Abnormal behavior track monitoring method and system based on big data

Abstract

The invention relates to the technical field of personnel monitoring, and discloses a big data-based abnormal behavior track monitoring method and system, and the method comprises the steps: dividing different types of activity regions, including a first activity region, a second activity region and a dangerous activity region, and analyzing the real-time position, historical travel record and consumption record of a target person, thereby achieving the monitoring of the abnormal behavior track. The system can give out multi-level alarms in different situations; the first-level alarm is triggered based on the target person entering the dangerous area; the second-level alarm is triggered based on the target person at the edge of the dangerous area; the third-level alarm is judged according to the frequency of the visited place of the target person; and the fourth-level alarm is evaluated in combination with the abnormality of the consumption place and the amount. In addition, through analysis of the mileage proportion and the mileage threshold value, the system can further judge whether a fifth-level alarm is given or not.

Description

Technical Field

[0001] The present invention relates to the technical field of personnel monitoring, and in particular to a method and system for monitoring abnormal behavior trajectories based on big data. Background Art

[0002] The vigorous development of big data has given rise to trajectory big data, which is manifested as large-scale, high-speed data streams generated by positioning devices. Timely and effective analysis and processing of trajectory big data in the form of data streams can discover abnormal phenomena hidden in trajectory data, thereby serving applications such as personnel management and security control. With the development of science and technology and the popularization of big data technology, behavior monitoring systems are playing an increasingly important role in personnel management and security control.

[0003] First, most traditional monitoring systems rely on a single type of data, such as video or location data. This single data source approach is incapable of dealing with complex behavior patterns and is unable to capture potential abnormal behaviors. Especially for those abnormal events that rely on the combined effect of multiple factors, monitoring only from a single dimension is prone to misjudgment or missed judgments. Second, existing systems often lack the ability to comprehensively analyze spatiotemporal data. Although they can record the location data of target personnel, they cannot effectively combine temporal and spatial characteristics for in-depth analysis. For example, existing technologies are usually unable to determine the rationality of a person appearing at a specific location within a specific time period. Then, although many systems can record historical travel or behavior data, existing methods usually do not fully utilize these data for the identification and analysis of behavior patterns. Historical data can reveal the regular behavior patterns of individuals and help identify abnormal deviations, but this potential has not been tapped in many systems. Finally, existing technologies often process different types of data in isolation and lack the ability to integrate data across dimensions. For example, location data and consumption records are often processed separately, without being combined for comprehensive analysis, making it difficult for monitoring systems to identify complex abnormal behaviors.

[0004] In summary, there is an urgent need for an abnormal behavior trajectory monitoring method and system based on big data, which can achieve accurate monitoring of abnormal behavior through integrating multi-dimensional data, including real-time location, historical travel records and consumption records, combined with flexible alarm strategies, regional division and behavior pattern analysis. Summary of the invention

[0005] The present invention provides a method and system for monitoring abnormal behavior trajectories based on big data, which promotes solving the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a method and system for monitoring abnormal behavior trajectories based on big data, comprising:

[0007] Obtain the personnel whose abnormal behavior trajectory needs to be monitored and record them as target personnel;

[0008] Delimiting a first activity area, wherein the first activity area refers to an activity area where the target person is allowed to move freely;

[0009] Delimiting a second activity area, wherein the second activity area refers to an activity area where the target person is not allowed to travel freely;

[0010] Delineate dangerous activity areas, where the dangerous activity areas refer to activity areas where target persons are prohibited from entering;

[0011] Determine whether to issue a first-level alarm to the target person according to the first-level alarm strategy based on the target person's location;

[0012] Determine whether to issue a second-level alarm to the target person according to the second-level alarm strategy based on the target person's location;

[0013] Obtain the historical travel records of the target person;

[0014] The historical travel records include the number of trips, the destination of each trip, the time of arrival at the destination, and the length of stay at each destination;

[0015] Determine whether to issue a third-level alert to the target person according to the third-level alert strategy based on the target person's historical travel records;

[0016] Determine whether to issue a fourth-level alarm to the target person based on the target person's consumption record and the fourth-level alarm strategy.

[0017] Optionally, judging whether to issue a first-level alarm to the target person according to the first-level alarm strategy based on the location of the target person specifically includes:

[0018] Get the real-time location of the target person and record it as the first location;

[0019] Determining whether the dangerous activity area includes the first location;

[0020] If the dangerous activity area includes the first location, a first-level alarm is issued to the target personnel;

[0021] If the hazardous activity area does not include the first position, no processing is performed.

[0022] Optionally, judging whether to issue a second-level alarm to the target person according to the second-level alarm strategy based on the location of the target person specifically includes:

[0023] Get the hazardous activity area;

[0024] Get the geometric center of the dangerous activity area and record it as the auxiliary point;

[0025] Obtain the regional boundary of the dangerous activity area, recorded as the auxiliary boundary;

[0026] Get the distance between each position on the auxiliary boundary and the auxiliary point, and select the distance with the largest length, which is recorded as the auxiliary radius;

[0027] Setting an auxiliary distance threshold; the auxiliary distance threshold is used to demarcate a third activity area based on the dangerous activity area;

[0028] A circle is drawn with the auxiliary point as the center and the sum of the auxiliary radius and the auxiliary distance threshold as the radius to form a circular domain. The area enclosed by the circular domain is recorded as the third active area.

[0029] Optionally, judging whether to issue a second level alarm to the target person according to the second level alarm strategy based on the location of the target person further includes:

[0030] Obtain the real-time location of the target person and record it as the second location;

[0031] determining whether the third activity area includes the second position;

[0032] If the third active area does not include the second position, no processing is performed;

[0033] If the third active area includes the second position;

[0034] Obtain the time when the target person enters the third activity area, which is recorded as the first time;

[0035] The time before the first moment and 24 hours apart from the first moment is recorded as the second moment;

[0036] The time period between the first moment and the second moment is recorded as the target time period;

[0037] A number threshold and a duration threshold are set, and the number threshold and the duration threshold are used to assist in determining whether to issue a second-level alarm to the target person.

