Intelligent prevention and control method and device for an event

Abstract

The application discloses an intelligent prevention and control method and device for an event, and the method comprises the following steps: collecting the space-time data of users corresponding to a target area, and determining the space-time information corresponding to the users according to the space-time data and information space-time processing conditions; determining the distribution characteristic information of the target area according to the space-time information and distribution characteristic processing conditions; and determining the event prediction result corresponding to the target area according to the distribution characteristic information and the geographic information corresponding to the target area, wherein the event prediction result is used to represent the event prediction probability corresponding to the target area. It can be seen that the application can improve the comprehensiveness, rationality and scientificity of the intelligent prevention and control method for the event, improve the prediction accuracy and reliability of the event, and further improve the prevention and control accuracy and reliability of the event. In addition, the application also improves the prediction efficiency and convenience of the event, and further improves the prevention and control efficiency, convenience and timeliness of the event.

Description

Technical Field

[0001] This invention relates to the field of intelligent prevention and control technology, and in particular to an intelligent prevention and control method and device for events. Background Technology

[0002] Looking at the situation both domestically and internationally, such incidents can have a significant impact on the stability and security of cities and cause great losses to people's lives and property. As cities place increasingly higher demands on refined management, it is necessary to implement regional modular management and prevention of urban incidents in order to minimize the impact on the normal operation and construction of cities.

[0003] Currently, incident prevention and control primarily relies on human monitoring and collection of warning signs that may be related to the incident. This warning information is then subjectively analyzed to predict the event, and prevention and control measures are determined accordingly. However, human intervention is easily influenced by factors such as mental state, environmental atmosphere, and subjective consciousness. Therefore, existing incident prevention and control methods suffer from low accuracy and reliability. Consequently, providing a new incident prevention and control method to improve accuracy and reliability is of paramount importance. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent prevention and control method and device for events, which can improve the accuracy and reliability of prevention and control.

[0005] To address the aforementioned technical problems, the first aspect of this invention discloses an intelligent event prevention and control method, the method comprising:

[0006] Collect spatiotemporal data of users corresponding to the target area, and determine the spatiotemporal information corresponding to the user based on the spatiotemporal data and the set spatiotemporal information processing conditions;

[0007] Based on the spatiotemporal information and the set distribution feature processing conditions, the distribution feature information corresponding to the target region is determined;

[0008] Based on the distribution feature information and the geographical information corresponding to the target area, the event prediction result corresponding to the target area is determined, and the event prediction result is used to represent the probability of the event corresponding to the target area occurring.

[0009] As an optional implementation, in the first aspect of the present invention, determining the distribution feature information corresponding to the target region based on the spatiotemporal information and the set distribution feature processing conditions includes:

[0010] Based on the spatiotemporal information and the set information dimension analysis conditions, the information dimension situation corresponding to the spatiotemporal information is determined;

[0011] Based on the information dimensions, a target distribution feature processing model that matches the spatiotemporal information is selected from the pre-constructed set of distribution feature processing models;

[0012] Based on the spatiotemporal information and the target distribution feature processing model, the corresponding distribution feature processing result is determined and used as the distribution feature information corresponding to the target region.

[0013] As an optional implementation, in the first aspect of the present invention, determining the spatiotemporal information corresponding to the user based on the spatiotemporal data and the set information spatiotemporal processing conditions includes:

[0014] Based on the spatiotemporal data and the set basic information processing conditions, the basic data is determined, and based on the obtained user intersection information and the set user type classification conditions, the user type is determined.

[0015] Based on the basic data, the user type information, and the parameter weighting processing conditions corresponding to the user type information, the spatiotemporal trajectory information is determined.

[0016] Based on the spatiotemporal trajectory information and the key location information of the target area obtained, the spatiotemporal information corresponding to the user is determined.

[0017] As an optional implementation, in the first aspect of the present invention, before determining the distribution feature information corresponding to the target region based on the spatiotemporal information and the set distribution feature processing conditions, the method further includes:

[0018] Determine whether the spatiotemporal information meets the preset quality verification conditions;

[0019] When the judgment result is yes, the operation of determining the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions is executed;

[0020] When the judgment result is negative, the information that needs to be adjusted is selected based on the spatiotemporal information and the quality verification conditions; the information that needs to be adjusted is subjected to quality adjustment operation according to the set information quality adjustment conditions to obtain the spatiotemporal information after quality adjustment; and based on the spatiotemporal information after quality adjustment, the operation of determining the distribution feature information corresponding to the target area according to the spatiotemporal information and the set distribution feature processing conditions is executed.

