Navigation positioning optimization system and method based on artificial intelligence

Abstract

The invention discloses an artificial intelligence-based navigation positioning optimization system and a method thereof, and belongs to the technical field of navigation positioning optimization. The method comprises: analyzing historical record data and establishing an abnormal display model; identifying each abnormal cause data according to the abnormal display model, generating each candidate solution according to the identified abnormal cause data, evaluating each candidate solution and obtaining a corresponding evaluation value; sorting each candidate solution in order of the solution evaluation value from small to large to obtain a first sequence; displaying the first sequence accordingly in the abnormal display model; identifying the first sequence in the abnormal display model, judging whether the first sequence is updated, and when it is judged that the update is not performed, not performing the corresponding operation; when it is judged that the update is performed, marking the updated candidate solution and prompting the corresponding management personnel; the management personnel determine the corresponding target optimization scheme, and perform positioning optimization according to the obtained target optimization scheme.

Description

Technical Field

[0001] The present invention belongs to the technical field of navigation and positioning optimization, and specifically relates to a navigation and positioning optimization system and method based on artificial intelligence. Background Art

[0002] With the popularization of global satellite navigation systems (such as GPS, Beidou navigation, etc.) and mobile devices, navigation and positioning technology has become an indispensable part of modern life. However, traditional navigation and positioning technology still has some problems and challenges in certain scenarios. For example, traditional navigation and positioning technology mainly relies on satellite signals for positioning, but in some areas (such as urban high-rise buildings, tunnels, basements, etc.), satellite signals may be weak or unable to be received, resulting in reduced positioning accuracy or inability to position; especially in the operation of the positioning system, its positioning defects are often not adjusted in time, resulting in a poor user experience in some cases; based on this, the present invention provides an artificial intelligence-based navigation and positioning optimization system and method thereof, which are used to optimize and improve the navigation system that is already in operation. Summary of the invention

[0003] In order to solve the problems existing in the above solutions, the present invention provides a navigation positioning optimization system and method based on artificial intelligence.

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

[0005] Navigation and positioning optimization system based on artificial intelligence, including data analysis module, optimization analysis module and optimization management module;

[0006] The data analysis module is used to analyze the historical record data and establish a corresponding abnormal display model, which is used to display the corresponding abnormal cause data, abnormal cause characteristics and abnormal cause probability in real time, and associate the corresponding abnormal classification data through the abnormal display model;

[0007] Furthermore, the method for establishing the abnormal display model includes:

[0008] Determine abnormality location data in the historical record data and abnormality cause data corresponding to the abnormality location data;

[0009] Classify each abnormal location data according to each abnormal cause data to obtain abnormal classification data corresponding to each abnormal cause data; perform abnormal feature extraction and analysis according to the abnormal cause data to obtain corresponding abnormal cause features;

[0010] Perform abnormal probability statistics based on abnormal classification data to determine the abnormal cause probability corresponding to each abnormal cause data;

[0011] Summarize the obtained abnormal cause data, abnormal cause characteristics, abnormal classification data, and abnormal cause probabilities, and establish a corresponding abnormal analysis library; establish a corresponding abnormal display model based on the abnormal analysis library.

[0012] Further, the method for determining abnormal location data includes:

[0013] Obtain a positioning standard, set a corresponding evaluation model based on the positioning standard, and the expression of the evaluation model is: ;

[0014] In the formula: q is historical record data; 1 or 0 is an abnormal evaluation value;

[0015] Evaluate the obtained historical record data through the evaluation model, mark the historical record data with an abnormal evaluation value of 1 as abnormal location data, and determine the corresponding abnormal cause data based on the abnormal location data and the positioning standard.

