A positioning data management system and method based on multi-sensor information fusion

Abstract

The present invention relates to the field of positioning data management, and in particular to a positioning data management system and method based on multi-sensor information fusion, comprising a vehicle positioning data acquisition module, a user positioning data response module, a monitoring period marking module, a target positioning event determination module, an objective characteristic factor analysis module and an early warning reminder module; the vehicle positioning data acquisition module is used to acquire vehicle positioning data transmitted by a vehicle network based on an indoor space when a user responds to a positioning demand; the user positioning data response module is used to respond to monitoring user positioning data after receiving and displaying a vehicle positioning route at a user device end; the monitoring period marking module is used to end monitoring when the user positioning data is the same as the vehicle positioning point, and mark the period corresponding to the response monitoring to the end of monitoring as the monitoring period; and the target positioning event determination module is used to mark a positioning event that displays a difference between a user positioning route and a vehicle positioning route at a user device end as a target positioning event.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning data management, and in particular to a positioning data management system and method based on multi-sensor information fusion. Background Art

[0002] At present, the most widely used field of positioning data is vehicle positioning systems. The existing vehicle positioning systems are very mature and can be applied to different scenarios to achieve vehicle positioning. However, for some indoor spaces, such as underground parking lots, different road environments may cause differences between user positioning routes and vehicle positioning routes. This may be due to the presence of some route directions in the spatial environment that are easy to mislead users, resulting in users being unable to accurately judge the direction of their movements; or it may be due to route deviations caused by unstable signals caused by the positioning system used by the user's device in the current indoor space. This results in mismatched time consumption when users search for vehicles based on the vehicle positioning route, reducing the efficiency of users solving problems based on positioning data. Summary of the invention

[0003] The object of the present invention is to provide a positioning data management system and method based on multi-sensor information fusion to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a positioning data management method based on multi-sensor information fusion, comprising the following analysis steps:

[0005] Step S100: obtaining vehicle positioning data transmitted by the Internet of Vehicles based on the indoor space when the user responds to the positioning demand, the Internet of Vehicles communicates with the user device to transmit the vehicle positioning data to the user device; the vehicle positioning data includes the vehicle positioning route and the vehicle positioning point; the user device responds to monitor the user positioning data after receiving and displaying the vehicle positioning route;

[0006] Step S200: When the user positioning data and the vehicle positioning point are the same, the monitoring ends, the time period corresponding to the response monitoring to the end of the monitoring is marked as the monitoring period, and the user positioning route formed by the user positioning data in the monitoring period is extracted; the positioning event showing the difference between the user positioning route and the vehicle positioning route on the user device side is marked as the target positioning event;

[0007] Step S300: determining objective characteristic factors affecting route differences in the corresponding indoor space based on the target positioning event;

[0008] Step S400: When there is a real-time user response to the vehicle positioning data, an early warning reminder is given to the real-time user terminal based on objective characteristic factors.

[0009] Further, objective characteristic factors corresponding to the differences in the indoor space influence routes are determined, including the following analysis steps:

[0010] Step S310: Obtain the turning points of the vehicle positioning route recorded by the target positioning event, the route distances between adjacent turning points, and the turning angles; mark the positioning routes with the same turning points, turning angles, and route distances between adjacent turning points as the same type of positioning route, and divide the target positioning events into one type of positioning event according to the corresponding same type of positioning route based on the marking;

[0011] Step S320: Obtain the types of the user terminal positioning methods recorded by one type of positioning event. When the types are not unique, extract the positioning event corresponding to the same type of user terminal positioning method and the highest recorded event times as the effective positioning event; obtain the deflection points where there are differences between the positioning routes of each user and the vehicle positioning route recorded by the effective positioning event. The deflection point refers to the starting point where the positioning data of the recorded user is separated from the vehicle positioning route;

[0012] Obtain the number of types N1 of the initial deflection points recorded in the effective positioning events. Deflection points at different positions represent one type, and the initial deflection point represents the starting point where the first separation occurs during the monitoring period; use the formula:

[0013] X1 = (N1 / M1) * [kmax / (kmax - kmin)],

[0014] Calculate the first characteristic index X1 of the initial deflection points recorded by the corresponding effective positioning events in one type of positioning event, where M1 represents the number of event records of the effective positioning event, k represents the number of times each type of initial deflection point is recorded, kmax represents the maximum value of the number of times the initial deflection point is recorded, and kmin represents the minimum value of the number of times the initial deflection point is recorded; set the first characteristic index threshold X0, traverse all the effective positioning events in each type of positioning event, and mark the vehicle positioning route corresponding to the effective positioning event when X1 < X0 as the suspicious route; the smaller the characteristic index, the greater the influence of the deflection points existing in the corresponding vehicle positioning route on the user's behavior based on the positioning data;

[0015] Step S330: extract the number of times recorded by each initial deflection point in the doubtful route, and take the initial deflection point corresponding to the maximum number of recorded times k1max as the first objective characteristic factor; determining the doubtful route can effectively explain that there may be some spatial positions in the current monitoring space that are prone to cause deviations for users using positioning data due to internal spatial structural problems; if the users who are most likely to deviate are reminded at the user end, the occurrence of user positioning data deviations caused by spatial objective factors can be effectively reduced. If the first characteristic indexes of all valid positioning events in each type of positioning event satisfy X1≥X0, it means that the structural characteristics in the current monitoring space are not the objective reasons for the obvious deviation of user positioning data; the same type of positioning events are classified as port positioning events according to the different positioning methods of each user end; based on the port positioning event, the second objective characteristic factor affecting the route difference in the indoor space is determined.

[0016] Further, determining a second objective characteristic factor affecting route differences in indoor space based on the port positioning event includes the following specific steps:

[0017] Step S331: Obtain the number of times m when different deflection points are recorded in the i-th type of port positioning event in each type of positioning event i ,Analysis of different deflection points recorded in the same type of port positioning events shows that when the user positioning method is not effective, the positioning deviation or error may be diverse; Calculate the abnormal rate Y of the i-th type of port positioning event i , Y i =m i / p, p represents the number of total port positioning events recorded in the corresponding type of positioning event;

[0018] The larger the abnormal rate, the greater the difference in the impact of the positioning method corresponding to the port positioning event on the user route and the more difficult it is to determine the inflection point. Retrieve the number of positioning events with the same positioning method as the i-th type of port positioning event in all types of positioning events and calculate the abnormal rate under the corresponding type of positioning event; use the formula:

[0019] Z i =a1*(U i / V)+a2*[(1 / U i )∑Y i ],

[0020] Calculate the second characteristic index Z of the i-th type port positioning event i , U i represents the total number of types of the i-th type of port location event recorded in each location event, V represents the total number of types of location type events; a1 represents the first reference coefficient corresponding to the proportion of port location events, and a2 represents the second reference coefficient corresponding to the average abnormality rate;

[0021] Step S332: sorting the user terminal positioning modes corresponding to the port positioning events from large to small according to the values ​​of the second characteristic indexes to generate a first sequence;

[0022] Step S333: extracting the first user terminal positioning mode in the first sequence as the second objective characteristic factor affecting route differences in the indoor space.

[0023] Further, step S400 includes the following:

[0024] Obtaining a first objective characteristic factor and a second objective characteristic factor of each indoor space record;

[0025] When the real-time user responds to the vehicle positioning data, the real-time vehicle positioning route is obtained. When the real-time vehicle positioning route contains the first objective characteristic factor, a reminder signal of the positioning data corresponding to the first objective characteristic factor is transmitted to the user device end; so as to remind the user that it is easy to go wrong at the positioning data corresponding to the first objective characteristic factor, and determine the real route;

[0026] The real-time positioning mode of the user device is obtained. When the real-time positioning mode is the same as the second objective characteristic factor, a signal for changing the positioning mode is transmitted to the user device. The changed positioning mode is to remove the positioning modes in the first sequence and take other positioning modes as the first selection priority. If the selectable positioning mode in the user device is a positioning mode in the first sequence, the last positioning mode in the first sequence is taken as the second selection priority selected by the user device based on the positioning mode.