[0021] As an optional implementation, in the first aspect of the present invention, after determining the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area, the method further includes:

[0022] The real-time event occurrence status corresponding to the target area is obtained, and the event prediction results and the real-time event occurrence status are analyzed to obtain the prediction accuracy.

[0023] Determine whether the prediction accuracy is greater than or equal to a preset prediction accuracy threshold;

[0024] When it is determined that the prediction accuracy is greater than or equal to the prediction accuracy threshold, the event prediction result is determined to be accurate and effective.

[0025] When it is determined that the prediction accuracy is less than the prediction accuracy threshold, prediction adjustment parameters are determined based on the event prediction result, the real-time event occurrence, and the prediction accuracy. The prediction adjustment parameters are used to adjust the intelligent prevention and control operation to improve the prediction accuracy of the event.

[0026] As an optional implementation, in the first aspect of the present invention, determining whether the spatiotemporal information meets a preset quality verification condition includes:

[0027] Analyze the spatiotemporal information and the preset information quality requirements to obtain the quality compliance. The information quality requirements include one or more of the following: information format requirements, information type attribute requirements, and information content requirements.

[0028] Determine whether the quality compliance degree is greater than or equal to a preset quality compliance degree threshold;

[0029] When it is determined that the quality compliance degree is greater than or equal to the quality compliance degree threshold, it is determined that the spatiotemporal information meets the preset quality verification conditions.

[0030] When it is determined that the quality compliance degree is less than the quality compliance degree threshold, it is determined that the spatiotemporal information does not meet the quality verification conditions.

[0031] As an optional implementation, in the first aspect of the present invention, after determining the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area, the method further includes:

[0032] Based on the event prediction results, predict the event occurrence trend corresponding to the target area. The event occurrence trend includes the event occurrence trend corresponding to the event prediction results and / or the occurrence trend of related events corresponding to the event.

[0033] Analyze the trend of the events to determine the urgency of prevention and control in the target area;

[0034] Determine whether the urgency level of the prevention and control measures is greater than or equal to a preset urgency level threshold.

[0035] When it is determined that the urgency of the prevention and control is greater than or equal to the urgency threshold, the event prevention and control information corresponding to the target area is determined based on the event occurrence trend, the urgency of the prevention and control, and the set event prevention and control conditions.

[0036] A second aspect of the present invention discloses an intelligent event prevention and control device, the device comprising:

[0037] The acquisition module is used to collect spatiotemporal data of users corresponding to the target area;

[0038] The determination module is used to determine the spatiotemporal information corresponding to the user based on the spatiotemporal data and the set spatiotemporal processing conditions; and to determine the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions.

[0039] The event prediction module determines the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area. The event prediction result is used to represent the probability of the event occurring in the target area.

[0040] As an optional implementation, in a second aspect of the present invention, the method by which the determining module determines the distribution feature information corresponding to the target region based on the spatiotemporal information and the set distribution feature processing conditions specifically includes:

[0041] Based on the spatiotemporal information and the set information dimension analysis conditions, the information dimension situation corresponding to the spatiotemporal information is determined;

[0042] Based on the information dimensions, a target distribution feature processing model that matches the spatiotemporal information is selected from the pre-constructed set of distribution feature processing models;

[0043] Based on the spatiotemporal information and the target distribution feature processing model, the corresponding distribution feature processing result is determined and used as the distribution feature information corresponding to the target region.

[0044] As an optional implementation, in a second aspect of the present invention, the method by which the determining module determines the spatiotemporal information corresponding to the user based on the spatiotemporal data and the set information spatiotemporal processing conditions specifically includes:

[0045] Based on the spatiotemporal data and the set basic information processing conditions, the basic data is determined, and based on the obtained user intersection information and the set user type classification conditions, the user type is determined.

[0046] Based on the basic data, the user type information, and the parameter weighting processing conditions corresponding to the user type information, the spatiotemporal trajectory information is determined.