[0016] The optimization analysis module is used to perform positioning optimization analysis according to the abnormal display model, identify each abnormal cause data according to the abnormal display model, identify the corresponding abnormal classification data of the abnormal cause data, and set the corresponding abnormal impact value for the abnormal cause data according to the identified abnormal classification data;

[0017] Generate each candidate solution according to the identified abnormal cause data, label each candidate solution with the corresponding abnormal cause data label; evaluate the optimization cost of each candidate solution;

[0018] Obtain the abnormal cause probability corresponding to the abnormal cause data;

[0019] According to the formula Calculate the corresponding solution evaluation value;

[0020] In the formula: WA is the solution evaluation value; e is a constant; δ is the abnormal cause probability; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1, YZ is the abnormal impact value; YB is the optimization cost;

[0021] Sort each candidate solution in ascending order of the solution evaluation value to obtain a first sequence, and mark the solution evaluation value and evaluation factor values of each candidate solution in the first sequence. The evaluation factor values include abnormal cause probability, abnormal impact value, and optimization cost; display the obtained first sequence in the abnormal display model.

[0022] Further, the method for setting the abnormal impact value according to the abnormal classification data includes:

[0023] Identify each abnormal location data in the abnormal classification data, evaluate each abnormal location data, and obtain a single impact value corresponding to each abnormal location data; the value range of the single impact value is [0, 100]; mark the abnormal location data corresponding to the abnormal cause data as i, i=1, 2, ..., n, n is a positive integer; mark the single impact value as Di;

[0024] According to the formula Calculate the abnormal impact value corresponding to the abnormal cause data;

[0025] Where: YZ is the abnormal impact value; Dmax represents the maximum single impact value corresponding to the abnormal cause data; a is the single impact value located at The number of abnormal positioning data between Dmax.

[0026] The optimization management module is used to perform positioning optimization management, identify the first sequence in the abnormal display model, determine whether the first sequence is updated, and when it is determined that the update is not performed, no corresponding operation is performed; when it is determined that the update is performed, the updated candidate solution is marked and a prompt is given to the corresponding manager; the manager determines the corresponding target optimization plan, and performs positioning optimization according to the obtained target optimization plan.

[0027] Furthermore, it also includes a prompt optimization module, which is used to optimize the unresolved abnormal cause data, mark the corresponding unresolved abnormal cause data as the target cause; obtain the abnormal cause characteristics of each target cause, integrate each target cause and the corresponding abnormal cause characteristics into early warning benchmark data; establish a corresponding early warning evaluation model based on the early warning benchmark data; the expression of the early warning evaluation model is ; x is the intersection analysis data; the output data is the warning value;

[0028] Acquire road condition analysis data in real time, analyze the road condition analysis data in real time through the early warning evaluation model, and obtain the corresponding early warning value; perform corresponding early warning processing according to the obtained early warning value.

[0029] The navigation positioning optimization method based on artificial intelligence includes:

[0030] Analyze the historical record data and establish an abnormal display model; identify each abnormal cause data according to the abnormal display model, generate each candidate solution according to the identified abnormal cause data, evaluate each candidate solution and obtain a corresponding evaluation value; sort each candidate solution in order of the solution evaluation value from small to large to obtain a first sequence; and display the first sequence in the abnormal display model accordingly;

[0031] Identify the first sequence in the abnormal display model, determine whether the first sequence is updated, and do not perform corresponding operations when it is determined that the first sequence is not updated; when it is determined that the update is to be performed, mark the updated candidate solution and prompt the corresponding management personnel; the management personnel determine the corresponding target optimization solution, and perform positioning optimization according to the obtained target optimization solution;