[0027] A positioning data management system based on multi-sensor information fusion, including a vehicle positioning data acquisition module, a user positioning data response module, a monitoring period marking module, a target positioning event determination module, an objective characteristic factor analysis module and an early warning reminder module;

[0028] The vehicle positioning data acquisition module is used to acquire the vehicle positioning data transmitted by the Internet of Vehicles based on the indoor space when the user responds to the positioning demand;

[0029] The user positioning data response module is used to respond to the monitoring user positioning data after receiving the displayed vehicle positioning route at the user device end;

[0030] The monitoring period marking module is used to end the monitoring when the user positioning data and the vehicle positioning point are the same, and mark the corresponding period from the monitoring to the end of the monitoring as the monitoring period;

[0031] The target positioning event determination module is used to mark the positioning event that displays the difference between the user positioning route and the vehicle positioning route on the user device as a target positioning event;

[0032] The objective characteristic factor analysis module is used to determine the objective characteristic factors that affect route differences in the corresponding indoor space;

[0033] The early warning reminder module is used to provide early warning reminders to real-time user terminals based on objective characteristic factors when there are real-time users responding to vehicle positioning data.

[0034] Further, the objective characteristic factor analysis module includes a same type positioning route calibration unit, a user terminal positioning mode acquisition unit, a valid positioning event marking unit, a characteristic index analysis unit and an objective characteristic factor output unit;

[0035] The same type positioning route marking unit is used to mark the positioning routes with the same turning points, turning point angles and route distances between adjacent turning points as the same type of positioning routes, and classify the target positioning events into one type of positioning events according to the corresponding same type of positioning routes based on the markings;

[0036] The user terminal positioning mode acquisition unit is used to acquire a type of positioning event recording the type of user terminal positioning mode;

[0037] The valid positioning event marking unit is used to extract the positioning event corresponding to the same type of user terminal positioning method and the highest number of recorded events as a valid positioning event when the types are not unique;

[0038] The characteristic index analysis unit is used to analyze the characteristic index in a type of positioning event;

[0039] The objective characteristic factor output unit is used to analyze and output the objective characteristic factors that satisfy the judgment relationship.

[0040] Further, the characteristic index analysis unit includes a first characteristic index calculation unit and a second characteristic index calculation unit;

[0041] The first characteristic index calculation unit is used to calculate the first characteristic index of the initial deflection point of the corresponding valid positioning event record in a type of positioning event;

[0042] The second characteristic index calculation unit is used to calculate the second characteristic index of the port positioning event.

[0043] Further, the objective characteristic factor output unit includes a first objective characteristic factor output unit and a second objective characteristic factor output unit;

[0044] The first objective characteristic factor output unit is used to analyze the corresponding first objective characteristic factor based on the first characteristic index;

[0045] The second objective characteristic factor output unit is used to sort the user-end positioning methods corresponding to the port positioning event from large to small according to the value of the second characteristic index to generate a first sequence; and extract the first user-end positioning method in the first sequence as the second objective characteristic factor affecting the route difference in the indoor space.

[0046] Furthermore, the early warning and reminder module includes a real-time positioning data acquisition unit and a priority early warning unit;

[0047] The real-time positioning data acquisition unit is used to acquire the real-time vehicle positioning route when the real-time user responds to the vehicle positioning data;

[0048] The priority warning unit is used to transmit a reminder signal of the positioning data corresponding to the first objective characteristic factor to the user device when the real-time vehicle positioning route contains the first objective characteristic factor; and when analyzing the second objective characteristic factor, change the positioning method based on the first sequence to issue a priority warning.