[0047] Based on the spatiotemporal trajectory information and the key location information of the target area obtained, the spatiotemporal information corresponding to the user is determined.

[0048] As an optional implementation, in a second aspect of the invention, the apparatus further includes:

[0049] The judgment module is used to determine whether the spatiotemporal information meets the preset quality verification conditions before the determining module determines the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions. When the judgment result is yes, the determining module is triggered to perform the operation of determining the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions.

[0050] The adjustment module is used to, when the judgment module determines that the spatiotemporal information does not meet the quality verification conditions, filter out the information that needs to be adjusted based on the spatiotemporal information and the quality verification conditions; perform quality adjustment operations on the information that needs to be adjusted according to the set information quality adjustment conditions to obtain the quality-adjusted spatiotemporal information; and, based on the quality-adjusted spatiotemporal information, trigger the determination module to perform the operation of determining the distribution feature information corresponding to the target region based on the spatiotemporal information and the set distribution feature processing conditions.

[0051] As an optional implementation, in a second aspect of the present invention, the acquisition module is further configured to acquire the real-time event occurrence status of the target area after the determining module determines the event prediction result corresponding to the target area based on the distribution feature information and the geographic information corresponding to the target area;

[0052] The device further includes:

[0053] The analysis module is used to analyze the event prediction results and the occurrence of the real-time event to obtain the prediction accuracy.

[0054] The judgment module is also used to determine whether the prediction accuracy is greater than or equal to a preset prediction accuracy threshold.

[0055] The determining module is further configured to determine that the event prediction result is accurate and valid when the judging module determines that the prediction accuracy is greater than or equal to the prediction accuracy threshold.

[0056] The determining module is further configured to determine a prediction adjustment parameter based on the event prediction result, the real-time event occurrence, and the prediction accuracy when the judgment module determines that the prediction accuracy is less than the prediction accuracy threshold. The prediction adjustment parameter is used to adjust the intelligent prevention and control operation to improve the prediction accuracy of the event.

[0057] As an optional implementation, in the second aspect of the present invention, the method by which the judging module judges whether the spatiotemporal information meets the preset quality verification conditions specifically includes:

[0058] Analyze the spatiotemporal information and the preset information quality requirements to obtain the quality compliance. The information quality requirements include one or more of the following: information format requirements, information type attribute requirements, and information content requirements.

[0059] Determine whether the quality compliance degree is greater than or equal to a preset quality compliance degree threshold;

[0060] When it is determined that the quality compliance degree is greater than or equal to the quality compliance degree threshold, it is determined that the spatiotemporal information meets the preset quality verification conditions.

[0061] When it is determined that the quality compliance degree is less than the quality compliance degree threshold, it is determined that the spatiotemporal information does not meet the quality verification conditions.

[0062] As an optional implementation, in a second aspect of the present invention, the event prediction module is further configured to, after determining the event prediction result corresponding to the target area based on the distribution feature information and the geographic information corresponding to the target area, predict the event occurrence trend corresponding to the target area based on the event prediction result, wherein the event occurrence trend includes the occurrence trend of the event corresponding to the event prediction result and / or the occurrence trend of related events corresponding to the event.

[0063] The analysis module is also used to analyze the trend of the event and obtain the urgency of prevention and control in the target area;

[0064] The judgment module is also used to determine whether the prevention and control urgency is greater than or equal to a preset prevention and control urgency threshold.

[0065] The determining module is further configured to, when the judging module determines that the urgency of the prevention and control is greater than or equal to the urgency threshold, determine the event prevention and control information corresponding to the target area based on the event occurrence trend, the urgency of the prevention and control, and the set event prevention and control conditions.

[0066] A third aspect of the present invention discloses another intelligent event prevention and control device, the device comprising:

[0067] Memory containing executable program code;

[0068] A processor coupled to the memory;

[0069] The processor calls the executable program code stored in the memory to execute an intelligent event prevention and control method disclosed in the first aspect of the present invention.

[0070] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute an intelligent event prevention and control method disclosed in the first aspect of the present invention.

