Method and system for identifying and correcting credibility of reported data of mining positioning system equipment

Abstract

The invention provides a credibility identification and correction method and system for data reported by mining positioning system equipment, and relates to the field of mine navigation and positioning. The method comprises the following steps: acquiring a plurality of historical position data sequentially reported by equipment for a plurality of monitoring objects according to a time sequence, constructing a historical track database, and establishing and training a target position direction prediction model based on machine learning; acquiring real-time trajectory data reported by the equipment to the monitoring target, and judging whether the position direction in the data is opposite to the predicted trajectory direction or not; when the directions are opposite, judging whether the data is credible or not according to the nearest effective position of the monitored target, and further adding a credible or uncredible identifier; when the directions are the same, judging as credible data; determining the credibility level of the equipment; and determining a data cleaning rule corresponding to the data for cleaning according to the credibility level and the identifier of the data based on the personnel limit scene, and correcting the track of the monitoring target. According to the invention, by improving the accuracy of the reported data, an error overcrowding event is avoided.

Description

Technical Field

[0001] This invention relates to the field of mine navigation and positioning technology, specifically to a method and system for identifying and correcting the reliability of data reported by mine positioning system equipment. Background Technology

[0002] Coal mines categorize their areas based on a comprehensive consideration of geological structure, mining technology, safety management needs, and legal regulations. These areas may be classified as general areas, key areas, pressing areas, and areas exceeding permitted depths or boundaries. Different areas have different requirements regarding the number of people allowed in. For example, key areas may have hidden dangers such as gas accumulation, excessive coal dust, and unstable roofs; dense crowds in these areas could exacerbate chain reactions of disasters. Furthermore, the national law, the "Coal Mine Safety Regulations," explicitly stipulates that the number of people working simultaneously in a tunneling face should not exceed nine. Therefore, mines set limits on the number of people allowed in certain areas and monitor the number of people within each area in real time. When the number of people in an area exceeds the set limit, an alarm is triggered, displaying the current number of people in the area on an LED screen and providing a voice reminder that the area is overcrowded and further entry should be avoided.

[0003] Positioning substations are core equipment for obtaining personnel location information in coal mines. They can acquire the real-time location of miners underground. Discrepancies between the location data reported by the substation and the actual location can easily lead to overcrowding in the area. These discrepancies mainly manifest in two ways: 1) the reported trajectory direction is unknown and cannot be mapped to specific coordinates; 2) the reported trajectory direction is reversed, resulting in incorrect coordinate data. Typically, the following methods are used to mitigate these scenarios: 1) increasing the frequency of regular manual inspections to check equipment status and immediately replacing faulty substations; 2) using sensors with higher sensitivity and stronger anti-interference capabilities for the positioning substation to reduce directional misjudgments, improve accuracy, and lower the false alarm rate. However, increasing the frequency of regular manual inspections increases manpower and material costs, while using high-sensitivity sensors increases hardware costs. Furthermore, directional misjudgments can still occur in areas with strong electromagnetic interference, such as from frequency converters or welding machines, or in environments with metal shielding effects, such as densely supported structures.

[0004] Therefore, if the reliability of the location data reported by the positioning substation can be directly determined, the investment of human and material resources will be greatly reduced. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for identifying and correcting the credibility of data reported by mining positioning system equipment. First, a target position direction prediction model is trained based on historical trajectory data. After judging whether the data with reversed direction is credible, a credibility label is applied. Then, based on the credibility label and the credibility level of the equipment, combined with the personnel restriction scenario of the target area, data cleaning is performed. This ensures the correctness of the overall data processing of the mine and avoids the occurrence of erroneous overstaffing events.

[0006] To achieve the above objectives, the present invention proposes the following technical solution: Firstly, a method for identifying and correcting the reliability of data reported by mining positioning system equipment is proposed, including: Acquire several historical location data and the corresponding historical real locations of multiple monitored objects reported by the device in a time sequence, and construct a historical trajectory database. Based on the historical trajectory database, a target position and orientation prediction model based on machine learning is established and trained. The system acquires real-time trajectory data reported by the device to the monitored target, compares the real-time trajectory data with the predicted trajectory direction of the monitored target predicted by the target position and direction prediction model, and determines whether the position and direction of the monitored target in the real-time trajectory data are opposite to the predicted trajectory direction. When the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the reliability of the real-time trajectory data is determined based on the most recent valid position of the monitored target in the historical trajectory database, and a reliable or unreliable label is attached to the real-time trajectory data; otherwise, when the position direction of the monitored target in the real-time trajectory data is the same as the predicted trajectory direction, the real-time trajectory data is directly determined to be reliable data. The reliability level of the device is determined based on the historical trajectory database. Based on the personnel restriction scenario for accessing the target area, the data cleaning rules corresponding to the real-time trajectory data are determined according to the trusted or untrusted identifier of the real-time trajectory data and the trust level of the device; wherein, the target area is the personnel restriction area. Data is cleaned according to the aforementioned data cleaning rules to correct the trajectory of the monitored target.

[0007] Furthermore, when the location direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the reliability of the real-time trajectory data is determined based on the most recent valid location of the monitored target in the historical trajectory database. The process of then attaching a reliable or unreliable identifier to the real-time trajectory data is as follows: When the device first reads the positioning card corresponding to the monitored target, and the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the distance between the real-time trajectory data and the current nearest effective position of the monitored target is calculated for the two positions with opposite directions. Compare the two calculated distances and determine the direction corresponding to the position with the smaller distance to the nearest valid position as the reasonable reporting direction; Determine whether the location and direction of the monitored target in the real-time trajectory data are consistent with the reasonable reporting direction. If the location and direction are consistent with the reasonable reporting direction, the real-time trajectory data is determined to be reliable data; otherwise, the real-time trajectory data is unreliable data. A trustworthy identifier is added to real-time trajectory data that is determined to be trustworthy data, and an untrustworthy identifier is added to real-time trajectory data that is determined to be untrustworthy data.