[0049] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention acquires and analyzes positioning events implemented in each monitoring space, monitors positioning events in which user positioning data and vehicle positioning data produce deviations; analyzes the reasons for the deviations caused by such positioning events, and judges the first objective characteristic factors and the second objective characteristic factors that may have an impact based on the route characteristics of the monitoring space and the positioning data of the user device, thereby minimizing the time consumption in solving positioning problems based on positioning data for users who respond to positioning needs in real time in the space; and improves the efficiency and intelligence of using positioning data needs in different indoor scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] 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:

[0051] Figure 1 It is a structural schematic diagram of a positioning data management system based on multi-sensor information fusion of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0053] See also Figure 1The present invention provides a technical solution: a positioning data management method based on multi-sensor information fusion, comprising the following analysis steps:

[0054] Step S100: obtaining vehicle positioning data transmitted by the Internet of Vehicles based on the indoor space when the user responds to the positioning demand, the Internet of Vehicles communicates with the user device to transmit the vehicle positioning data to the user device; the vehicle positioning data includes the vehicle positioning route and the vehicle positioning point; the user device responds to monitor the user positioning data after receiving and displaying the vehicle positioning route;

[0055] Step S200: When the user positioning data and the vehicle positioning point are the same, the monitoring ends, the time period corresponding to the response monitoring to the end of the monitoring is marked as the monitoring period, and the user positioning route formed by the user positioning data in the monitoring period is extracted; the positioning event showing the difference between the user positioning route and the vehicle positioning route on the user device side is marked as the target positioning event;

[0056] The vehicle positioning route is given based on the path planning algorithm, such as the Dijkstra algorithm and Floyd algorithm in the classic algorithms; while the user positioning route is formed based on the movement trajectory formed by the device end recording the user's positioning data in real time within a certain period of time; such as the driving trajectory of the common device navigation end, the movement trajectory recorded by the device app, and so on.

[0057] Step S300: determining objective characteristic factors affecting route differences in the corresponding indoor space based on the target positioning event;

[0058] Step S400: When there is a real-time user response to the vehicle positioning data, an early warning reminder is given to the real-time user terminal based on objective characteristic factors.

[0059] Determining the objective characteristic factors that affect route differences in the corresponding indoor space includes the following analysis steps:

[0060] Step S310: Obtain turning points, route distances between adjacent turning points, and turning point angles of the vehicle positioning route recorded by the target positioning event; mark positioning routes with the same turning points, turning point angles, and route distances between adjacent turning points as the same type of positioning routes, and classify the target positioning events into a type of positioning event according to the corresponding positioning routes of the same type based on the markings;

[0061] Step S320: obtaining a type of positioning event recording the type of user-side positioning method, the user-side positioning methods include GPS positioning, base station positioning, WIFI positioning and AGPS positioning; when the type is not unique, extracting the same type of user-side positioning method and the corresponding positioning event with the highest number of recorded events as the valid positioning event; obtaining the valid positioning event recording the inflection point where the positioning route of each user differs from the positioning route of the vehicle, the inflection point refers to the starting point where the positioning data of the recorded user is separated from the positioning route of the vehicle;

[0062] Obtain the number N1 of types of initial deflection points recorded in valid positioning events. Deflection points at different positions represent one category, and the initial deflection point represents the starting point where separation first occurs within the monitoring period. Use the formula:

[0063] X1 = (N1 / M1) * [kmax / (kmax - kmin)],

[0064] Calculate the first characteristic index X1 corresponding to the initial deflection points recorded in valid positioning events for one type of positioning event. Here, M1 represents the number of event records of valid positioning events, k represents the number of times each type of initial deflection point is recorded, kmax represents the maximum value of the number of times the initial deflection point is recorded, and kmin represents the minimum value of the number of times the initial deflection point is recorded. Set the first characteristic index threshold X0, and traverse all valid positioning events in each type of positioning event. Mark the vehicle positioning route corresponding to the valid positioning event when X1 < X0 as a suspicious route. The smaller the characteristic index, the greater the impact of the deflection points existing in the corresponding vehicle positioning route on the user's behavior based on positioning data.