[0071] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0072] In this embodiment of the invention, spatiotemporal data of users corresponding to a target area are collected, and spatiotemporal information corresponding to the user is determined based on the spatiotemporal data and set spatiotemporal processing conditions. Distribution characteristic information corresponding to the target area is determined based on the spatiotemporal information and set distribution characteristic processing conditions. Event prediction results corresponding to the target area are determined based on the distribution characteristic information and the geographical information corresponding to the target area. These event prediction results represent the predicted probability of an event occurring in the target area. Therefore, this invention can spatiotemporally process personnel information and, combined with the determined personnel distribution characteristic information of the area, predict the probability of events occurring in the area, thereby achieving intelligent event prevention and control. This improves the comprehensiveness, rationality, and scientific nature of intelligent event prevention and control methods, and is conducive to improving the accuracy and reliability of event prediction, thus improving the precision and reliability of event prevention and control. Furthermore, it also helps to improve the efficiency and convenience of event prediction, thereby improving the efficiency, convenience, and timeliness of event prevention and control. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a flowchart illustrating an intelligent event prevention and control method disclosed in an embodiment of the present invention;

[0075] Figure 2 This is a flowchart illustrating another intelligent event prevention and control method disclosed in an embodiment of the present invention;

[0076] Figure 3This is a schematic diagram of the structure of an intelligent event prevention and control device disclosed in an embodiment of the present invention;

[0077] Figure 4 This is a schematic diagram of the structure of another intelligent event prevention and control device disclosed in an embodiment of the present invention;

[0078] Figure 5 This is a schematic diagram of the structure of another intelligent event prevention and control device disclosed in an embodiment of the present invention;

[0079] Figure 6 This is a schematic diagram of event prevention and control distribution prediction for an intelligent event prevention and control method disclosed in an embodiment of the present invention;

[0080] Figure 7 This is a schematic diagram of the application module of an intelligent event prevention and control method disclosed in an embodiment of the present invention. Detailed Implementation

[0081] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0082] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0083] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0084] This invention discloses an intelligent event prevention and control method and device. It can spatially and temporally process personnel information and, combined with determined regional personnel distribution characteristics, predict the probability of events occurring in a region. This achieves intelligent event prevention and control, improving the comprehensiveness, rationality, and scientific nature of intelligent event prevention and control methods. It also enhances the accuracy and reliability of event prediction, thereby improving the precision and reliability of event prevention and control. Furthermore, it improves the efficiency and convenience of event prediction, further enhancing the efficiency, convenience, and timeliness of event prevention and control. Detailed descriptions follow.

[0085] Example 1

[0086] Please see Figure 1 , Figure 1 This is a flowchart illustrating an intelligent event prevention and control method disclosed in an embodiment of the present invention. Wherein, Figure 1 The described method can be applied to an intelligent event prevention and control device, wherein the device may include a server, which may be a local server or a cloud server, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, this intelligent prevention and control method for such events includes the following operations:

[0087] 101. Collect spatiotemporal data of users corresponding to the target area.

[0088] Optionally, the spatiotemporal data can be location check-in information or other information that reflects the user's geographic location activity time-series trajectory information. This embodiment of the invention does not limit the scope of the data. Further, for example, the geographic location activity time-series trajectory information could be Zhang San's location check-in information as follows: Zhang San, 20211207, 09:30:25, **Company, **Building, **Zhongke Road, Pudong New Area (County), Shanghai, corresponding POI: (120.209524, 36.305702). Further examples are not provided here.

[0089] Optionally, the collected spatiotemporal data is input into the data storage module; further, the data storage module builds a data platform and completes data access, storage and ETL processing and conversion, and stores the processed data in the data storage repository with reference to the following weight and penalty coefficient processing methods. This embodiment of the invention does not limit the scope of the invention.

[0090] Optionally, regional hotspot information data of the target area can be determined based on the population distribution data of the target area, and the regional hotspot information data can also be input into the data storage module for data processing. This embodiment of the invention does not limit the scope of the invention.

[0091] 102. Based on the spatiotemporal data and the set spatiotemporal processing conditions, determine the spatiotemporal information corresponding to the user.

[0092] 103. Based on the spatiotemporal information and the set distribution feature processing conditions, determine the distribution feature information corresponding to the target area.

[0093] 104. Based on the distribution characteristics and the geographical information corresponding to the target area, determine the event prediction results corresponding to the target area. The event prediction results are used to represent the probability of the event occurring in the target area.