[0038] Optionally, the step of determining whether to issue a second level alarm to the target person according to the second level alarm strategy based on the location of the target person also includes:

[0039] Obtain the historical travel records of the target person;

[0040] The travel records of the target person whose destination is located in the third activity area in the historical travel records constitute the first set;

[0041] For each travel record in the first set, obtain the time of arrival at the destination;

[0042] Acquire travel records whose arrival time at the destination is within the target time period to form a second set;

[0043] For each travel record in the second set, obtain the stay time at the destination;

[0044] Acquire travel records whose stay time at the destination is greater than or equal to the duration threshold to form a third set;

[0045] Get the number of elements in the third set, recorded as the first number;

[0046] If the first number is greater than or equal to the number threshold, a second level alarm is issued to the target person;

[0047] If the first number is less than the number threshold, no processing is performed.

[0048] Optionally, judging whether to issue a third-level alarm to the target person according to the third-level alarm strategy based on the historical travel record of the target person specifically includes:

[0049] Obtain all destinations in the target person's historical travel records;

[0050] For each destination, obtain the number of times the target person arrives at this destination, and record it as the number of first times;

[0051] Get the number of trips in the target person's historical travel records, and record it as the second number;

[0052] Calculate the first number ÷ the second number, and use the result as the auxiliary ratio;

[0053] Set the ratio threshold;

[0054] The ratio threshold is used to characterize the frequency of the target person arriving at the destination;

[0055] Get the destination of the target person at this time, and record it as the target destination;

[0056] If the target destination is different from all destinations in the target person's historical travel records, a third-level alert will be issued to the target person;

[0057] If the target destination is the same as one of the destinations in the target person's historical travel records;

[0058] Obtain the auxiliary ratio of the destinations that are the same as the target destination in the historical travel records of the target person;

[0059] If the auxiliary ratio is greater than the ratio threshold, no processing is performed;

[0060] If the auxiliary ratio is less than or equal to the ratio threshold, a third-level alarm is issued to the target person.

[0061] Optionally, judging whether to issue a fourth-level alarm to the target person according to the fourth-level alarm strategy based on the consumption record of the target person specifically includes:

[0062] Obtain the consumption records of the target person;

[0063] The consumption record includes the amount of expenditure, the time of expenditure and the location of expenditure;

[0064] Get the target person's expenditure position and record it as the expenditure position;

[0065] If the expenditure location is different from each destination in the target person's historical travel records;

[0066] Setting ancillary spending thresholds;

[0067] The auxiliary expenditure threshold is used to assist in determining whether to issue a fourth-level alert to the target person;

[0068] Get the target person's expenditure amount;

[0069] If the target person's expenditure amount is greater than or equal to the auxiliary expenditure threshold, a fourth-level alarm will be issued to the target person.

[0070] Optionally, judging whether to issue a fourth-level alarm to the target person according to the fourth-level alarm strategy based on the consumption record of the target person further includes:

[0071] Set the spending frequency threshold, spending interval threshold and spending amount threshold;

[0072] The expenditure number threshold, expenditure interval threshold and expenditure amount threshold are used to assist in determining whether to issue a fourth-level alert to the target person;

[0073] Get the time when the target person makes the expenditure, and record it as the expenditure time;

[0074] The time before the expenditure time and separated from the expenditure time by the expenditure interval time threshold is recorded as the end time;

[0075] The time period between the expenditure time and the end time is recorded as the expenditure time period;

[0076] Obtain the consumption records of the target person in the expenditure time period to form an expenditure set;

[0077] Obtain consumption records in the expenditure set whose expenditure amount is less than or equal to the expenditure amount threshold to form a small expenditure set;

[0078] Get the number of elements in the small expenditure set and record it as the number of expenditures;

[0079] If the number of expenditures is less than the expenditure threshold, no processing will be performed;

[0080] If the number of expenditures is greater than or equal to the expenditure threshold, a fourth-level alarm will be issued to the target person.

[0081] Optionally, also include:

[0082] Through the second activity area minimum mileage threshold input port, manually input the second activity area minimum mileage threshold used to assist in determining whether to issue a fifth-level alarm to the target person;

[0083] Through the second activity area maximum mileage threshold input port, manually input the second activity area maximum mileage threshold used to assist in determining whether to issue a fifth-level alarm to the target person;

[0084] The minimum mileage threshold of the second activity area is less than the maximum mileage threshold of the second activity area;

[0085] Through the mileage ratio threshold input port, manually input the mileage ratio threshold used to assist in determining whether to issue a fifth-level alarm to the target person;

[0086] The total travel mileage of the target person in the first activity area is obtained through the data acquisition module, which is recorded as the first mileage, and the total travel mileage of the target person in the second activity area is obtained, which is recorded as the second mileage;

[0087] By comparing the second mileage with the minimum mileage threshold of the second activity area and the maximum mileage threshold of the second activity area;

[0088] When the second mileage is ≤ the minimum mileage threshold of the second activity area, no processing is performed;

[0089] When the second mileage is greater than the maximum mileage threshold of the second activity area, a fifth-level warning is issued to the target person;

[0090] When the minimum mileage threshold of the second activity area ≤ the second mileage ≤ the maximum mileage threshold of the second activity area;

[0091] Compare the second mileage ÷ the first mileage with the mileage ratio threshold by a comparison module;

[0092] If the second mileage ÷ the first mileage < the mileage ratio threshold, no processing will be done;

[0093] If the second mileage ÷ the first mileage ≥ the mileage ratio threshold, a fifth level alarm is issued to the target person.