[0065] Step S330: Extract the number of times each initial deflection point in the suspicious route is recorded, and take the initial deflection point corresponding to the maximum recorded number k1max as the first objective characteristic factor. Determining the suspicious route can effectively indicate that there may be some spatial positions in the current monitoring space that are prone to causing deviations for users using positioning data due to internal space structure problems. If the user most likely to have a deviation is reminded at the user end, it can effectively reduce the situation of user positioning data deviation caused by objective spatial factors. If the first characteristic index of all valid positioning events in each type of positioning event satisfies X1 ≥ X0, it indicates that the structural characteristics in the current monitoring space are not an obvious objective reason for causing user positioning data deviation. Classify the positioning events of the same type according to different positioning methods at each user end as port positioning events. Determine the second objective characteristic factor affecting the route differences in the indoor space based on the port positioning events.

[0066] Determine the second objective characteristic factor affecting the route differences in the indoor space based on the port positioning events, including the following specific steps:

[0067] Step S331: Obtain the number m of times different deflection points are recorded in the i-th type of port positioning event in each type of positioning event i and analyze that recording different deflection points in the same type of port positioning event indicates that when the user positioning method has a poor effect, the positioning deviation or error may be diverse. Calculate the abnormality rate Y i of the i-th type of port positioning event, where Y i = m i / p, where p represents the total number of port positioning events recorded in the corresponding type of positioning event.

[0068] As shown in the examples:

[0069] The positioning direction of the port positioning event is GPS positioning, and there are 5 port positioning events;

[0070] The deflection points recorded respectively are: deflection point 1, deflection point 2, deflection point 1, deflection point 1, deflection point 2;

[0071] Then the number of times different deflection points are recorded in this type of port positioning event is m=2; p=5; the abnormality rate of the port positioning event achieved by GPS positioning is 2 / 5;

[0072] The larger the abnormal rate, the greater the difference in the impact of the positioning method corresponding to the port positioning event on the user route and the more difficult it is to determine the inflection point. Retrieve the number of positioning events with the same positioning method as the i-th type of port positioning event in all types of positioning events and calculate the abnormal rate under the corresponding type of positioning event; use the formula:

[0073] Z i =a1*(U i / V)+a2*[(1 / U i )∑Y i ],

[0074] Calculate the second characteristic index Z of the i-th type port positioning event i , U i represents the total number of types of the i-th type of port location event recorded in each location event, V represents the total number of types of location type events; a1 represents the first reference coefficient corresponding to the proportion of port location events, and a2 represents the second reference coefficient corresponding to the average abnormality rate;

[0075] Step S332: sorting the user terminal positioning modes corresponding to the port positioning events from large to small according to the values ​​of the second characteristic indexes to generate a first sequence;

[0076] Step S333: extracting the first user terminal positioning mode in the first sequence as the second objective characteristic factor affecting route differences in the indoor space.

[0077] Step S400 includes the following:

[0078] Obtaining a first objective characteristic factor and a second objective characteristic factor of each indoor space record;

[0079] When the real-time user responds to the vehicle positioning data, the real-time vehicle positioning route is obtained. When the real-time vehicle positioning route contains the first objective characteristic factor, a reminder signal of the positioning data corresponding to the first objective characteristic factor is transmitted to the user device end; so as to remind the user that it is easy to go wrong at the positioning data corresponding to the first objective characteristic factor, and determine the real route;

[0080] The real-time positioning mode of the user equipment end is obtained. When the real-time positioning mode is the same as the second objective characteristic factor, a signal for changing the positioning mode is transmitted to the user equipment end. The changed positioning mode is to remove the positioning modes in the first sequence and take other positioning modes as the first selection priority. If the selectable positioning mode in the user equipment end is the positioning mode in the first sequence, the last positioning mode in the first sequence is taken as the second selection priority selected by the user equipment end based on the positioning mode.