[0094] Optionally, the event prediction results can be displayed using a prevention and control distribution prediction map. (The event prevention and control distribution prediction map can be referenced.) Figure 6 As shown, the embodiments of the present invention are not limited. Specifically, the darker the color, the greater the probability of an event occurring in that location, and the greater the need for prevention and control.

[0095] Optionally, the intelligent event prevention and control method described in this embodiment of the invention can be embodied in an intelligent event prevention and control platform. This platform may include data acquisition and access devices, a data storage and analysis platform, a prevention and control management service platform, and a visualization display (large screen) device. Further, the data acquisition and access devices may include a data acquisition module and a data input module. The data input module may include collected spatiotemporal data, geographic information data, etc. The data storage and analysis platform may include a data storage module, a data processing module, a model management and service system, a feedback learning module, etc. The prevention and control management service platform mainly includes a model learning result display module and a prevention and control suggestion module. The data storage module includes storage elements such as a big data platform and a data warehouse. The application module process can be referenced. Figure 7 As shown, the embodiments of the present invention are not limited.

[0096] As can be seen, the intelligent event prevention and control method described in the embodiments of the present invention can spatialize personnel information and predict the probability of event occurrence in a region by combining the determined personnel distribution characteristics of the region, so as to realize intelligent event prevention and control, improve the comprehensiveness, rationality and scientific nature of intelligent event prevention and control methods, and help improve the accuracy and reliability of event prediction, thereby improving the precision and reliability of event prevention and control. In addition, it also helps improve the efficiency and convenience of event prediction, thereby improving the efficiency, convenience and timeliness of event prevention and control.

[0097] In an optional embodiment, determining the distribution feature information corresponding to the target region based on spatiotemporal information and set distribution feature processing conditions may include:

[0098] Based on the spatiotemporal information and the set information dimension analysis conditions, determine the information dimension situation corresponding to the spatiotemporal information;

[0099] Based on the information dimension, target distribution feature processing models that match the spatiotemporal information are selected from the pre-built set of distribution feature processing models;

[0100] Based on the spatiotemporal information and the target distribution feature processing model, the corresponding distribution feature processing results are determined and used as the distribution feature information of the target area.

[0101] Optionally, when the spatiotemporal information corresponds to an information dimension that satisfies the first dimension condition, the target distribution feature processing model matching the spatiotemporal information can be:

[0102]

[0103] Where μ is the mean (expected value) of the data, and σ is the standard deviation of the data.

[0104] Optionally, when the spatiotemporal information corresponds to an information dimension that satisfies the second dimension condition, the target distribution feature processing model matching the spatiotemporal information can be:

[0105]

[0106] Where μ is the data mean (expectation), Σ is the covariance, and D is the data dimension.

[0107] As can be seen, this optional embodiment can further determine the distribution feature information by matching the corresponding distribution feature processing model according to the information dimension. This is beneficial to improving the comprehensiveness and rationality of the distribution feature information determination method, as well as the pertinence and flexibility of the distribution feature information determination parameters. In turn, it is beneficial to improve the accuracy and reliability of the determined distribution feature information, thereby improving the accuracy and reliability of the subsequent event prediction results determined based on the distribution feature information.

[0108] In another optional embodiment, the determination of the user's corresponding spatiotemporal information based on spatiotemporal data and set information spatiotemporal processing conditions may include:

[0109] Based on spatiotemporal data and the established basic information processing conditions, the basic data is determined, and based on the obtained user intersection information and the established user type classification conditions, the user type is determined.

[0110] Based on the basic data, user type information, and the parameter weighting conditions corresponding to the user type information, the spatiotemporal trajectory information is determined.

[0111] Based on the spatiotemporal trajectory information and the key location information of the target area, the spatiotemporal information corresponding to the user is determined.

[0112] Optionally, the basic information processing conditions can be expressed based on spatialized information (latitude and longitude coordinates) of time series, which is not limited in this embodiment of the invention.