[0094] Optional, including:

[0095] Information acquisition module, used to obtain the historical travel records and consumption records of the target person;

[0096] The second activity area minimum mileage threshold input port is used to input the second activity area minimum mileage threshold;

[0097] A second activity area maximum mileage threshold input port, used to input the second activity area maximum mileage threshold;

[0098] Mileage ratio threshold input port, used to input mileage ratio threshold;

[0099] A data acquisition module, in which a user acquires the total travel mileage of a target person in a first activity area and acquires the total travel mileage of a target person in a second activity area;

[0100] The comparison module is used to compare the second mileage with the minimum mileage threshold of the second activity area and the maximum mileage threshold of the second activity area.

[0101] The present invention has the following beneficial effects:

[0102] 1. This method and system for monitoring abnormal behavior trajectories based on big data, by demarcating different activity areas - the first activity area, the second activity area and the dangerous activity area, the system can perform differentiated management on the behavior of target personnel according to the importance and control requirements of different areas; in addition, regional division is also convenient for management and resource allocation. For example, the system can allocate different monitoring resources and strategies according to the importance of different areas. More sensors and monitoring equipment can be deployed in dangerous areas to ensure high-density monitoring and rapid response; in the first activity area, the monitoring density can be relatively reduced to save resources; through refined regional division and management, the system can respond to different monitoring needs more flexibly and provide more accurate and efficient abnormal behavior monitoring. This multi-level management method not only improves the monitoring effect, but also optimizes resource utilization, ensuring that the system can provide the best monitoring performance in various situations. At the same time, this method also provides a basis for further expansion and customization of monitoring strategies in the future, and has high application value and development potential.

[0103] 2. This method and system for monitoring abnormal behavior trajectories based on big data can flexibly respond to various abnormal behaviors according to different risk levels and processing methods by setting different levels of alarm strategies, thereby improving the accuracy and effectiveness of monitoring. First, the multi-level alarm strategy can provide more detailed risk management. The system sets different alarm levels according to the behavior and location of the target person. For example, the first-level alarm is used to respond to the situation where the target person enters a dangerous area. This is the most direct risk warning, ensuring that measures are taken immediately to prevent potential security incidents. In addition, the system combines historical travel records and consumption records to identify potential high-risk behaviors. This hierarchical management method enables the system to take corresponding processing measures according to different risk levels to avoid excessive alarms and missed reports. Secondly, the multi-level alarm strategy can also combine different data sources to provide a comprehensive risk assessment. For example, the system can combine real-time location data, historical travel records, and consumption records to conduct a multi-dimensional comprehensive analysis to identify more complex and hidden abnormal behavior patterns. For example, if a target person spends a large amount of money at a place that he or she rarely visits, the system can use this comprehensive analysis to determine whether his or her behavior is abnormal and issue a corresponding high-level alarm. In short, the multi-level alarm strategy significantly improves the flexibility and intelligence of the abnormal behavior trajectory monitoring method based on big data. By setting alarm strategies of different levels, the system can flexibly respond to various abnormal behaviors according to different risk levels and processing methods, thereby improving the accuracy and effectiveness of monitoring. This flexible alarm strategy not only improves the practicality and friendliness of the system, but also provides a basis for future expansion and customization of monitoring strategies.

[0104] 3. This method and system for monitoring abnormal behavior trajectories based on big data obtains the real-time location of the target person and determines whether the dangerous activity area contains the real-time location of the target person. If the dangerous activity area contains the real-time location of the target person, a first-level alarm is issued to the target person. In this way, it is possible to immediately identify whether the target person has entered the dangerous activity area and quickly issue a first-level alarm. This immediate response capability can effectively intervene in the early stages of potential dangers to ensure the safety of the target person.

[0105] 4. This method and system for monitoring abnormal behavior trajectories based on big data first obtains the geometric center of the dangerous activity area, obtains the maximum distance from the geometric center of the dangerous activity area on the boundary of the dangerous activity area, and draws a circle with the geometric center as the center and the maximum distance as the radius to form a circular domain. The area within the circular domain is recorded as the third activity area. The third activity area includes the dangerous activity area and the area near the dangerous activity area. If the target person enters the third activity area, the time when the target person enters the third activity area is obtained and recorded as the first moment, and the moment 24 hours before the first moment is recorded as the second moment; between the first moment and the second moment, the travel records of the target person whose stay time in the third activity area is greater than or equal to the duration are screened, and the number is obtained. If the number is greater than the number threshold, it means that the target person frequently approaches the dangerous activity area and has a certain risk, so a second-level alarm is issued to the target person at this time. In this way, the abnormal behavior of the target person in the edge area of ​​the dangerous activity area can be captured, thereby improving the comprehensiveness and accuracy of monitoring.

[0106] 5. This method and system for monitoring abnormal behavior trajectories based on big data obtains each destination in the travel record of the target person, and for each destination, calculates the ratio of the number of times it appears to the total number of destinations in the travel record of the target person as the auxiliary ratio; obtains the destination of the target person's trip this time, and records it as the target destination. If the auxiliary ratio of the destination in the travel record that is the same as the target destination is less than or equal to the ratio threshold, it means that the location arrived at by the target person this time is an unfamiliar location, so a third-level alarm is issued to the target person. In this way, the target person can be prevented from entering an unfamiliar area, thereby ensuring the safety of the target person.

