information evaluation model is: Wherein: x is the regional retrieval information; when XL(x)=1, the corresponding regional retrieval information is the surveying and mapping associated data.

4. The unmanned intelligent positioning, navigation and mapping system according to claim 3 is characterized in that: Methods for analyzing linked datasets include: Identify each surveying and mapping related data in the related data set, and identify the candidate time and information source corresponding to the surveying and mapping related data; Match the corresponding initial trust value according to the information source of each surveying and mapping related data; mark each surveying and mapping related data with the same initial trust value and candidate time as related classification data; determine the corresponding representative trust value and target time according to each related classification data; A time evaluation model is established, and the expression of the time evaluation model is: Where: a is the target time; Calculate the corresponding surveying and mapping assessment value according to the formula PGL = ST (a) × XW; Where: PGL is the surveying and mapping evaluation value; ST(a) is the time evaluation model; a is the target time; XW is the representative trust value; The area where the surveying and mapping evaluation value is greater than the threshold X1 is marked as the target surveying and mapping area.

5. The unmanned intelligent positioning, navigation and mapping system according to claim 4 is characterized in that: The method for determining the corresponding representative trust value and target time according to each associated classification data includes: Identify the number of mapping related data in the related classification data and mark them as common quantities; According to the formula XW = XA × 1.08 LG Calculate the classification trust value corresponding to each associated classification data; where: XW is the classification trust value; XA is the initial trust value; LG is the common number; The highest classification trust value is selected and marked as the representative trust value, and the candidate time corresponding to the representative trust value is marked as the target time.

6. The unmanned intelligent positioning, navigation and mapping system according to claim 1, characterized in that: Methods for determining unmanned equipment include: Establishing an equipment information database, wherein the equipment information database is used to store various unmanned driving equipment information and equipment mapping features corresponding to each unmanned driving equipment; Collect the regional information of the target surveying and mapping area, and generate the corresponding target area features according to the regional information; compare the device surveying and mapping features in the device information library according to the obtained target area features to obtain the candidate devices that meet the surveying and mapping requirements; Evaluate each candidate device to obtain the applied unmanned driving device.

7. The unmanned intelligent positioning, navigation and mapping system according to claim 6, characterized in that: The method for evaluating candidate devices includes: Estimate the required time for surveying and mapping the target surveying and mapping area according to the unmanned driving device information of the candidate device, and mark it as the surveying and mapping duration; and set the corresponding surveying and mapping accuracy value according to the unmanned driving device information of the candidate device, and the value range of the surveying and mapping accuracy value is [60, 100]; Compare each pair of candidate devices, and the comparison formula is: and In the formula: QU1 and QU2 are the comparison values of the two candidate devices being compared; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; CL1 and CL2 are the surveying and mapping durations of the two candidate devices being compared; CP1 and CP2 are the surveying and mapping accuracy values of the two candidate devices being compared; Compare QU1 and QU2, and the one with the larger comparison value has a higher priority; retain the candidate device with the higher priority, and eliminate the other candidate device being compared, and so on until only one candidate device remains, and regard the remaining single selected device as the applied unmanned driving device.

8. The unmanned intelligent positioning, navigation and mapping system according to claim 1, characterized in that: The method for processing abnormal surveying and mapping feature data of surveying and mapping data includes: Define abnormal surveying and mapping feature data, establish a corresponding abnormal surveying and mapping recognition model according to the defined abnormal surveying and mapping feature data, analyze the surveying and mapping data through the abnormal surveying and mapping recognition model, identify each abnormal surveying and mapping feature data, and eliminate the identified abnormal surveying and mapping feature data from the surveying and mapping data.

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