[0113] Optional parameter weighting processing conditions, for example: When the user type is the first user type, based on the activity location information of the first user type over a certain period of time, the spatiotemporal feature representation of each relevant trajectory information is extracted as the user's spatiotemporal information; when the user type is the second user type, based on the activity location information of the second user type over a certain period of time, the spatiotemporal feature representation of each relevant trajectory information is extracted, and weights and penalty coefficients are added, that is, if the user is in the same location as the first user type user, the weight coefficient is 'a'; if the user is in a different location, the weight coefficient is 'b'; if the user is not in the same location, the penalty coefficient is 'c', thereby determining the user's spatiotemporal information. The spatiotemporal information of the user is determined by extracting spatiotemporal features of relevant trajectory information based on the activity location information of the user of the third user type in the past for a certain period of time. Weights and penalty coefficients are added. That is, if the user is in the same place as the user of the first user type and the user of the second user type, the weight coefficient is d; if the user is in the same place as the user of the first user type but not in the same place as the user of the second user type, the weight coefficient is e; if the user is not in the same place as the user of the first user type but is in the same place as the user of the second user type, the weight coefficient is f; if the user is not in the same place as the user of the first user type and the user of the second user type, the penalty coefficient is g. Thus, the spatiotemporal information of the user is determined. This embodiment of the invention does not limit the scope of the invention.

[0114] It is evident that this optional embodiment can match the corresponding spatiotemporal data processing method according to the user type, which is conducive to improving the comprehensiveness and rationality of the spatiotemporal information determination method, improving the pertinence and flexibility of the spatiotemporal information determination parameters, and thus improving the accuracy and reliability of the determined spatiotemporal information.

[0115] Example 2

[0116] Please see Figure 2 , Figure 2 This is a flowchart illustrating another intelligent event prevention and control method disclosed in an embodiment of the present invention. Wherein, Figure 2 The described method can be applied to an intelligent event prevention and control device, wherein the device may include a server, which may be a local server or a cloud server, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, this intelligent prevention and control method for such events includes the following operations:

[0117] 201. Collect spatiotemporal data of users corresponding to the target area, and determine the spatiotemporal information corresponding to the users based on the spatiotemporal data and the set spatiotemporal processing conditions.

[0118] 202. Determine whether the spatiotemporal information meets the preset quality verification conditions. If the determination result is yes, proceed to step 203; if the determination result is no, proceed to step 204.

[0119] 203. Based on the spatiotemporal information and the set distribution feature processing conditions, determine the distribution feature information corresponding to the target area, and execute step 205.

[0120] 204. Based on the spatiotemporal information and quality verification conditions, filter out the information that needs to be adjusted; according to the set information quality adjustment conditions, perform quality adjustment operations on the information that needs to be adjusted to obtain the spatiotemporal information after quality adjustment; and based on the spatiotemporal information after quality adjustment, perform the operation of determining the distribution feature information corresponding to the target area according to the spatiotemporal information and the set distribution feature processing conditions, and execute step 205.

[0121] 205. Based on the distribution characteristics and the geographic information corresponding to the target area, determine the event prediction results corresponding to the target area. The event prediction results are used to represent the probability of the event occurring in the target area.

[0122] In this embodiment of the invention, for other descriptions of steps 201, 203 and 205, please refer to the other detailed descriptions of steps 101-104 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0123] As can be seen, the embodiments of the present invention can spatialize personnel information and predict the probability of events occurring in a region by combining it with the determined personnel distribution characteristics of the region, so as to realize intelligent event prevention and control, improve the comprehensiveness, rationality and scientific nature of intelligent event prevention and control methods, and improve the accuracy and reliability of event prediction, thereby improving the precision and reliability of event prevention and control. In addition, it can also improve the efficiency and convenience of event prediction, thereby improving the efficiency, convenience and timeliness of event prevention and control. Furthermore, it can also provide information quality verification and information quality adjustment methods, enriching the intelligent functions of an intelligent event prevention and control method, which can improve the comprehensiveness and rationality of an intelligent event prevention and control method. Quality verification and quality adjustment of spatialized information can improve the usability and effectiveness of spatialized information, reduce the occurrence of untimely prevention and control due to non-compliant spatialized information quality, and thus improve the operational stability of event prevention and control based on spatialized information, thereby improving the efficiency and accuracy of event prevention and control.

[0124] In an optional embodiment, after determining the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area, the method may further include the following operations:

[0125] Obtain the real-time event occurrence status corresponding to the target area, and analyze the event prediction results and real-time event occurrence status to obtain the prediction accuracy;

[0126] Determine whether the prediction accuracy is greater than or equal to the preset prediction accuracy threshold;

[0127] When the prediction accuracy is determined to be greater than or equal to the prediction accuracy threshold, the event prediction result is deemed accurate and effective.