[0008] Furthermore, when the location direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the reliability of the real-time trajectory data is determined based on the most recent valid location of the monitored target in the historical trajectory database. The process of then attaching a reliable or unreliable identifier to the real-time trajectory data is as follows: If the position direction of the monitored target in the real-time trajectory data of the same monitored target reported by the device is opposite to the predicted trajectory direction, then it is verified whether the device has reported the entry data of the positioning card within a preset time range from the most recent effective position of the monitored target. If the device reports entry data of the positioning card within a preset time range, the real-time trajectory data is determined to be unreliable data; otherwise, the real-time trajectory data is reliable data. A trustworthy identifier is added to real-time trajectory data that is determined to be trustworthy data, and an untrustworthy identifier is added to real-time trajectory data that is determined to be untrustworthy data.

[0009] Furthermore, the process of determining the reliability level of the device based on the historical trajectory database is as follows: The number of unreliable data in the historical trajectory database within a preset statistical period is counted, and the credibility level of the device is determined based on the number of unreliable data.

[0010] Furthermore, based on the limited access scenario for the target area, the process of determining the data cleaning rules corresponding to the real-time trajectory data according to the trusted or untrusted identifiers of the real-time trajectory data and the trust level of the device is as follows: The maximum number of people allowed in the target area is determined based on the type of the target area. When the monitored target is in a non-restricted area and its real-time trajectory data at the current moment is marked with an untrusted identifier, the data is retained for data reference. When the monitored target is within the target area, and its real-time trajectory data at the current moment is marked with an untrusted identifier, the data is filtered out, and the status of the monitored target within the target area is recorded as untrusted. When the monitored target is in a non-restricted area and its real-time trajectory data at the current moment is accompanied by a trusted identifier, the data is retained for data reference. When the monitored target is within the target area and its real-time trajectory data at the current moment is accompanied by a trusted identifier, it is determined whether the monitored target's entry into the target area will exceed the limit of personnel, thereby determining the data cleaning rules corresponding to the real-time trajectory data.

[0011] Furthermore, the process of determining whether the monitored target's entry into the target area will exceed the personnel limit, thereby determining the data cleaning rules corresponding to the real-time trajectory data, is as follows: If the monitored target is within the target area, its real-time trajectory data at the current moment is appended with a trusted identifier, and the monitored target's entry into the target area will not exceed the limit of personnel, then the data is retained. When the monitored target is within the target area, its current real-time trajectory data is appended with a trusted identifier, and the monitored target entering the target area would exceed the personnel limit, then: Determine whether the real-time trajectory data of the monitored target at the previous moment has an untrusted identifier attached. If the real-time trajectory data has an untrusted identifier attached, filter or retain the data according to the data cleaning rules corresponding to the trust level of the device; otherwise, directly enable the over-capacity alarm. Among them, when data is retained according to the data cleaning rules corresponding to the trust level of the device, the over-capacity alarm is enabled.

[0012] Furthermore, in the scenario of limited access to the target area, the process of determining the data cleaning rules corresponding to the real-time trajectory data based on the trusted or untrusted identifiers of the real-time trajectory data and the trust level of the device also includes: If the monitored target is located within the overlapping area of ​​the target area and the adjacent restricted area, and the real-time trajectory data corresponding to the current moment is appended with a trusted identifier, then: If the monitored target enters the target area without exceeding the limit number of people, the data is retained; if the monitored target enters the target area and exceeds the limit number of people, the real-time trajectory data of the monitored target in the target area at the previous moment is checked to see if it has an untrusted identifier attached. If the real-time trajectory data has an untrusted identifier attached, the data is filtered or retained according to the data cleaning rules corresponding to the trust level of the device; otherwise, the over-capacity alarm is directly activated. The system verifies whether the real-time trajectory data of the monitored target at the previous moment before entering the adjacent restricted area has an untrusted identifier. If the real-time trajectory data has an untrusted identifier, the data is filtered or retained according to the data cleaning rules corresponding to the trust level of the device; otherwise, the data is directly retained. If directly retaining the data or retaining the data according to the data cleaning rules corresponding to the trust level of the device would cause the monitored target to exceed the limit of personnel when entering the adjacent restricted area, an over-capacity alarm is activated.

[0013] Secondly, a reliability identification and correction system for data reported by mining positioning system equipment is proposed, including: The acquisition module is used to acquire several historical location data reported by the device to multiple monitored objects in a time sequence and the historical real location corresponding to each historical location data, and to build a historical trajectory database. The model building module is used to build and train a target position and direction prediction model based on machine learning, according to the historical trajectory database. The comparison and judgment module is used to acquire real-time trajectory data reported by the device to the monitored target, compare the real-time trajectory data with the predicted trajectory direction of the monitored target predicted by the target position and direction prediction model, and determine whether the position and direction of the monitored target in the real-time trajectory data are opposite to the predicted trajectory direction. The identification and attachment module is used to determine whether the real-time trajectory data is reliable based on the most recent valid position of the monitored target in the historical trajectory database when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, and then attach a reliable or unreliable identifier to the real-time trajectory data; otherwise, when the position direction of the monitored target in the real-time trajectory data is the same as the predicted trajectory direction, the real-time trajectory data is directly determined to be reliable data. The rating determination module is used to determine the credibility rating of the device based on the historical trajectory database. The rule determination module is used to determine the data cleaning rules corresponding to the real-time trajectory data based on the personnel restriction scenario of accessing the target area, according to the trusted or untrusted identifier of the real-time trajectory data and the trust level of the device; wherein, the target area is a personnel restriction area; The trajectory correction module is used to clean the data according to the data cleaning rules and correct the trajectory of the monitored target.