[0081] A positioning data management system based on multi-sensor information fusion, including a vehicle positioning data acquisition module, a user positioning data response module, a monitoring period marking module, a target positioning event determination module, an objective characteristic factor analysis module and an early warning reminder module;

[0082] The vehicle positioning data acquisition module is used to acquire the vehicle positioning data transmitted by the Internet of Vehicles based on the indoor space when the user responds to the positioning demand;

[0083] The user positioning data response module is used to respond to the monitoring user positioning data after receiving the displayed vehicle positioning route at the user device end;

[0084] The monitoring period marking module is used to end the monitoring when the user positioning data and the vehicle positioning point are the same, and mark the corresponding period from the monitoring to the end of the monitoring as the monitoring period;

[0085] The target positioning event determination module is used to mark the positioning event that displays the difference between the user positioning route and the vehicle positioning route on the user device as a target positioning event;

[0086] The objective characteristic factor analysis module is used to determine the objective characteristic factors that affect route differences in the corresponding indoor space;

[0087] The early warning reminder module is used to provide early warning reminders to real-time user terminals based on objective characteristic factors when there are real-time users responding to vehicle positioning data.

[0088] The objective characteristic factor analysis module includes a same type positioning route calibration unit, a user end positioning method acquisition unit, a valid positioning event marking unit, a characteristic index analysis unit and an objective characteristic factor output unit;

[0089] The same type positioning route marking unit is used to mark the positioning routes with the same turning points, turning point angles and route distances between adjacent turning points as the same type of positioning routes, and classify the target positioning events into one type of positioning events according to the corresponding same type of positioning routes based on the markings;

[0090] The user terminal positioning mode acquisition unit is used to acquire a type of positioning event recording the type of user terminal positioning mode;

[0091] The valid positioning event marking unit is used to extract the positioning event corresponding to the same type of user terminal positioning method and the highest number of recorded events as a valid positioning event when the types are not unique;

[0092] The characteristic index analysis unit is used to analyze the characteristic index in a type of positioning event;

[0093] The objective characteristic factor output unit is used to analyze and output the objective characteristic factors that satisfy the judgment relationship.

[0094] The characteristic index analysis unit includes a first characteristic index calculation unit and a second characteristic index calculation unit;

[0095] The first characteristic index calculation unit is used to calculate the first characteristic index of the initial deflection point of the corresponding valid positioning event record in a type of positioning event;

[0096] The second characteristic index calculation unit is used to calculate the second characteristic index of the port positioning event.

[0097] The objective characteristic factor output unit includes a first objective characteristic factor output unit and a second objective characteristic factor output unit;

[0098] The first objective characteristic factor output unit is used to analyze the corresponding first objective characteristic factor based on the first characteristic index;

[0099] The second objective characteristic factor output unit is used to sort the user-end positioning methods corresponding to the port positioning event from large to small according to the value of the second characteristic index to generate a first sequence; and extract the first user-end positioning method in the first sequence as the second objective characteristic factor affecting the route difference in the indoor space.

[0100] The early warning and reminder module includes a real-time positioning data acquisition unit and a priority early warning unit;

[0101] The real-time positioning data acquisition unit is used to acquire the real-time vehicle positioning route when the real-time user responds to the vehicle positioning data;

[0102] The priority warning unit is used to transmit a reminder signal of the positioning data corresponding to the first objective characteristic factor to the user device when the real-time vehicle positioning route contains the first objective characteristic factor; and when analyzing the second objective characteristic factor, change the positioning method based on the first sequence to issue a priority warning.

[0103] 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.