[0128] When the prediction accuracy is determined to be less than the prediction accuracy threshold, prediction adjustment parameters are determined based on the event prediction results, real-time event occurrence, and prediction accuracy. These prediction adjustment parameters are used to adjust intelligent prevention and control operations to improve the prediction accuracy of events.

[0129] As can be seen, this optional embodiment can analyze the prediction accuracy of event prediction results and adjust the parameters of intelligent event prevention and control, enriching the intelligent functions of an intelligent event prevention and control method. It is conducive to improving the comprehensiveness and rationality of an intelligent event prevention and control method, improving the timeliness of event prevention and control adjustments, and thus improving the prediction accuracy and reliability of events, thereby improving the accuracy and reliability of event prevention and control.

[0130] In another optional embodiment, the above determination of whether the spatiotemporal information meets the preset quality verification conditions may include:

[0131] Analyze the spatiotemporal information and the preset information quality requirements to obtain the quality compliance. The information quality requirements include one or more of the following: information format requirements, information type attribute requirements, and information content requirements.

[0132] Determine whether the quality compliance rate is greater than or equal to the preset quality compliance rate threshold;

[0133] When it is determined that the quality compliance degree is greater than or equal to the quality compliance degree threshold, the spatiotemporal information is determined to meet the preset quality verification conditions.

[0134] When the quality compliance is determined to be less than the quality compliance threshold, it is determined that the spatiotemporal information does not meet the quality verification conditions.

[0135] As can be seen, this optional embodiment can determine the quality compliance of spatiotemporal information and the satisfaction of the quality verification conditions of spatiotemporal information based on the comparison between the quality compliance and the quality compliance threshold. This is beneficial to improving the rationality and feasibility of the method for determining the satisfaction of quality verification conditions, thereby improving the accuracy and reliability of the determined satisfaction of quality verification conditions, as well as improving the efficiency and convenience of determining the satisfaction of quality verification conditions.

[0136] In yet another optional embodiment, after determining the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area, the method may further include the following operations:

[0137] Based on the event prediction results, predict the event occurrence trend corresponding to the target area. The event occurrence trend includes the occurrence trend of the event corresponding to the event prediction results and / or the occurrence trend of related events corresponding to the event.

[0138] Analyze the trend of events to determine the urgency of prevention and control in the target area;

[0139] Determine whether the urgency level of the prevention and control measures is greater than or equal to the preset urgency level threshold.

[0140] When the urgency level of prevention and control is determined to be greater than or equal to the urgency level threshold, the event prevention and control information corresponding to the target area is determined based on the event occurrence trend, the urgency level of prevention and control, and the set event prevention and control conditions.

[0141] Optionally, the event prevention and control information corresponding to the target area is used to prompt the management department to make prevention and control suggestions for the target area and areas where events may occur in the future, so as to enable the management decision-making department to quickly deploy control measures and quickly block the occurrence of events and / or the spread of their impact.

[0142] Further, optionally, when it is determined that the urgency of prevention and control is less than the urgency threshold, it is determined that no event prevention and control operation needs to be performed on the target area.

[0143] As can be seen, this optional embodiment can provide an intelligent event prevention and control suggestion method. When the urgency of the prevention and control is greater than or equal to the urgency threshold, an event prevention and control suggestion is determined, thereby realizing the intelligent event prevention and control suggestion function. This enriches the intelligent function of an event prevention and control method, which is conducive to improving the comprehensiveness and rationality of an event prevention and control method. In turn, it is conducive to improving the selectivity and efficiency of event prevention and control, and also conducive to reducing unnecessary waste of prevention and control resources.

[0144] Example 3

[0145] Please see Figure 3 , Figure 3This is a schematic diagram of the structure of an intelligent event prevention and control device disclosed in an embodiment of the present invention. Figure 3 The described apparatus may include a server, wherein the server includes a local server or a cloud server, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, this intelligent event prevention and control device may include:

[0146] The acquisition module 301 is used to collect spatiotemporal data of users corresponding to the target area.