[0032] Mark the unresolved abnormal cause data as target causes; obtain the abnormal cause characteristics of each target cause, and integrate each target cause and the corresponding abnormal cause characteristics into early warning benchmark data; obtain road condition analysis data in real time, analyze the road condition analysis data in real time through a preset early warning evaluation model, and perform corresponding early warning processing based on the analysis results.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] Through the cooperation between the data analysis module, the optimization analysis module and the optimization management module, the existing navigation and positioning system can be optimized in a targeted manner, solutions that meet the requirements can be intelligently generated, and managers can be efficiently assisted in optimizing the navigation and positioning system. By setting up the data analysis module, a large amount of historical record data generated by the navigation and positioning system can be intelligently analyzed, and the role of accumulated data can be deeply explored to help the platform party intuitively understand the details of the current navigation and positioning system problems, so that the staff can make targeted improvements. By setting up the prompt optimization module, the impact of existing abnormal causes on user use can be reduced, the user application experience can be improved, and the adaptive optimization of navigation and positioning can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0037] 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 part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1As shown, the navigation and positioning optimization system based on artificial intelligence includes a data analysis module, an optimization analysis module, an optimization management module and a prompt optimization module;

[0039] The data analysis module is used to analyze the historical record data of the navigation and positioning system, determine the abnormal positioning data in the historical record data and the abnormal cause data corresponding to the abnormal positioning data; classify each abnormal positioning data according to each abnormal cause data, and obtain the abnormal classification data corresponding to each abnormal cause data; perform abnormal feature extraction and analysis based on the abnormal cause data to obtain the corresponding abnormal cause feature; that is, when a specific abnormal cause data is determined, it can be determined why the positioning does not meet the positioning standards, such as network anomalies caused by building shielding, specific system failure causes, positioning equipment damage and other reasons, and then extract the performance characteristics of the corresponding reasons and integrate them into abnormal cause features.

[0040] Perform abnormal probability statistics based on abnormal classification data, determine the occurrence probability corresponding to each abnormal cause data, and mark the obtained occurrence probability as the abnormal cause probability;

[0041] Summarize the acquired abnormal cause data, abnormal cause characteristics, abnormal classification data and abnormal cause probability to establish a corresponding abnormal analysis library; subsequently update the abnormal analysis library in real time according to the changes of corresponding data;

[0042] A corresponding abnormal display model is established according to the abnormal analysis library. The abnormal display model is used to display the corresponding abnormal cause data, abnormal cause characteristics and abnormal cause probability in real time, and access jump to abnormal classification data can be performed based on the abnormal display model; specifically, the abnormal display model is a display model established based on existing display technology.

[0043] The method for determining abnormal location data includes:

[0044] Get the positioning standard set by the platform to determine which positioning conditions are normal and which are abnormal. The platform sets it according to the actual situation of the navigation and positioning system. Set the corresponding evaluation model according to the obtained positioning standard. The evaluation model is established based on the existing recognition and judgment technology and is used to identify historical record data and compare the identified historical record data with the positioning standard to determine whether it meets the positioning standard. The expression is: ; In the formula: q is the input data, that is, the historical record data; 1 or 0 is the abnormal evaluation value;

[0045] The acquired historical record data is evaluated through the evaluation model, and the historical record data with an abnormal evaluation value of 1 is marked as abnormal location data, and the data in the abnormal location data that does not meet the location criteria is marked and integrated into abnormal cause data.

[0046] By setting up a data analysis module, intelligent analysis is carried out on a large amount of historical record data generated by the navigation and positioning system, the role of the accumulated data is deeply explored, and the platform side is helped to intuitively understand the details of the current problems of the navigation and positioning system, which is convenient for the staff to make targeted improvements.

[0047] The optimization analysis module is used to perform positioning optimization analysis according to the abnormal display model, identify each abnormal cause data according to the abnormal display model, identify the corresponding abnormal classification data of the abnormal cause data, and set the corresponding abnormal influence value of the abnormal cause data according to the identified abnormal classification data;

[0048] Generate each candidate solution according to the identified abnormal cause data, evaluate based on various current advanced positioning technologies according to the specific cause to judge whether it can solve the problem, mark the technical solution or combined solution that can solve the problem as a candidate solution. This step uses existing retrieval technologies for retrieval to generate each candidate solution; label each candidate solution with the corresponding abnormal cause data label; evaluate the improvement cost of each candidate solution and mark it as the optimization cost;