[0014] Furthermore, the execution unit in the identifier appending module that determines the reliability of the real-time trajectory data based on the most recent valid location of the monitored target in the historical trajectory database when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, and then appends a reliable or unreliable identifier to the real-time trajectory data, includes: The calculation unit is used to calculate the distance between the real-time trajectory data and the current nearest effective position of the monitoring target when the device first reads the positioning card corresponding to the monitoring target, and the position direction of the monitoring target in the real-time trajectory data is opposite to the predicted trajectory direction. The comparison unit is used to compare two calculated distances and determine the direction corresponding to the position with the smaller distance to the nearest valid position as the reasonable reporting direction; The judgment unit is used to determine whether the position direction of the monitored target in the real-time trajectory data is consistent with the reasonable reporting direction. If the position direction is consistent with the reasonable reporting direction, the real-time trajectory data is determined to be reliable data; otherwise, the real-time trajectory data is unreliable data. The identification attachment unit is used to attach a trustworthy identifier to real-time trajectory data that is determined to be trustworthy data, and to attach an untrustworthy identifier to real-time trajectory data that is determined to be untrustworthy data.

[0015] Thirdly, an electronic device is proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the aforementioned method for reliability identification and correction of data reported by mining positioning system equipment.

[0016] As can be seen from the above technical solutions, the technical solutions of the present invention have achieved the following beneficial effects: This invention discloses a method and system for identifying and correcting the credibility of data reported by mining positioning system equipment. The method includes: acquiring several historical location data reported sequentially by the equipment to multiple monitored objects in a time sequence, constructing a historical trajectory database, and establishing and training a target position and direction prediction model based on machine learning; acquiring real-time trajectory data reported by the equipment to the monitored targets, and determining whether the position and direction in the data are opposite to the predicted trajectory direction; when the directions are opposite, determining the credibility of the data based on the most recent valid location of the monitored target, and then attaching a credibility or untrustworthiness label; when the directions are the same, determining the data as credible; determining the credibility level of the equipment; and, based on a limited personnel scenario, determining the corresponding data cleaning rules according to the credibility level and the data label, cleaning the data, and correcting the trajectory of the monitored targets. This invention trains a target position and direction prediction model based on historical trajectory data, and by judging the credibility of the data, it does not completely rely on hardware-reported target direction data, greatly improving data accuracy. Furthermore, the implementation of this invention effectively reduces the frequency of regular manual inspections, reducing equipment and labor costs.

[0017] Compared to existing technologies, this invention sets different data reliability judgment standards for the identification order of the positioning cards of the monitored targets, which is more in line with the actual movement pattern of the monitored targets, thereby improving the accuracy of the above data. Secondly, for the data after reliability judgment, this invention retains or filters the real-time trajectory data of the reported monitored targets for different personnel restriction scenarios, and determines whether to trigger over-personnel warnings, thereby improving the accuracy of tracking the location of monitored targets in mines, while reducing misjudgments, avoiding the occurrence of erroneous over-personnel events, and reducing safety risks in mines.

[0018] It should be understood that all combinations of the foregoing concepts and the additional concepts described in more detail below can be considered part of the inventive subject matter of this disclosure, provided that such concepts do not contradict each other.

[0019] The foregoing and other aspects, embodiments, and features of the teachings of the present invention will be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the invention, such as features and / or beneficial effects of exemplary embodiments, will become apparent from the following description or may be learned through practice of specific embodiments according to the teachings of the present invention. Attached Figure Description

[0020] The accompanying drawings are not drawn to scale. In the drawings, each identical or nearly identical component shown in the various figures may be denoted by the same reference numeral. For clarity, not every component is labeled in each figure. Embodiments of various aspects of the invention will now be described by way of example and with reference to the accompanying drawings, wherein: Figure 1This is a flowchart of the reliability identification and correction method for data reported by the mining positioning system equipment of the present invention; Figure 2 This is the first step in the method of determining the reliability of real-time trajectory data based on the nearest valid location according to the present invention. Figure 3 This is the second step in the method of determining the reliability of real-time trajectory data based on the nearest valid location according to the present invention. Figure 4 This is a flowchart illustrating the data cleaning rules for determining whether a monitored target will exceed the personnel limit, as per the present invention. Figure 5 This is a block diagram of the reliability identification and correction system for data reported by the mining positioning system equipment of the present invention. Figure 6 This is an example diagram of an electronic device disclosed in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art.

[0022] The terms "first," "second," and similar words used in the specification and claims of this patent application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, unless the context clearly indicates otherwise, the singular forms of "an," "a," or "the," etc., do not indicate a quantity limitation, but rather indicate the presence of at least one. Terms such as "comprising" or "including" mean that the element or object preceding "comprising" encompasses the features, integrals, steps, operations, elements, and / or components listed following "comprising" or "including," and do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0023] When the real-time location reported by the positioning substation does not match the actual location of personnel underground, it can easily lead to overcrowding incidents in the area. Therefore, existing technologies mainly improve the accuracy of reported data by replacing the positioning substation equipment with more accurate equipment and increasing the frequency of regular manual inspections. However, the existing solutions increase the cost of using positioning substations and have limited improvement in accuracy. Therefore, this invention aims to propose a method and system for identifying and correcting the reliability of data reported by mining positioning system equipment. This method can directly determine the reliability of the location data reported by the positioning substation, thereby ensuring the safety of personnel underground while significantly reducing the manpower and material costs.

[0024] Combination Figure 1 As shown, the reliability identification and correction method for data reported by mining positioning system equipment disclosed in this invention includes the following steps: Step S102: Obtain several historical location data and the historical real location corresponding to each historical location data from the equipment to report a number of monitored objects in a time sequence, and construct a historical trajectory database; wherein, the equipment is an information collection and dissemination organization such as a positioning substation, positioning card or positioning base station set up by the mining positioning system that can report data.