[0104] 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 positioning data management method based on multi-sensor information fusion, characterized in that: It includes the following analysis steps: Step S100: Obtain vehicle positioning data transmitted by the vehicle networking when the user responds to the positioning requirement based on the indoor space. The vehicle networking is communicatively connected to the user device end to transmit the vehicle positioning data to the user device end; The vehicle positioning data includes a vehicle positioning route and vehicle positioning points; after the user device end receives and displays the vehicle positioning route, it responds to monitoring the user positioning data; Step S200: When the user positioning data is the same as the vehicle positioning point, the monitoring ends. Mark the time period corresponding to the response monitoring to the end of the monitoring as the monitoring time period, and extract the user positioning route formed by the user positioning data within the monitoring time period; Mark the positioning event that shows the difference between the user positioning route and the vehicle positioning route on the user device end as the target positioning event; Step S300: Based on the target positioning event, determine the objective characteristic factors that affect the route difference in the corresponding indoor space; The determination of the objective characteristic factors that affect the route difference in the corresponding indoor space includes the following analysis steps: Step S310: Obtain the turning points of the vehicle positioning route recorded by the target positioning event, the route distances between adjacent turning points, and the turning angles; Mark the positioning routes with the same turning points, turning angles, and route distances between adjacent turning points as the same type of positioning routes, and divide the target positioning event into one type of positioning event based on the mark according to the corresponding same type of positioning routes; Step S320: Obtain the types of user-side positioning methods recorded by one type of positioning event; When the types are not unique, extract the positioning event corresponding to the same type of user-side positioning method and the highest recorded event times as the effective positioning event; Obtain the deflection points where there are differences between each user positioning route and the vehicle positioning route recorded by the effective positioning event. The deflection point refers to the starting point where the recorded user positioning data is separated from the vehicle positioning route; Obtain the number of types N1 of the initial deflection points recorded in the effective positioning event. Different positions of the deflection points represent one type. The initial deflection point represents the starting point where the first separation occurs within the monitoring time period; Use the formula: X1 = (N1 / M1) * [kmax / (kmax - kmin)], Calculate the first characteristic index X1 of the initial deflection points recorded in the corresponding effective positioning event of one type of positioning event, where M1 represents the number of event records of the effective positioning event, k represents the number of times each type of initial deflection point is recorded, kmax represents the maximum value of the number of times the initial deflection point is recorded, and kmin represents the minimum value of the number of times the initial deflection point is recorded; Set the first characteristic index threshold X0, traverse all effective positioning events in each type of positioning event, and mark the vehicle positioning route corresponding to the effective positioning event when X1 < X0 as the suspicious route; Step S330: Extract the number of times each initial deflection point in the suspicious route is recorded, and take the initial deflection point corresponding to the maximum recorded number k1max as the first objective characteristic factor; If the first characteristic index of all effective positioning events in each type of positioning event satisfies X1 ≥ X0, classify the same type of positioning event into port positioning events according to the different user-side positioning methods; Determine the second objective characteristic factor that affects the route difference in the indoor space based on the port positioning event; The method of determining the second objective characteristic factor affecting route differences in the indoor space based on the port positioning event includes the following specific steps: Step S331: Obtain the number of times m when different deflection points are recorded in the i-th type of port positioning event in each type of positioning event i , calculate the abnormal rate Y of the i-th type port positioning event i , Y i =m i / p, p represents the number of total port positioning events recorded in the corresponding type of positioning event; Retrieve the number of times that the location event with the same location method as the i-th type port location event is recorded in all types of location events and calculate the abnormal rate under the corresponding type of location event; use the formula: Z i =a1*(U i / V)+a2*[(1 / U i )∑Y i ], Calculate the second characteristic index Z of the i-th type port positioning event i , U i represents the total number of types of the i-th type of port location event recorded in each location event, V represents the total number of types of location type events; a1 represents the first reference coefficient corresponding to the proportion of port location events, and a2 represents the second reference coefficient corresponding to the average abnormality rate; Step S332: sorting the user terminal positioning modes corresponding to the port positioning events from large to small according to the values ​​of the second characteristic indexes to generate a first sequence; Step S333: extracting the first user terminal positioning mode in the first sequence as the second objective characteristic factor affecting route differences in the indoor space; Step S400: When there is a real-time user response to the vehicle positioning data, an early warning reminder is given to the real-time user terminal based on objective characteristic factors.

2. The positioning data management method based on multi-sensor information fusion according to claim 1, characterized in that: The step S400 includes the following: Obtaining a first objective characteristic factor and a second objective characteristic factor of each indoor space record; When the real-time user responds to the vehicle positioning data, the real-time vehicle positioning route is obtained, and when the real-time vehicle positioning route contains the first objective characteristic factor, a reminder signal of the positioning data corresponding to the first objective characteristic factor is transmitted to the user device end; The real-time positioning mode of the user equipment end is obtained. When the real-time positioning mode is the same as the second objective characteristic factor, a signal for changing the positioning mode is transmitted to the user equipment end. The changing positioning mode is to remove the positioning modes in the first sequence as the first selection priority. If the selectable positioning mode in the user equipment end is the positioning mode in the first sequence, the last positioning mode in the first sequence is used as the second selection priority of the user equipment end based on the positioning mode selection.