[0049] Mark the obtained optimization cost as YB, and obtain the abnormal cause probability corresponding to the abnormal cause data;

[0050] According to the formula Calculate the corresponding solution evaluation value; in the formula: WA is the solution evaluation value; e is a constant; δ is the abnormal cause probability; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1, YZ is the abnormal influence value; YB is the optimization cost;

[0051] Sort each candidate solution in ascending order of the solution evaluation value to obtain the first sequence, and mark the solution evaluation value and evaluation factor value of each candidate solution in the first sequence. The evaluation factor values include the abnormal cause probability, abnormal influence value and optimization cost; display the obtained first sequence correspondingly in the abnormal display model.

[0052] The abnormal influence value is evaluated and set according to the degree of influence of the abnormal classification data on the user's navigation and positioning, and the value range is [0, 100]; taking the maximum influence degree as 100, such as various major safety accidents, and no influence as 0; subsequently, according to the size of each influence degree, insert them one by one between 0 and 100. Through a large amount of data simulation, an abnormal influence value matching table will be obtained, and corresponding matching can be carried out subsequently; the specific method is as follows:

[0053] Identify each abnormal location data in the abnormal classification data, evaluate each abnormal location data, obtain the abnormal impact value corresponding to each abnormal location data, and mark the abnormal impact value of the abnormal location data as a single impact value for distinction; mark the abnormal location data as i, i=1, 2, ..., n, n is a positive integer; mark the single impact value as Di;

[0054] According to the formula Calculate the abnormal impact value corresponding to the abnormal cause data;

[0055] Where: YZ is the abnormal impact value; Dmax represents the maximum single impact value corresponding to the abnormal cause data; a is the single impact value located at The number of abnormal positioning data between Dmax.

[0056] In one embodiment, the candidate solutions in the first sequence are screened and eliminated according to the solution evaluation values, such as eliminating the candidate solutions with solution evaluation values ​​greater than the threshold X1; or only retaining the top K candidate solutions in the sorting, where K is a positive integer; the specific screening method is set according to the needs of the platform.

[0057] The optimization management module is used to perform positioning optimization management, identify the first sequence in the abnormal display model, and determine whether the first sequence is updated. When it is determined that it is not updated, no corresponding operation is performed, that is, each candidate solution in the current first sequence has been understood and evaluated by the corresponding related personnel, and no repeated reminder is required; when it is determined that an update is to be performed, the updated candidate solution is marked and prompted to the corresponding management personnel; the management personnel determine the corresponding target optimization solution, which refers to the solution that can be applied after adjustment. Positioning optimization is performed according to the obtained target optimization solution.

[0058] Through the cooperation among the data analysis module, optimization analysis module and optimization management module, the existing navigation and positioning system can be optimized in a targeted manner, solutions that meet the requirements can be intelligently generated, and management personnel can be assisted to optimize the navigation and positioning system efficiently.

[0059] In one embodiment, since not all problems can be solved, and unsolved problems will always exist, and in this case, how to minimize the impact of abnormalities is a problem that needs to be solved, therefore, the present invention proposes a prompt optimization module;

[0060] The prompt optimization module is used to optimize the unresolved abnormal cause data, mark the corresponding unresolved abnormal cause data as target causes; obtain the abnormal cause characteristics of each target cause, and integrate each target cause and the corresponding abnormal cause characteristics into early warning benchmark data; establish a corresponding early warning evaluation model based on the early warning benchmark data, that is, use the existing similarity technology to establish it, and match the subsequently acquired intersection analysis data with the early warning benchmark data to determine whether it has abnormal cause characteristics that meet the early warning requirements; specifically, other existing technologies can also be used for judgment; the expression of the early warning evaluation model is ; In the formula: x is the input data, i.e. the intersection analysis data; 1 or 0 is the warning value; as long as one meets the standard, the warning value is 1;

[0061] Real-time acquisition of traffic analysis data refers to the relevant traffic data that users will encounter during navigation, mainly data that may affect navigation, such as signal differences that may be caused by culverts, mountain roads, etc.;

[0062] The road condition analysis data is analyzed in real time through the early warning evaluation model to obtain the corresponding early warning value; the corresponding early warning processing is carried out according to the obtained early warning value; if the early warning value is equal to 0, no early warning is issued; if the early warning value is equal to 1, an early warning is issued according to the preset corresponding early warning measures.