[0025] Step S104: Based on the historical trajectory database, establish and train a target position and direction prediction model based on machine learning; Step S106: Obtain the real-time trajectory data reported by the device for the monitored target, compare the real-time trajectory data with the predicted trajectory direction of the monitored target predicted by the target position and direction prediction model, and determine whether the position and direction of the monitored target in the real-time trajectory data are opposite to the predicted trajectory direction. Step S108: When the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the reliability of the real-time trajectory data is determined based on the most recent valid position of the monitored target in the historical trajectory database, and a reliable or unreliable identifier is attached to the real-time trajectory data; otherwise, when the position direction of the monitored target in the real-time trajectory data is the same as the predicted trajectory direction, the real-time trajectory data is directly determined to be reliable data. Step S110: Determine the reliability level of the device based on the historical trajectory database; Step S112: Based on the personnel restriction scenario for accessing the target area, determine the data cleaning rules corresponding to the real-time trajectory data according to the trusted or untrusted identifier of the real-time trajectory data and the trust level of the device; wherein, the target area is a personnel restriction area. Step S114: Perform data cleaning according to the data cleaning rules to correct the trajectory of the monitored target.

[0026] The reliability identification and correction method for reported data from the mining positioning system disclosed in this invention can be generally divided into four structural components. The first component involves establishing and training a target position and direction prediction model based on a historical trajectory database to determine real-time trajectory data with reversed direction. The second component involves calculating the reliability of the current real-time trajectory data based on the most recent valid location and assigning a reliability label to the data, which includes both reliable and unreliable labels. The third component involves performing statistical analysis based on the historical trajectory database established by the equipment to determine the equipment's reliability level. The fourth component involves determining the data cleaning rules corresponding to the real-time trajectory data based on the reliability label of the real-time trajectory data and the equipment's reliability level, combined with the personnel restriction scenario of the target area, and performing relevant data filtering and cleaning. Through these four structural components, the overall data processing accuracy of the optimized system is fully realized, preventing erroneous overcrowding events and reducing safety hazards in the mine.

[0027] Combination Figure 2 As shown, in step S108 above, when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the process of determining whether the real-time trajectory data is reliable based on the most recent valid position of the monitored target in the historical trajectory database, and then attaching a reliable or unreliable identifier to the real-time trajectory data, may include the following steps: Step S202: When the device first reads the positioning card corresponding to the monitored target, and the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the distance between the real-time trajectory data and the current nearest effective position of the monitored target is calculated for each of the two positions with opposite directions. Step S204: The two calculated distances are compared, and the direction corresponding to the position with the smaller distance to the nearest effective position is determined to be the reasonable reporting direction. Step S206: It is determined whether the position direction of the monitored target in the real-time trajectory data is consistent with the reasonable reporting direction. If the position direction is consistent with the reasonable reporting direction, the real-time trajectory data is determined to be reliable data; otherwise, the real-time trajectory data is unreliable data. Step S208: A reliable identifier is added to the real-time trajectory data determined to be reliable data, and an unreliable identifier is added to the real-time trajectory data determined to be unreliable data.

[0028] For example, consider two adjacent devices, A and B, located in the same tunnel. Device A is installed to the left of device B. Device A is installed at 2400 meters in the tunnel, and device B is installed at 2500 meters in the tunnel. When the monitored target moves from device A to device B, the predicted trajectory direction of the monitored target, as predicted by the target position direction prediction model, is from the right side of device A to the left side of device B. However, the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction. That is, the reported position is to the right of device B, and the two directions are opposite. The nearest valid location of the monitored target is known to be 50 meters to the right of device A, corresponding to a location at 2450 meters in the tunnel. The real-time trajectory data is 50 meters to the right of device B, corresponding to a location at 2550 meters in the tunnel, so its distance from the nearest valid location is 100 meters. The location of the real-time trajectory data after reversing its direction is 50 meters to the left of device B, corresponding to a location at 2450 meters in the tunnel, so its distance from the nearest valid location is 0. Therefore, the direction corresponding to the minimum distance of 0 is the reasonable reporting direction, i.e., 50 meters to the left of device B. This is inconsistent with the real-time trajectory data, so the real-time trajectory data is determined to be unreliable data and an unreliable label is added.

[0029] The above Figure 2 The illustrated method for determining the reliability of real-time trajectory data represents one scenario in data reliability identification: the device identifies the corresponding locator card for the first time during the target location and monitoring process, and the locator card remains online thereafter. Data reliability identification also includes another scenario: the locator card is initially identified for a period of time during the target location and monitoring process, but the signal is lost and then captured by the device a second time; this scenario is as follows... Figure 3 As shown, the implementation process of adding a credibility identifier after determining whether the real-time trajectory data is reliable based on the most recent valid location of the monitored target in the historical trajectory database in step S108 can include the following steps: Step S302, when the position direction of the monitored target in the real-time trajectory data of the same monitored target reported by the device is opposite to the predicted trajectory direction, then verify whether the device has reported the entry data of the positioning card within a preset time range from the most recent valid location of the monitored target; Step S304, when the device has reported the entry data of the positioning card within the preset time range, then determine that the real-time trajectory data is unreliable data; otherwise, the real-time trajectory data is reliable data; Step S306, add a credibility identifier to the real-time trajectory data determined to be reliable data, and add an unreliable identifier to the real-time trajectory data determined to be unreliable data.

[0030] For example, device A monitors the same target and detects that its reported real-time trajectory data is opposite to the predicted trajectory direction. The data reliability judgment process is as follows: it verifies whether device A has reported the entry data of the corresponding positioning card of the target within 4 minutes from the most recent effective position of the target under device A's monitoring. The result shows that device A reported the entry data of the positioning card at 3 minutes and 12 seconds from the most recent effective position. This indicates that the positioning card of the target was identified and captured by device A for the second time, and the target experienced signal loss within the coverage area of ​​device A. Therefore, the real-time trajectory data within these 4 minutes is judged as unreliable and an unreliable label is added. Data outside 4 minutes is judged as reliable. In this example, the preset time range is set to 4 minutes. In other examples, it can be set to other values ​​according to the data reporting frequency of the device.