[0107] 6. This method and system for monitoring abnormal behavior trajectories based on big data improves the acquisition of consumption records and travel records of target persons; if the target person makes expenditures at a location he has never been to, the following steps are performed: first, an auxiliary expenditure threshold is set to obtain the expenditure amount of the target person; if the expenditure amount of the target person is greater than or equal to the auxiliary expenditure threshold, it means that the target person has made large expenditures at an unfamiliar location, which is marked as abnormal behavior, and a fourth-level alarm is issued to the target person; secondly, an expenditure number threshold, an expenditure interval time threshold, and an expenditure amount threshold are set to obtain the moment when the target person makes expenditures, which is recorded as the expenditure moment; the moment before the expenditure moment with an interval of the expenditure interval time threshold is obtained, which is recorded as the end moment; between the end moment and the expenditure moment, consumption records with expenditure amounts less than or equal to the expenditure amount threshold are screened out in the user consumption records, and the number is counted; if the number is greater than or equal to the expenditure number threshold, it means that the user has frequent small transactions at an unfamiliar location, which is marked as abnormal behavior, and a fourth-level alarm is issued to the target person. Through the above method, the property safety of the target person can be guaranteed. This method expands the monitoring scope of the system so that it is not limited to geographical location, but also includes economic behavior.

[0108] 7. A method and system for monitoring abnormal behavior trajectories based on big data, artificially setting the minimum mileage threshold of the second activity area, the maximum mileage threshold of the second activity area and the mileage ratio threshold, obtaining the total travel mileage of the target person in the first activity area through the data acquisition module, recorded as the first mileage, and obtaining the total travel mileage of the target person in the second activity area, recorded as the second mileage; when the second mileage ≤ the minimum mileage threshold of the second activity area, it means that the travel mileage of the target person in the second acquisition area is very small, and no alarm is required; when the second mileage > the maximum mileage threshold of the second activity area, it means that the travel mileage of the target person in the second acquisition area is very large, and it exceeds the limit at this time, so the fifth level alarm is issued to the target person; when the minimum mileage threshold of the second activity area ≤ the second mileage ≤ the maximum mileage threshold of the second activity area, and the second mileage ÷ the first mileage < the mileage ratio threshold, it means that although the target person travels in the second activity area, the proportion of travel in the second activity area is very small, and no alarm is required; when the minimum mileage threshold of the second activity area ≤ the second mileage ≤ the maximum mileage threshold of the second activity area, and the second mileage ÷ the first mileage ≥ the mileage ratio threshold, it means that the travel proportion of the target person in the second activity area is very large, which exceeds the limit, so the fifth level alarm is issued to the target person. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0110] 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.

[0111] Example, see Figure 1 , a method and system for monitoring abnormal behavior trajectory based on big data, comprising:

[0112] Obtain the personnel whose abnormal behavior trajectory needs to be monitored and record them as target personnel;

[0113] Delimiting a first activity area, wherein the first activity area refers to an activity area where the target person is allowed to move freely;

[0114] The first activity area allows the target person to move freely, which reduces excessive interference with normal behavior and improves the system's friendliness and user experience. The target person's activities in this area are not restricted, and the system will not issue unnecessary alarms, thus avoiding interference with normal life and work.

[0115] Delimiting a second activity area, wherein the second activity area refers to an activity area where the target person is not allowed to travel freely;

[0116] The second activity area is a restricted area. According to actual needs, the system can conduct special monitoring of the activities of the target person in this area. By setting specific restrictions and monitoring strategies, the system can evaluate when the target person enters or leaves the second activity area to ensure that appropriate monitoring measures are taken when necessary.

[0117] Delineate dangerous activity areas, where the dangerous activity areas refer to activity areas where target persons are prohibited from entering;

[0118] Dangerous activity areas are completely prohibited for target personnel to enter, providing the most direct and strict security protection; through real-time location monitoring, the system can immediately identify whether the target personnel have entered the dangerous area and quickly issue a first-level alarm; this instant response capability can effectively intervene in the early stage of potential dangers to ensure the safety of the target personnel and the safety of the area;

[0119] This method and system for monitoring abnormal behavior trajectories based on big data, by demarcating different activity areas - the first activity area, the second activity area and the dangerous activity area, the system can perform differentiated management on the behavior of target personnel according to the importance and control requirements of different areas; in addition, regional division is also convenient for management and resource allocation. For example, the system can allocate different monitoring resources and strategies according to the importance of different areas. More sensors and monitoring equipment can be deployed in dangerous areas to ensure high-density monitoring and rapid response; in the first activity area, the monitoring density can be relatively reduced to save resources; through refined regional division and management, the system can respond to different monitoring needs more flexibly and provide more accurate and efficient abnormal behavior monitoring. This multi-level management method not only improves the monitoring effect, but also optimizes resource utilization, ensuring that the system can provide the best monitoring performance in various situations. At the same time, this method also provides a basis for further expansion and customization of monitoring strategies in the future, and has high application value and development potential;

[0120] Determine whether to issue a first-level alarm to the target person according to the first-level alarm strategy based on the target person's location;

[0121] Determine whether to issue a second-level alarm to the target person according to the second-level alarm strategy based on the target person's location;

[0122] Obtain the historical travel records of the target person;

[0123] The historical travel records include the number of trips, the destination of each trip, the time of arrival at the destination, and the length of stay at each destination;

[0124] Determine whether to issue a third-level alert to the target person according to the third-level alert strategy based on the target person's historical travel records;

[0125] Determine whether to issue a fourth-level alarm to the target person based on the target person's consumption record and the fourth-level alarm strategy.

[0126] The step of determining whether to issue a first-level alarm to the target person according to the first-level alarm strategy based on the location of the target person specifically includes:

[0127] Get the real-time location of the target person and record it as the first location;

[0128] Determining whether the dangerous activity area includes the first location;

[0129] If the dangerous activity area includes the first location, a first-level alarm is issued to the target personnel;

[0130] If the hazardous activity area does not include the first location, no processing is performed;

[0131] The method and system for monitoring abnormal behavior trajectories based on big data obtain the real-time location of a target person and determine whether a dangerous activity area contains the real-time location of the target person. If the dangerous activity area contains the real-time location of the target person, a first-level alarm is issued to the target person. In this way, it is possible to immediately identify whether the target person has entered the dangerous activity area and quickly issue a first-level alarm. This immediate response capability can effectively intervene in the early stage of potential danger to ensure the safety of the target person.