3. A positioning data management system based on multi-sensor information fusion, such as a positioning data management method based on multi-sensor information fusion as described in any one of claims 1-2, characterized in that: It includes a vehicle positioning data acquisition module, a user positioning data response module, a monitoring period marking module, a target positioning event determination module, an objective characteristic factor analysis module and an early warning reminder module; The vehicle positioning data acquisition module is used to acquire vehicle positioning data transmitted by the Internet of Vehicles based on the indoor space when the user responds to the positioning demand; The user positioning data response module is used to respond to monitoring user positioning data after receiving and displaying the vehicle positioning route at the user equipment end; The monitoring period marking module is used to end the monitoring when the user positioning data and the vehicle positioning point are the same, and mark the period corresponding to the monitoring response to the end of the monitoring as the monitoring period; The target positioning event determination module is used to mark the positioning event that displays the difference between the user positioning route and the vehicle positioning route on the user device as a target positioning event; The objective characteristic factor analysis module is used to determine the objective characteristic factors that affect route differences in the corresponding indoor space; The early warning reminder module is used to provide early warning reminders to the real-time user terminal based on objective characteristic factors when there is a real-time user responding to the vehicle positioning data.

4. A positioning data management system based on multi-sensor information fusion according to claim 3, characterized in that: The objective characteristic factor analysis module includes a same type positioning route calibration unit, a user terminal positioning mode acquisition unit, a valid positioning event marking unit, a characteristic index analysis unit and an objective characteristic factor output unit; The same type positioning route marking unit is used to mark positioning routes with the same turning points, turning point angles, and route distances between adjacent turning points as the same type of positioning routes, and classify target positioning events into a type of positioning events according to the corresponding same type of positioning routes based on the markings; The user terminal positioning mode acquisition unit is used to acquire a type of positioning event recording the type of user terminal positioning mode; The valid positioning event marking unit is used to extract the positioning event corresponding to the same type of user terminal positioning method and the highest number of recorded events as the valid positioning event when the type is not unique; The characteristic index analysis unit is used to analyze the characteristic index in a type of positioning event; The objective characteristic factor output unit is used to analyze and output the objective characteristic factors that satisfy the judgment relationship.

5. A positioning data management system based on multi-sensor information fusion according to claim 4, characterized in that: The characteristic index analysis unit includes a first characteristic index calculation unit and a second characteristic index calculation unit; The first characteristic index calculation unit is used to calculate a first characteristic index of an initial deflection point corresponding to a valid positioning event record in a type of positioning event; The second characteristic index calculation unit is used to calculate a second characteristic index of the port positioning event.

6. A positioning data management system based on multi-sensor information fusion according to claim 5, characterized in that: The objective characteristic factor output unit includes a first objective characteristic factor output unit and a second objective characteristic factor output unit; The first objective characteristic factor output unit is used to analyze the corresponding first objective characteristic factor based on the first characteristic index; The second objective characteristic factor output unit is used to sort the user-end positioning methods corresponding to the port positioning event from large to small according to the value of the second characteristic index to generate a first sequence; and extract the first user-end positioning method in the first sequence as the second objective characteristic factor affecting route differences in the indoor space.

7. A positioning data management system based on multi-sensor information fusion according to claim 6, characterized in that: The early warning reminder module includes a real-time positioning data acquisition unit and a priority early warning unit; The real-time positioning data acquisition unit is used to acquire the real-time vehicle positioning route when the real-time user responds to the vehicle positioning data; The priority warning unit is used to transmit a reminder signal of positioning data corresponding to the first objective characteristic factor to the user device when the real-time vehicle positioning route contains the first objective characteristic factor; and when analyzing the second objective characteristic factor, change the positioning method based on the first sequence to issue a priority warning.

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