[0031] In step S110 of the present invention, the credibility level of the device is classified according to the historical trajectory database in the following manner: based on the fact that each trajectory data reported by the device is attached with a credibility identifier, the number of untrusted data in the historical trajectory database within a preset statistical period is counted, and the credibility level of the device is determined according to the number of untrusted data.

[0032] In this embodiment, the preset statistical period is 7 days. That is, by counting the number of unreliable data in the historical trajectory database from the current time to the previous 7 days, the credibility level is divided according to the number of unreliable data, as shown in Table 1 below; the number of unreliable data corresponding to each credibility level can be configured as needed. For example, device A reported 110,000 unreliable data entries in the past 7 days and is classified as Level 1; device B reported 60,000 unreliable data entries in the past 7 days and is classified as Level 2; and device C reported 10,000 unreliable data entries in the past 7 days and is classified as Level 3.

[0033] Table 1 Equipment Reliability Level Classification Credibility level Number of unreliable data Level 1 More than 100,000 Level 2 More than 100,000, less than 50,000 Level 3 Less than 50,000 After completing the reliability assessment of real-time trajectory data and the reliability level assessment of the equipment, data filtering or retention can begin for specific personnel restriction scenarios. Compared with existing technologies, this application analyzes different personnel restriction scenarios specifically, rather than directly determining data retention or filtering based solely on data reliability indicators, resulting in higher identification accuracy. Secondly, this application combines the equipment's reliability level analysis to determine whether the monitored target can be retained in a specific scenario, further improving the accuracy of retained data and avoiding false overcrowding events while identifying genuine overcrowding events in personnel restriction scenarios.

[0034] Based on a limited-access scenario for the target area, this application determines the data cleaning rules corresponding to the real-time trajectory data according to the trusted or untrusted identifiers of the real-time trajectory data and the trust level of the device. The specific process is as follows: The maximum number of people allowed in the target area is determined based on the type of the target area. When the monitored target is in a non-restricted area and its current real-time trajectory data is marked with an untrusted identifier, the data is retained for data reference. In this embodiment, the location system uses the trajectory data reported by all devices before the trust level of the devices is determined. Data cleaning only begins after the trust level of the devices is determined.

[0035] When the monitored target is within the target area, and its real-time trajectory data at the current moment is marked with an untrusted identifier, the data is filtered out, and the status of the monitored target within the target area is recorded as untrusted. When the monitored target is in a non-restricted area and its real-time trajectory data at the current moment is accompanied by a trusted identifier, the data is retained for data reference. When the monitored target is within the target area and its real-time trajectory data at the current moment is accompanied by a trusted identifier, it is determined whether the monitored target's entry into the target area will exceed the limit of personnel, thereby determining the data cleaning rules corresponding to the real-time trajectory data.

[0036] Combination Figure 4 As shown, the process of determining whether the monitored target's entry into the target area will exceed the personnel limit, thereby determining the data cleaning rules corresponding to the real-time trajectory data, includes the following steps: Step S402, when the monitored target is in the target area, its current real-time trajectory data is accompanied by a trusted identifier and the monitored target's entry into the target area will not exceed the personnel limit, then the data is retained; Step S404, when the monitored target is in the target area, its current real-time trajectory data is accompanied by a trusted identifier and the monitored target's entry into the target area will exceed the personnel limit, then: determine whether the real-time trajectory data corresponding to the monitored target at the previous moment is accompanied by an untrusted identifier. If the real-time trajectory data is accompanied by an untrusted identifier, then data filtering or retention is performed according to the data cleaning rules corresponding to the device's trust level; otherwise, the over-capacity alarm is directly activated; wherein, when data retention is performed according to the data cleaning rules corresponding to the device's trust level, the over-capacity alarm is activated.

[0037] In this embodiment, the specific data cleaning rules for filtering or retaining data according to the data cleaning rules corresponding to the trust level of the device are shown in Table 2 below; wherein, the specific data filtering items in the data cleaning rules can be configured according to the data reporting frequency, and Table 2 only shows the data configured in this embodiment.

[0038] Table 2 Data Cleaning Rules Corresponding to Equipment Trustworthiness Levels

[0039] Credibility level Number of unreliable data Data cleaning rules - data filtering entries Level 1 More than 100,000 30 consecutive trusted identification data Level 2 More than 100,000, less than 50,000 20 consecutive trusted identification data Level 3 Less than 50,000 10 consecutive trusted identification data For example, device A reported 110,000 untrusted data entries in the past 7 days, with a trust level of 1. If device A reports real-time trajectory data for monitored target 'a' within the restricted area and includes an untrusted flag, then if subsequent reports from device A remain trustworthy, the first 30 entries will be filtered, and subsequent real-time trajectory data will remain trustworthy and participate in the over-capacity alarm calculation. Otherwise, if fewer than 30 consecutive trustworthy data entries are reported (e.g., the 28th entry becomes untrustworthy), these trustworthy trajectories will be filtered out, and the data will be recalculated from the 28th entry onwards until the over-capacity warning is met. The number of consecutively filtered trustworthy data entries at different trust levels gradually decreases as the trust level increases.

[0040] Based on the personnel restriction scenario for accessing the target area, and according to the trusted or untrusted identifiers of the real-time trajectory data, as well as the trust level of the device, the application scenario of the data cleaning rules corresponding to the real-time trajectory data is determined. Besides the specific data filtering rules required when the monitored target is deemed to be overcrowded in the personnel restriction scenario, there is another scenario with data cleaning rules, the specific content of which includes: If the monitored target is located within the overlapping area of ​​the target area and the adjacent restricted area, and the real-time trajectory data corresponding to the current moment is accompanied by a trusted identifier, then: if the monitored target's entry into the target area will not exceed the number of people allowed, the data is retained; if the monitored target's entry into the target area would exceed the number of people allowed, the real-time trajectory data corresponding to the monitored target in the target area at the previous moment is checked for the presence of an untrusted identifier. If the real-time trajectory data is accompanied by an untrusted identifier, the data is filtered or retained according to the data cleaning rules corresponding to the device's trust level; otherwise, the over-capacity alarm is directly activated. The system verifies whether the real-time trajectory data of the monitored target at the previous moment before entering the adjacent restricted area has an untrusted identifier. If the real-time trajectory data has an untrusted identifier, the data is filtered or retained according to the data cleaning rules corresponding to the trust level of the device; otherwise, the data is directly retained. If directly retaining the data or retaining the data according to the data cleaning rules corresponding to the trust level of the device would cause the monitored target to exceed the limit of personnel when entering the adjacent restricted area, an over-capacity alarm is activated.