[0132] The step of determining whether to issue a second level alarm to the target person according to the second level alarm strategy based on the location of the target person specifically includes:

[0133] Get the hazardous activity area;

[0134] Get the geometric center of the dangerous activity area and record it as the auxiliary point;

[0135] Obtain the regional boundary of the dangerous activity area, recorded as the auxiliary boundary;

[0136] Get the distance between each position on the auxiliary boundary and the auxiliary point, and select the distance with the largest length, which is recorded as the auxiliary radius;

[0137] Setting an auxiliary distance threshold; the auxiliary distance threshold is used to demarcate a third activity area based on the dangerous activity area;

[0138] A circle is drawn with the auxiliary point as the center and the sum of the auxiliary radius and the auxiliary distance threshold as the radius to form a circular domain. The area enclosed by the circular domain is recorded as the third active area.

[0139] The step of determining whether to issue a second level alarm to the target person according to the second level alarm strategy based on the location of the target person also includes:

[0140] Obtain the real-time location of the target person and record it as the second location;

[0141] determining whether the third activity area includes the second position;

[0142] If the third active area does not include the second position, no processing is performed;

[0143] If the third active area includes the second position;

[0144] Obtain the time when the target person enters the third activity area, which is recorded as the first time;

[0145] The time before the first moment and 24 hours apart from the first moment is recorded as the second moment;

[0146] The time period between the first moment and the second moment is recorded as the target time period;

[0147] A number threshold and a duration threshold are set, and the number threshold and the duration threshold are used to assist in determining whether to issue a second-level alarm to the target person.

[0148] The step of determining whether to issue a second level alarm to the target person according to the second level alarm strategy based on the position of the target person also includes:

[0149] Obtain the historical travel records of the target person;

[0150] The travel records of the target person whose destination is located in the third activity area in the historical travel records constitute the first set;

[0151] For each travel record in the first set, obtain the time of arrival at the destination;

[0152] Acquire travel records whose arrival time at the destination is within the target time period to form a second set;

[0153] For each travel record in the second set, obtain the stay time at the destination;

[0154] Acquire travel records whose stay time at the destination is greater than or equal to the duration threshold to form a third set;

[0155] Get the number of elements in the third set, recorded as the first number;

[0156] If the first number is greater than or equal to the number threshold, a second level alarm is issued to the target person;

[0157] If the first number is less than the number threshold, no processing is performed;

[0158] The method and system for monitoring abnormal behavior trajectories based on big data first obtain the geometric center of a dangerous activity area, obtain the maximum distance from the geometric center of the dangerous activity area on the boundary of the dangerous activity area, and draw a circle with the geometric center as the center and the maximum distance as the radius to form a circular domain. The area within the circular domain is recorded as a third activity area. The third activity area includes the dangerous activity area and the area near the dangerous activity area. If a target person enters the third activity area, the moment when the target person enters the third activity area is obtained and recorded as the first moment, and the moment 24 hours before the first moment is recorded as the second moment; between the first moment and the second moment, the travel records of the target person whose stay time in the third activity area is greater than or equal to the duration are screened, and the number is obtained. If the number is greater than the number threshold, it means that the target person frequently approaches the dangerous activity area and has a certain risk, so a second-level alarm is issued to the target person at this time. In this way, the abnormal behavior of the target person in the edge area of ​​the dangerous activity area can be captured, thereby improving the comprehensiveness and accuracy of monitoring.

[0159] The step of determining whether to issue a third-level alarm to the target person according to the third-level alarm strategy based on the historical travel records of the target person specifically includes:

[0160] Obtain all destinations in the target person's historical travel records;

[0161] For each destination, obtain the number of times the target person arrives at this destination, and record it as the number of first times;

[0162] Get the number of trips in the target person's historical travel records, and record it as the second number;

[0163] Calculate the first number ÷ the second number, and use the result as the auxiliary ratio;

[0164] Set the ratio threshold;

[0165] The ratio threshold is used to characterize the frequency of the target person arriving at the destination;

[0166] Get the destination of the target person at this time, and record it as the target destination;

[0167] If the target destination is different from all destinations in the target person's historical travel records, a third-level alert will be issued to the target person;

[0168] If the target destination is the same as one of the destinations in the target person's historical travel records;

[0169] Obtain the auxiliary ratio of the destinations that are the same as the target destination in the historical travel records of the target person;

[0170] If the auxiliary ratio is greater than the ratio threshold, no processing is performed;

[0171] If the auxiliary ratio is less than or equal to the ratio threshold, a third-level alarm is issued to the target person;

[0172] This method and system for monitoring abnormal behavior trajectories based on big data obtains each destination in the travel record of a target person, and for each destination, calculates the ratio of the number of times it appears to the total number of destinations in the travel record of the target person as an auxiliary ratio; obtains the destination of the target person's current trip and records it as the target destination. If the auxiliary ratio of the destination in the travel record that is the same as the target destination is less than or equal to a ratio threshold, it means that the location arrived at by the target person this time is an unfamiliar location, so a third-level alarm is issued to the target person. In this way, the target person can be prevented from entering an unfamiliar area, thereby ensuring the safety of the target person.