[0063] By setting up a prompt optimization module, the impact of existing abnormal causes on user usage can be reduced, the user application experience can be improved, and the adaptive optimization of navigation positioning can be achieved.

[0064] The navigation positioning optimization method based on artificial intelligence includes:

[0065] Analyze the historical record data and establish an abnormal display model; identify each abnormal cause data according to the abnormal display model, generate each candidate solution according to the identified abnormal cause data, evaluate each candidate solution and obtain a corresponding evaluation value; sort each candidate solution in order of the solution evaluation value from small to large to obtain a first sequence; and display the first sequence in the abnormal display model accordingly;

[0066] Identify the first sequence in the abnormal display model, determine whether the first sequence is updated, and do not perform corresponding operations when it is determined that the first sequence is not updated; when it is determined that the update is to be performed, mark the updated candidate solution and prompt the corresponding management personnel; the management personnel determine the corresponding target optimization solution, and perform positioning optimization according to the obtained target optimization solution;

[0067] Mark the unresolved abnormal cause data as target causes; obtain the abnormal cause characteristics of each target cause, and integrate each target cause and the corresponding abnormal cause characteristics into early warning benchmark data; obtain road condition analysis data in real time, analyze the road condition analysis data in real time through a preset early warning evaluation model, and perform corresponding early warning processing based on the analysis results.

[0068] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0069] 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 skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The navigation and positioning optimization system based on artificial intelligence is characterized by: Including data analysis module, optimization analysis module and optimization management module; The data analysis module is used to analyze the historical record data and establish a corresponding abnormal display model, which is used to display the corresponding abnormal cause data, abnormal cause characteristics and abnormal cause probability in real time, and associate the corresponding abnormal classification data through the abnormal display model; The optimization analysis module is used to perform positioning optimization analysis according to the abnormal display model, identify each abnormal cause data according to the abnormal display model, identify abnormal classification data corresponding to the abnormal cause data, and set the abnormal impact value corresponding to the abnormal cause data according to the identified abnormal classification data; Generate each candidate solution according to the identified abnormal cause data, and label each candidate solution with the corresponding abnormal cause data; evaluate the optimization cost of each candidate solution; obtain the abnormal cause probability corresponding to the abnormal cause data; Calculate the corresponding solution evaluation value based on the abnormal impact value, abnormal cause probability and optimization cost; Sort the candidate solutions in ascending order of solution evaluation value to obtain a first sequence, and mark the solution evaluation value and evaluation factor value of each candidate solution in the first sequence, where the evaluation factor value includes abnormal cause probability, abnormal impact value and optimization cost; The obtained first sequence is displayed correspondingly in the abnormal display model; The optimization management module is used to perform positioning optimization management, identify the first sequence in the abnormal display model, determine whether the first sequence is updated, and when it is determined that the first sequence is not updated, no corresponding operation is performed; when it is determined that the update is to be performed, the updated candidate solution is marked and a prompt is given to the corresponding management personnel; The corresponding target optimization plan is determined by the management personnel, and the positioning optimization is performed based on the obtained target optimization plan.

2. The navigation and positioning optimization system based on artificial intelligence according to claim 1 is characterized in that: The method for establishing the abnormal display model includes: Determine abnormality location data in the historical record data and abnormality cause data corresponding to the abnormality location data; Classify each abnormal location data according to each abnormal cause data to obtain abnormal classification data corresponding to each abnormal cause data; perform abnormal feature extraction and analysis according to the abnormal cause data to obtain corresponding abnormal cause features; Perform abnormal probability statistics based on abnormal classification data to determine the abnormal cause probability corresponding to each abnormal cause data; The acquired abnormal cause data, abnormal cause characteristics, abnormal classification data and abnormal cause probability are summarized to establish a corresponding abnormal analysis library; and a corresponding abnormal display model is established based on the abnormal analysis library.