[0041] When the real-time trajectory data of the monitored target enters the overlapping area of ​​the target area and the adjacent restricted area, the data processing of the monitored target in the target area and the data processing of the adjacent restricted area are carried out simultaneously, and the corresponding data cleaning rules are adopted respectively.

[0042] In addition, as an optional implementation, the motion trajectory of the monitored target is composed of several trajectory data with added trusted identifiers in a time series, which has higher accuracy than the prior art.

[0043] Based on the same inventive concept as the above-described method embodiments, this application also provides a reliability identification and correction system for data reported by mining positioning system equipment, such as... Figure 5 The diagram shows a framework for a reliability identification and correction system for data reported by mining positioning system equipment. Figure 5It is understood that the credibility identification and correction system includes the following modules: an acquisition and construction module, used to acquire several historical location data reported by the device to multiple monitored objects in a time sequence and the historical real location corresponding to each historical location data, and construct a historical trajectory database; a model building module, used to build and train a target position direction prediction model based on machine learning according to the historical trajectory database; a comparison and judgment module, used to acquire real-time trajectory data reported by the device to the monitored target, compare the real-time trajectory data with the predicted trajectory direction of the monitored target predicted by the target position direction prediction model, and determine whether the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction; and an identification and attachment module, used to determine the real-time trajectory direction when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, based on the most recent valid position of the monitored target in the historical trajectory database. The system determines whether the real-time trajectory data is reliable, and then attaches a reliable or unreliable identifier to the real-time trajectory data; otherwise, if the position direction of the monitored target in the real-time trajectory data is the same as the predicted trajectory direction, the real-time trajectory data is directly determined to be reliable data; the level determination module is used to determine the reliability level of the device based on the historical trajectory database; specifically, it counts the number of unreliable data in the historical trajectory database within a preset statistical period, and determines the reliability level of the device based on the number of unreliable data; the rule determination module is used to determine the data cleaning rules corresponding to the real-time trajectory data based on the personnel restriction scenario of accessing the target area, the reliable or unreliable identifier of the real-time trajectory data, and the reliability level of the device; wherein, the target area is a personnel restriction area; the trajectory correction module is used to perform data cleaning according to the data cleaning rules and correct the trajectory of the monitored target.

[0044] The steps of the system for implementing the reliability identification and correction method for data reported by the mining positioning system equipment disclosed in the above embodiments have already been described and will not be repeated here.

[0045] For example, the execution unit in the identification and attachment module that determines whether the real-time trajectory data is reliable based on the most recent valid location of the monitored target in the historical trajectory database when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, and then attaches a reliable or unreliable identifier to the real-time trajectory data, includes: a calculation unit, used to calculate the distance between the real-time trajectory data and the current most recent valid location of the monitored target at two positions with opposite directions when the device first reads the positioning card corresponding to the monitored target and the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction; a comparison unit, used to compare the two calculated distances and determine that the direction corresponding to the position with the smaller distance to the most recent valid location is the reasonable reporting direction; a judgment unit, used to determine whether the position direction of the monitored target in the real-time trajectory data is consistent with the reasonable reporting direction, and if the position direction is consistent with the reasonable reporting direction, then the real-time trajectory data is determined to be reliable data; otherwise, the real-time trajectory data is unreliable data; and an identification and attachment unit, used to attach a reliable identifier to the real-time trajectory data determined to be reliable data and an unreliable identifier to the real-time trajectory data determined to be unreliable data.

[0046] For example, in the aforementioned identifier attachment module, when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the process of determining whether the real-time trajectory data is reliable based on the most recent valid position of the monitored target in the historical trajectory database, and then attaching a reliable or unreliable identifier to the real-time trajectory data, can also be implemented by the following scheme: When the position direction of the monitored target in the real-time trajectory data of the same monitored target reported by the device is opposite to the predicted trajectory direction, the device verifies whether it has reported the entry data of the positioning card within a preset time range from the most recent valid position of the monitored target; if the device has reported the entry data of the positioning card within the preset time range, the real-time trajectory data is determined to be unreliable data; otherwise, the real-time trajectory data is reliable data; a reliable identifier is attached to the real-time trajectory data determined to be reliable data, and an unreliable identifier is attached to the real-time trajectory data determined to be unreliable data.

[0047] For example, in the rule determination module, based on the personnel restriction scenario for accessing the target area, the process of determining the data cleaning rules corresponding to the real-time trajectory data according to the trusted or untrusted identifier of the real-time trajectory data and the trust level of the device is as follows: Based on the type of the target area, determine the personnel limit for the target area; when the monitored target is in a non-restricted area and its current real-time trajectory data has an untrusted identifier, retain the data for data reference; when the monitored target is in the target area and its current real-time trajectory data has an untrusted identifier, filter the data and simultaneously record the status of the monitored target in the target area as untrusted; when the monitored target is in a non-restricted area and its current real-time trajectory data has a trusted identifier, retain the data for data reference; when the monitored target is in the target area and its current real-time trajectory data has a trusted identifier, determine whether the monitored target entering the target area will exceed the personnel limit, thereby determining the data cleaning rules corresponding to the real-time trajectory data. The process of determining whether the monitored target entering the target area will exceed the personnel limit has been detailed in the above method description and will not be repeated here.