[0173] The step of determining whether to issue a fourth level alarm to the target person according to the fourth level alarm strategy based on the consumption record of the target person specifically includes:

[0174] Obtain the consumption records of the target person;

[0175] The consumption record includes the amount of expenditure, the time of expenditure and the location of expenditure;

[0176] Get the target person's expenditure position and record it as the expenditure position;

[0177] If the expenditure location is different from each destination in the target person's historical travel records;

[0178] Setting ancillary spending thresholds;

[0179] The auxiliary expenditure threshold is used to assist in determining whether to issue a fourth-level alert to the target person;

[0180] Get the target person's expenditure amount;

[0181] If the target person's expenditure amount is greater than or equal to the auxiliary expenditure threshold, a fourth-level alarm will be issued to the target person.

[0182] The step of determining whether to issue a fourth level alarm to the target person according to the fourth level alarm strategy based on the consumption record of the target person also includes:

[0183] Set the spending frequency threshold, spending interval threshold and spending amount threshold;

[0184] The expenditure number threshold, expenditure interval threshold and expenditure amount threshold are used to assist in determining whether to issue a fourth-level alert to the target person;

[0185] Get the time when the target person makes the expenditure, and record it as the expenditure time;

[0186] The time before the expenditure time and separated from the expenditure time by the expenditure interval time threshold is recorded as the end time;

[0187] The time period between the expenditure time and the end time is recorded as the expenditure time period;

[0188] Obtain the consumption records of the target person in the expenditure time period to form an expenditure set;

[0189] Obtain consumption records in the expenditure set whose expenditure amount is less than or equal to the expenditure amount threshold to form a small expenditure set;

[0190] Get the number of elements in the small expenditure set and record it as the number of expenditures;

[0191] If the number of expenditures is less than the expenditure threshold, no processing will be performed;

[0192] If the number of expenditures is greater than or equal to the expenditure threshold, a fourth-level alert is issued to the target person;

[0193] The method and system for monitoring abnormal behavior trajectory based on big data improve the acquisition of consumption records and travel records of target persons; if the target person makes expenditures at a location that has never been visited, the following steps are performed: first, an auxiliary expenditure threshold is set to obtain the expenditure amount of the target person; if the expenditure amount of the target person is greater than or equal to the auxiliary expenditure threshold, it means that the target person makes large expenditures at an unfamiliar location, which is marked as abnormal behavior, and a fourth-level alarm is issued to the target person; secondly, an expenditure number threshold, an expenditure interval time threshold, and an expenditure amount threshold are set to obtain the moment when the target person makes expenditures, which is recorded as the expenditure moment; the moment before the expenditure moment that is separated by the expenditure interval time threshold is obtained, which is recorded as the end moment; between the end moment and the expenditure moment, consumption records with expenditure amounts less than or equal to the expenditure amount threshold are screened out in the user consumption records, and the number is counted; if the number is greater than or equal to the expenditure number threshold, it means that the user makes frequent small transactions at an unfamiliar location, which is marked as abnormal behavior, and a fourth-level alarm is issued to the target person. Through the above method, the property safety of the target person can be guaranteed. This method expands the monitoring scope of the system so that it is not limited to geographical location but also includes economic behavior.

[0194] Also includes:

[0195] Through the second activity area minimum mileage threshold input port, manually input the second activity area minimum mileage threshold used to assist in determining whether to issue a fifth-level alarm to the target person;

[0196] Through the second activity area maximum mileage threshold input port, manually input the second activity area maximum mileage threshold used to assist in determining whether to issue a fifth-level alarm to the target person;

[0197] The minimum mileage threshold of the second activity area is less than the maximum mileage threshold of the second activity area;

[0198] Through the mileage ratio threshold input port, manually input the mileage ratio threshold used to assist in determining whether to issue a fifth-level alarm to the target person;

[0199] The total travel mileage of the target person in the first activity area is obtained through the data acquisition module, which is recorded as the first mileage, and the total travel mileage of the target person in the second activity area is obtained, which is recorded as the second mileage;

[0200] By comparing the second mileage with the minimum mileage threshold of the second activity area and the maximum mileage threshold of the second activity area;

[0201] When the second mileage is ≤ the minimum mileage threshold of the second activity area, no processing is performed;

[0202] When the second mileage is greater than the maximum mileage threshold of the second activity area, a fifth-level warning is issued to the target person;

[0203] When the minimum mileage threshold of the second activity area ≤ the second mileage ≤ the maximum mileage threshold of the second activity area;

[0204] Compare the second mileage ÷ the first mileage with the mileage ratio threshold by a comparison module;

[0205] If the second mileage ÷ the first mileage < the mileage ratio threshold, no processing will be done;

[0206] If the second mileage ÷ the first mileage ≥ the mileage ratio threshold, a fifth-level alarm is issued to the target person;

[0207] The method and system for monitoring abnormal behavior trajectory based on big data artificially set the minimum mileage threshold of the second activity area, the maximum mileage threshold of the second activity area and the mileage ratio threshold, obtain the total travel mileage of the target person in the first activity area through the data acquisition module, record it as the first mileage, and obtain the total travel mileage of the target person in the second activity area, record it as the second mileage; when the second mileage is ≤ the minimum mileage threshold of the second activity area, it means that the travel mileage of the target person in the second acquisition area is very small, and no alarm is required; when the second mileage is greater than the maximum mileage threshold of the second activity area, it means that the travel mileage of the target person in the second acquisition area is very large, and it exceeds the limit at this time, so the fifth level alarm is issued to the target person; when the minimum mileage threshold of the second activity area is ≤ the second mileage ≤ the maximum mileage threshold of the second activity area, and the second mileage ÷ the first mileage is less than the mileage ratio threshold, it means that although the target person travels in the second activity area, the proportion of travel in the second activity area is very small, and no alarm is required; when the minimum mileage threshold of the second activity area is ≤ the second mileage ≤ the maximum mileage threshold of the second activity area, and the second mileage ÷ the first mileage is greater than the mileage ratio threshold, it means that the travel proportion of the target person in the second activity area is very large, and it exceeds the limit, so the fifth level alarm is issued to the target person.