3. The navigation and positioning optimization system based on artificial intelligence according to claim 2 is characterized in that: Methods for determining abnormal location data include: The positioning standard is obtained, and a corresponding evaluation model is set based on the positioning standard. The expression of the evaluation model is: Where: q is the historical record data; 1 or 0 is the abnormal evaluation value; The acquired historical record data is evaluated through the evaluation model, the historical record data with an abnormal evaluation value of 1 is marked as abnormal location data, and the corresponding abnormal cause data is determined according to the abnormal location data and the location standard.

4. The navigation and positioning optimization system based on artificial intelligence according to claim 1, characterized in that: Methods for setting anomaly impact values ​​based on anomaly classification data include: Identify each abnormal positioning data in the abnormal classification data, evaluate each abnormal positioning data, and obtain a single influence value corresponding to each abnormal positioning data; the value range of the single influence value is [0, 100]; mark the abnormal positioning data corresponding to the abnormal cause data as i, where i = 1, 2, ……, n, and n is a positive integer; mark the single influence value as Di. According to the formula Calculate the abnormal impact value corresponding to the abnormal cause data; Where: YZ is the abnormal impact value; Dmax represents the maximum single impact value corresponding to the abnormal cause data; a is the single impact value located at The number of abnormal positioning data between Dmax.

5. The navigation and positioning optimization system based on artificial intelligence according to claim 1 is characterized in that: The calculation method of the solution evaluation value includes: According to the formula WA=(e δ -1)×(b1×YZ+b2×1.1 YB ) Calculate the corresponding scheme evaluation value; In the formula: WA is the solution evaluation value; e is a constant; δ is the abnormal cause probability; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1, YZ is the abnormal influence value; YB is the optimization cost.

6. The navigation and positioning optimization system based on artificial intelligence according to claim 1, characterized in that: It further includes a prompt optimization module, which is used to optimize the unsolved abnormal cause data, mark the corresponding unsolved abnormal cause data as the target cause; obtain the abnormal cause characteristics of each target cause, and integrate each target cause and the corresponding abnormal cause characteristics into the warning reference data. Based on the early warning benchmark data, a corresponding early warning evaluation model is established; the expression of the early warning evaluation model is x is the intersection analysis data; the output data is the warning value; Obtain the road condition analysis data in real time, perform real-time analysis on the road condition analysis data through the warning evaluation model, and obtain the corresponding warning value; perform corresponding warning processing according to the obtained warning value.

7. The navigation positioning optimization method based on artificial intelligence is characterized by: Applied to the navigation positioning optimization system based on artificial intelligence as described in any one of claims 1 to 6, the method includes: Analyze the historical record data to establish an abnormal display model; identify each abnormal cause data according to the abnormal display model, generate each candidate solution according to the identified abnormal cause data, evaluate each candidate solution, and obtain the corresponding evaluation value; sort each candidate solution in ascending order of the solution evaluation value to obtain the first sequence; display the first sequence in the abnormal display model. Identify the first sequence in the abnormal display model, determine whether the first sequence is updated. When it is determined that there is no update, no corresponding operation is performed; when it is determined that there is an update, mark the updated candidate solution and prompt the corresponding management personnel; the management personnel determine the corresponding target optimization solution, and perform positioning optimization according to the obtained target optimization solution. Mark the unsolved abnormal cause data as the target cause; obtain the abnormal cause characteristics of each target cause, and integrate each target cause and the corresponding abnormal cause characteristics into the warning reference data; obtain the road condition analysis data in real time, perform real-time analysis on the road condition analysis data through the preset warning evaluation model, and perform corresponding warning processing according to the analysis result.

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