[0048] Based on the same inventive concept as the above method embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the reliability identification and correction method for data reported by the mining positioning system equipment in the above embodiments.

[0049] In one embodiment, the electronic device may be a server, and in this embodiment, the structure of the electronic device may be as follows: Figure 6 As shown, it includes a memory 201, a communication module 203, and one or more processors 202.

[0050] The memory 201 is used to store computer programs executed by the processor 202. The memory 201 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0051] Memory 201 may be volatile memory, such as random-access memory (RAM); memory 201 may also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 201 may be any other medium capable of carrying or storing a desired computer program having the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 201 may be a combination of the above-described memories.

[0052] The processor 202 may include one or more central processing units (CPUs) or digital processing units, etc. The processor 202 is used to implement the aforementioned audio data processing method when calling a computer program stored in the memory 201.

[0053] The communication module 203 is used to communicate with terminal devices and other servers.

[0054] This application embodiment does not limit the specific connection medium between the memory 201, communication module 203, and processor 202 described above. This application embodiment... Figure 6 The memory 201 and the processor 202 are connected via a bus 204, and the bus 204 is in Figure 6 The connections between other components are illustrated with arrows and are for illustrative purposes only, not as limiting information. Bus 204 can be divided into address bus, data bus, control bus, etc. For ease of description, Figure 6 The text uses only one arrow to describe it, but does not indicate that there is only one bus or one type of bus.

[0055] Based on the same inventive concept as the above-described method embodiments, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program. When the computer program is run on a computer, it enables the electronic device to implement the reliability identification and correction method for data reported by the mining positioning system equipment in the above embodiments. The computer-readable storage medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a magnetic disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0056] Based on the same inventive concept as the above-described method embodiments, embodiments of the present invention also provide a computer program product. The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to perform the steps in the reliability identification and correction method for reported data from a mining positioning system device according to various exemplary embodiments of this application, as described above. The program product can take the form of any combination of one or more readable media. These computer program commands can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the commands executed by the processor of the computer or other programmable data processing device generate a process for implementing... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for identifying and correcting the reliability of data reported by mining positioning system equipment, characterized in that, include: Acquire several historical location data and the corresponding historical real locations of multiple monitored objects reported by the device in a time sequence, and construct a historical trajectory database. Based on the historical trajectory database, a target position and orientation prediction model based on machine learning is established and trained. The system acquires real-time trajectory data reported by the device to the monitored target, compares the real-time trajectory data with the predicted trajectory direction of the monitored target predicted by the target position and direction prediction model, and determines whether the position and direction of the monitored target in the real-time trajectory data are opposite to the predicted trajectory direction. When the location direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the reliability of the real-time trajectory data is determined based on the most recent valid location of the monitored target in the historical trajectory database, and then a reliable or unreliable identifier is attached to the real-time trajectory data. Otherwise, if the position and direction of the monitored target in the real-time trajectory data are the same as the predicted trajectory direction, the real-time trajectory data is directly determined to be reliable data. The reliability level of the device is determined based on the historical trajectory database. Based on the personnel restriction scenario for accessing the target area, the data cleaning rules corresponding to the real-time trajectory data are determined according to the trusted or untrusted identifier of the real-time trajectory data and the trust level of the device; wherein, the target area is the personnel restriction area. Data is cleaned according to the aforementioned data cleaning rules to correct the trajectory of the monitored target.

2. The method for identifying and correcting the reliability of data reported by mining positioning system equipment according to claim 1, characterized in that, When the location direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the process of determining whether the real-time trajectory data is reliable based on the most recent valid location of the monitored target in the historical trajectory database, and then attaching a reliable or unreliable label to the real-time trajectory data, is as follows: When the device first reads the positioning card corresponding to the monitored target, and the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the distance between the real-time trajectory data and the current nearest effective position of the monitored target is calculated for the two positions with opposite directions. Compare the two calculated distances and determine the direction corresponding to the position with the smaller distance to the nearest valid position as the reasonable reporting direction; Determine whether the location and direction of the monitored target in the real-time trajectory data are consistent with the reasonable reporting direction. If the location and direction are consistent with the reasonable reporting direction, the real-time trajectory data is determined to be reliable data; otherwise, the real-time trajectory data is unreliable data. A trustworthy identifier is added to real-time trajectory data that is determined to be trustworthy data, and an untrustworthy identifier is added to real-time trajectory data that is determined to be untrustworthy data.

3. The method for identifying and correcting the reliability of data reported by mining positioning system equipment according to claim 1, characterized in that, When the location direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, the process of determining whether the real-time trajectory data is reliable based on the most recent valid location of the monitored target in the historical trajectory database, and then attaching a reliable or unreliable label to the real-time trajectory data, is as follows: If the position direction of the monitored target in the real-time trajectory data of the same monitored target reported by the device is opposite to the predicted trajectory direction, then it is verified whether the device has reported the entry data of the positioning card within a preset time range from the most recent effective position of the monitored target. If the device reports entry data of the positioning card within a preset time range, the real-time trajectory data is determined to be unreliable data. Otherwise, the real-time trajectory data is reliable data; A trustworthy identifier is added to real-time trajectory data that is determined to be trustworthy data, and an untrustworthy identifier is added to real-time trajectory data that is determined to be untrustworthy data.

4. The method for identifying and correcting the reliability of data reported by mining positioning system equipment according to claim 1, characterized in that, The process of determining the reliability level of the device based on the historical trajectory database is as follows: The number of unreliable data in the historical trajectory database within a preset statistical period is counted, and the credibility level of the device is determined based on the number of unreliable data.