[0208] include:

[0209] Information acquisition module, used to obtain the historical travel records and consumption records of the target person;

[0210] The second activity area minimum mileage threshold input port is used to input the second activity area minimum mileage threshold;

[0211] A second activity area maximum mileage threshold input port, used to input the second activity area maximum mileage threshold;

[0212] Mileage ratio threshold input port, used to input mileage ratio threshold;

[0213] A data acquisition module, in which a user acquires the total travel mileage of a target person in a first activity area and acquires the total travel mileage of a target person in a second activity area;

[0214] A comparison module, used for comparing the second mileage with a minimum mileage threshold of the second activity area and a maximum mileage threshold of the second activity area;

[0215] This method and system for monitoring abnormal behavior trajectories based on big data, by setting different levels of alarm strategies, the system can flexibly respond to various abnormal behaviors according to different risk levels and processing methods, and improve the accuracy and effectiveness of monitoring; first, the multi-level alarm strategy can provide more detailed risk management. The system sets different alarm levels according to the behavior and location of the target person. For example, the first level alarm is used to respond to the situation where the target person enters a dangerous area. This is the most direct risk warning to ensure that measures are taken immediately to prevent the occurrence of potential security incidents; in addition, those potential high-risk behaviors are identified by combining historical travel records and consumption records. This hierarchical management method enables the system to take corresponding processing measures according to different risk levels to avoid excessive alarms and missed reports; secondly, the multi-level alarm strategy can also combine different data sources to provide a comprehensive risk assessment. For example, the system can combine real-time location data, historical travel records, and consumption records for a multi-dimensional comprehensive analysis to identify more complex and hidden abnormal behavior patterns. For example, if a target person spends a large amount of money at a place that he or she rarely visits, the system can use this comprehensive analysis to determine whether his or her behavior is abnormal and issue a corresponding high-level alarm. In short, the multi-level alarm strategy significantly improves the flexibility and intelligence of the abnormal behavior trajectory monitoring method based on big data. By setting alarm strategies of different levels, the system can flexibly respond to various abnormal behaviors according to different risk levels and processing methods, thereby improving the accuracy and effectiveness of monitoring. This flexible alarm strategy not only improves the practicality and friendliness of the system, but also provides a basis for future expansion and customization of monitoring strategies.

[0216] 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.

[0217] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for monitoring abnormal behavior trajectories based on big data, characterized in that: include: Obtain the personnel whose abnormal behavior trajectory needs to be monitored and record them as target personnel; Delimiting a first activity area, wherein the first activity area refers to an activity area where the target person is allowed to move freely; Delimiting a second activity area, wherein the second activity area refers to an activity area where the target person is not allowed to travel freely; Delineate dangerous activity areas, where the dangerous activity areas refer to activity areas where target persons are prohibited from entering; Determine whether to issue a first-level alarm to the target person according to the first-level alarm strategy based on the target person's location; Determine whether to issue a second-level alarm to the target person according to the second-level alarm strategy based on the target person's location; Obtain the historical travel records of the target person; The historical travel records include the number of trips, the destination of each trip, the time of arrival at the destination, and the length of stay at each destination; Determine whether to issue a third-level alert to the target person according to the third-level alert strategy based on the target person's historical travel records; Determine whether to issue a fourth-level alarm to the target person based on the target person's consumption record and the fourth-level alarm strategy.

2. The method for monitoring abnormal behavior trajectories based on big data according to claim 1, characterized in that: The step of determining whether to issue a first-level alarm to the target person according to the first-level alarm strategy based on the location of the target person specifically includes: Get the real-time location of the target person and record it as the first location; Determining whether the dangerous activity area includes the first location; If the dangerous activity area includes the first location, a first-level alarm is issued to the target personnel; If the hazardous activity area does not include the first position, no processing is performed.

3. The method for monitoring abnormal behavior trajectories based on big data according to claim 1, characterized in that: The step of determining whether to issue a second level alarm to the target person according to the second level alarm strategy based on the location of the target person specifically includes: Get the hazardous activity area; Get the geometric center of the dangerous activity area and record it as the auxiliary point; Obtain the regional boundary of the dangerous activity area, recorded as the auxiliary boundary; Get the distance between each position on the auxiliary boundary and the auxiliary point, and select the distance with the largest length, which is recorded as the auxiliary radius; Setting an auxiliary distance threshold; the auxiliary distance threshold is used to demarcate a third activity area based on the dangerous activity area; A circle is drawn with the auxiliary point as the center and the sum of the auxiliary radius and the auxiliary distance threshold as the radius to form a circular domain. The area enclosed by the circular domain is recorded as the third active area.

4. The method for monitoring abnormal behavior trajectories based on big data according to claim 3 is characterized in that: The step of determining whether to issue a second level alarm to the target person according to the second level alarm strategy based on the location of the target person also includes: Obtain the real-time location of the target person and record it as the second location; determining whether the third activity area includes the second position; If the third active area does not include the second position, no processing is performed; If the third active area includes the second position; Obtain the time when the target person enters the third activity area, which is recorded as the first time; The time before the first moment and 24 hours apart from the first moment is recorded as the second moment; The time period between the first moment and the second moment is recorded as the target time period; A number threshold and a duration threshold are set, and the number threshold and the duration threshold are used to assist in determining whether to issue a second-level alarm to the target person.