5. The method for identifying and correcting the reliability of data reported by mining positioning system equipment according to claim 1, characterized in that, Based on the limited access scenario for the target area, the process of determining the data cleaning rules corresponding to the real-time trajectory data according to the trusted or untrusted identifiers of the real-time trajectory data and the trust level of the device is as follows: The maximum number of people allowed in the target area is determined based on the type of the target area. When the monitored target is in a non-restricted area and its real-time trajectory data at the current moment is marked with an untrusted identifier, the data is retained for data reference. When the monitored target is within the target area, and its real-time trajectory data at the current moment is marked with an untrusted identifier, the data is filtered out, and the status of the monitored target within the target area is recorded as untrusted. When the monitored target is in a non-restricted area and its real-time trajectory data at the current moment is accompanied by a trusted identifier, the data is retained for data reference. When the monitored target is within the target area and its real-time trajectory data at the current moment is accompanied by a trusted identifier, it is determined whether the monitored target's entry into the target area will exceed the limit of personnel, thereby determining the data cleaning rules corresponding to the real-time trajectory data.

6. The method for identifying and correcting the reliability of data reported by mining positioning system equipment according to claim 5, characterized in that, The process of determining whether the monitored target's entry into the target area will exceed the personnel limit, and thus determining the data cleaning rules corresponding to the real-time trajectory data, is as follows: If the monitored target is within the target area, its real-time trajectory data at the current moment is appended with a trusted identifier, and the monitored target's entry into the target area will not exceed the limit of personnel, then the data is retained. When the monitored target is within the target area, its current real-time trajectory data is appended with a trusted identifier, and the monitored target entering the target area would exceed the personnel limit, then: Determine whether the real-time trajectory data of the monitored target at the previous moment has an untrusted identifier attached. If the real-time trajectory data has an untrusted identifier attached, filter or retain the data according to the data cleaning rules corresponding to the trust level of the device; otherwise, directly enable the over-capacity alarm. Among them, when data is retained according to the data cleaning rules corresponding to the trust level of the device, the over-capacity alarm is enabled.

7. The method for identifying and correcting the reliability of data reported by mining positioning system equipment according to claim 5, characterized in that, Based on the limited access scenario for the target area, the process of determining the data cleaning rules corresponding to the real-time trajectory data according to the trusted or untrusted identifiers of the real-time trajectory data and the trust level of the device further includes: If the monitored target is located within the overlapping area of ​​the target area and the adjacent restricted area, and the real-time trajectory data corresponding to the current moment is appended with a trusted identifier, then: If the monitored target enters the target area without exceeding the limit number of people, the data is retained; if the monitored target enters the target area and exceeds the limit number of people, the real-time trajectory data of the monitored target in the target area at the previous moment is checked to see if it has an untrusted identifier attached. If the real-time trajectory data has an untrusted identifier attached, the data is filtered or retained according to the data cleaning rules corresponding to the trust level of the device; otherwise, the over-capacity alarm is directly activated. The system verifies whether the real-time trajectory data of the monitored target at the previous moment before entering the adjacent restricted area has an untrusted identifier. If the real-time trajectory data has an untrusted identifier, the data is filtered or retained according to the data cleaning rules corresponding to the trust level of the device; otherwise, the data is directly retained. If directly retaining the data or retaining the data according to the data cleaning rules corresponding to the trust level of the device would cause the monitored target to exceed the limit of personnel when entering the adjacent restricted area, an over-capacity alarm is activated.

8. A reliability identification and correction system for data reported by a mining positioning system, characterized in that, include: The acquisition module is used to acquire several historical location data reported by the device to multiple monitored objects in a time sequence and the historical real location corresponding to each historical location data, and to build a historical trajectory database. The model building module is used to build and train a target position and direction prediction model based on machine learning, according to the historical trajectory database. The comparison and judgment module is used to acquire real-time trajectory data reported by the device to the monitored target, compare the real-time trajectory data with the predicted trajectory direction of the monitored target predicted by the target position and direction prediction model, and determine whether the position and direction of the monitored target in the real-time trajectory data are opposite to the predicted trajectory direction. The identification and attachment module is used to determine whether the real-time trajectory data is reliable based on the most recent valid location of the monitored target in the historical trajectory database when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, and then attach a reliable or unreliable identifier to the real-time trajectory data. Otherwise, if the position and direction of the monitored target in the real-time trajectory data are the same as the predicted trajectory direction, the real-time trajectory data is directly determined to be reliable data. The rating determination module is used to determine the credibility rating of the device based on the historical trajectory database. The rule determination module is used to determine the data cleaning rules corresponding to the real-time trajectory data based on the personnel restriction scenario of accessing the target area, according to the trusted or untrusted identifier of the real-time trajectory data and the trust level of the device; wherein, the target area is a personnel restriction area; The trajectory correction module is used to clean the data according to the data cleaning rules and correct the trajectory of the monitored target.

9. The reliability identification and correction system for data reported by mining positioning system equipment according to claim 8, characterized in that, The execution unit in the identification appending module, which determines whether the real-time trajectory data is trustworthy based on the most recent valid location of the monitored target in the historical trajectory database when the position direction of the monitored target in the real-time trajectory data is opposite to the predicted trajectory direction, and then appends a trustworthy or untrustworthy identifier to the real-time trajectory data, includes: The calculation unit is used to calculate the distance between the real-time trajectory data and the current nearest effective position of the monitoring target when the device first reads the positioning card corresponding to the monitoring target, and the position direction of the monitoring target in the real-time trajectory data is opposite to the predicted trajectory direction. The comparison unit is used to compare two calculated distances and determine the direction corresponding to the position with the smaller distance to the nearest valid position as the reasonable reporting direction; The judgment unit is used to determine whether the position direction of the monitored target in the real-time trajectory data is consistent with the reasonable reporting direction. If the position direction is consistent with the reasonable reporting direction, the real-time trajectory data is determined to be reliable data; otherwise, the real-time trajectory data is unreliable data. The identification attachment unit is used to attach a trustworthy identifier to real-time trajectory data that is determined to be trustworthy data, and to attach an untrustworthy identifier to real-time trajectory data that is determined to be untrustworthy data.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it enables the electronic device to implement the reliability identification and correction method for data reported by the mining positioning system equipment as described in any one of claims 1 to 7.

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