5. The method for monitoring abnormal behavior trajectory based on big data according to claim 4 is characterized in that: The step of determining whether to issue a second level alarm to the target person according to the second level alarm strategy based on the position of the target person also includes: Obtain the historical travel records of the target person; The travel records of the target person whose destination is located in the third activity area in the historical travel records constitute the first set; For each travel record in the first set, obtain the time of arrival at the destination; Acquire travel records whose arrival time at the destination is within the target time period to form a second set; For each travel record in the second set, obtain the stay time at the destination; Acquire travel records whose stay time at the destination is greater than or equal to the duration threshold to form a third set; Get the number of elements in the third set, recorded as the first number; If the first number is greater than or equal to the number threshold, a second level alarm is issued to the target person; If the first number is less than the number threshold, no processing is performed.

6. The method for monitoring abnormal behavior trajectories based on big data according to claim 1, characterized in that: The step of determining whether to issue a third-level alarm to the target person according to the third-level alarm strategy based on the historical travel records of the target person specifically includes: Obtain all destinations in the target person's historical travel records; For each destination, obtain the number of times the target person arrives at this destination, and record it as the number of first times; Get the number of trips in the target person's historical travel records, and record it as the second number; Calculate the first number ÷ the second number, and use the result as the auxiliary ratio; Set the ratio threshold; The ratio threshold is used to characterize the frequency of the target person arriving at the destination; Get the destination of the target person at this time, and record it as the target destination; If the target destination is different from all destinations in the target person's historical travel records, a third-level alert will be issued to the target person; If the target destination is the same as one of the destinations in the target person's historical travel records; Obtain the auxiliary ratio of the destinations that are the same as the target destination in the historical travel records of the target person; If the auxiliary ratio is greater than the ratio threshold, no processing is performed; If the auxiliary ratio is less than or equal to the ratio threshold, a third-level alarm is issued to the target person.

7. The method for monitoring abnormal behavior trajectories based on big data according to claim 1, characterized in that: The step of determining whether to issue a fourth level alarm to the target person according to the fourth level alarm strategy based on the consumption record of the target person specifically includes: Obtain the consumption records of the target person; The consumption record includes the amount of expenditure, the time of expenditure and the location of expenditure; Get the target person's expenditure position and record it as the expenditure position; If the expenditure location is different from each destination in the target person's historical travel records; Setting ancillary spending thresholds; The auxiliary expenditure threshold is used to assist in determining whether to issue a fourth-level alert to the target person; Get the target person's expenditure amount; If the target person's expenditure amount is greater than or equal to the auxiliary expenditure threshold, a fourth-level alarm will be issued to the target person.

8. The method for monitoring abnormal behavior trajectory based on big data according to claim 7 is characterized in that: The step of determining whether to issue a fourth level alarm to the target person according to the fourth level alarm strategy based on the consumption record of the target person also includes: Set the spending frequency threshold, spending interval threshold and spending amount threshold; The expenditure number threshold, expenditure interval threshold and expenditure amount threshold are used to assist in determining whether to issue a fourth-level alert to the target person; Get the time when the target person makes the expenditure, and record it as the expenditure time; The time before the expenditure time and separated from the expenditure time by the expenditure interval time threshold is recorded as the end time; The time period between the expenditure time and the end time is recorded as the expenditure time period; Obtain the consumption records of the target person in the expenditure time period to form an expenditure set; Obtain consumption records in the expenditure set whose expenditure amount is less than or equal to the expenditure amount threshold to form a small expenditure set; Get the number of elements in the small expenditure set and record it as the number of expenditures; If the number of expenditures is less than the expenditure threshold, no processing will be performed; If the number of expenditures is greater than or equal to the expenditure threshold, a fourth-level alarm will be issued to the target person.

9. The method for monitoring abnormal behavior trajectory based on big data according to claim 1, characterized in that: Also includes: Through the second activity area minimum mileage threshold input port, manually input the second activity area minimum mileage threshold used to assist in determining whether to issue a fifth-level alarm to the target person; Through the second activity area maximum mileage threshold input port, manually input the second activity area maximum mileage threshold used to assist in determining whether to issue a fifth-level alarm to the target person; The minimum mileage threshold of the second activity area is less than the maximum mileage threshold of the second activity area; Through the mileage ratio threshold input port, manually input the mileage ratio threshold used to assist in determining whether to issue a fifth-level alarm to the target person; The total travel mileage of the target person in the first activity area is obtained through the data acquisition module, which is recorded as the first mileage, and the total travel mileage of the target person in the second activity area is obtained, which is recorded as the second mileage; By comparing the second mileage with the minimum mileage threshold of the second activity area and the maximum mileage threshold of the second activity area; When the second mileage is ≤ the minimum mileage threshold of the second activity area, no processing is performed; When the second mileage is greater than the maximum mileage threshold of the second activity area, a fifth-level warning is issued to the target person; When the minimum mileage threshold of the second activity area ≤ the second mileage ≤ the maximum mileage threshold of the second activity area; Compare the second mileage ÷ the first mileage with the mileage ratio threshold by a comparison module; If the second mileage ÷ the first mileage < the mileage ratio threshold, no processing will be done; If the second mileage ÷ the first mileage ≥ the mileage ratio threshold, a fifth level alarm is issued to the target person.

10. A system for implementing the abnormal behavior trajectory monitoring method based on big data as claimed in claim 1, characterized in that: include: Information acquisition module, used to obtain the historical travel records and consumption records of the target person; The second activity area minimum mileage threshold input port is used to input the second activity area minimum mileage threshold; A second activity area maximum mileage threshold input port, used to input the second activity area maximum mileage threshold; Mileage ratio threshold input port, used to input mileage ratio threshold; A data acquisition module, in which a user acquires the total travel mileage of a target person in a first activity area and acquires the total travel mileage of a target person in a second activity area; The comparison module is used to compare the second mileage with the minimum mileage threshold of the second activity area and the maximum mileage threshold of the second activity area.

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