[0147] The determination module 302 is used to determine the spatiotemporal information corresponding to the user based on spatiotemporal data and set spatiotemporal processing conditions; and to determine the distribution characteristic information corresponding to the target area based on the spatiotemporal information and set distribution characteristic processing conditions.

[0148] The event prediction module 303 determines the event prediction result for the target area based on the distribution characteristic information and the geographical information corresponding to the target area. The event prediction result is used to represent the probability of the event occurring in the target area.

[0149] It is evident that implementation Figure 3 The described intelligent event prevention and control device can spatialize personnel information and predict the probability of events occurring in a region by combining it with the determined personnel distribution characteristics of the region. This enables intelligent event prevention and control, improves the comprehensiveness, rationality, and scientific nature of intelligent event prevention and control methods, and helps improve the accuracy and reliability of event prediction. In addition, it helps improve the efficiency and convenience of event prediction, thereby improving the efficiency, convenience, and timeliness of event prevention and control.

[0150] In an optional embodiment, the method by which the determining module 302 determines the distribution feature information corresponding to the target region based on the spatiotemporal information and the set distribution feature processing conditions specifically includes:

[0151] Based on the spatiotemporal information and the set information dimension analysis conditions, determine the information dimension situation corresponding to the spatiotemporal information;

[0152] Based on the information dimension, target distribution feature processing models that match the spatiotemporal information are selected from the pre-built set of distribution feature processing models;

[0153] Based on the spatiotemporal information and the target distribution feature processing model, the corresponding distribution feature processing results are determined and used as the distribution feature information of the target area.

[0154] It is evident that implementation Figure 4The described device can match the corresponding distribution feature processing model according to the information dimension to further determine the distribution feature information. This is beneficial to improving the comprehensiveness and rationality of the distribution feature information determination method, as well as the pertinence and flexibility of the determination parameters of the distribution feature information. In turn, it is beneficial to improve the accuracy and reliability of the determined distribution feature information, thereby improving the accuracy and reliability of the subsequent event prediction results determined based on the distribution feature information.

[0155] In another optional embodiment, the method by which the determining module 302 determines the spatiotemporal information corresponding to the user based on spatiotemporal data and set information spatiotemporal processing conditions specifically includes:

[0156] Based on spatiotemporal data and the established basic information processing conditions, the basic data is determined, and based on the obtained user intersection information and the established user type classification conditions, the user type is determined.

[0157] Based on the basic data, user type information, and the parameter weighting conditions corresponding to the user type information, the spatiotemporal trajectory information is determined.

[0158] Based on the spatiotemporal trajectory information and the key location information of the target area, the spatiotemporal information corresponding to the user is determined.

[0159] It is evident that implementation Figure 4 The described device can also match the corresponding spatiotemporal data processing method according to the user type, which is conducive to improving the comprehensiveness and rationality of the spatiotemporal information determination method, improving the pertinence and flexibility of the spatiotemporal information determination parameters, and thus improving the accuracy and reliability of the determined spatiotemporal information.

[0160] In yet another alternative embodiment, such as Figure 4 As shown, the device may further include:

[0161] The judgment module 304 is used to determine whether the spatiotemporal information meets the preset quality verification conditions before the determination module 302 determines the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions. When the judgment result is yes, the determination module 302 is triggered to perform the above-mentioned operation of determining the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions.

[0162] The adjustment module 305 is used to filter out the information that needs to be adjusted based on the spatiotemporal information and the quality verification conditions when the judgment module 304 determines that the spatiotemporal information does not meet the quality verification conditions; to perform quality adjustment operations on the information that needs to be adjusted according to the set information quality adjustment conditions, so as to obtain the quality-adjusted spatiotemporal information; and to trigger the determination module 302 to perform the above-mentioned operation of determining the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions.

[0163] It is evident that implementation Figure 4 The described device can also provide information quality verification and information quality adjustment methods, enriching the intelligent functions of an intelligent event prevention and control method. This is beneficial to improving the comprehensiveness and rationality of an intelligent event prevention and control method. By verifying and adjusting the quality of spatiotemporal information, it is beneficial to improve the availability and effectiveness of spatiotemporal information, reduce the occurrence of untimely prevention and control due to non-compliance of spatiotemporal information quality, and thus improve the operational stability of event prevention and control based on spatiotemporal information, thereby improving the efficiency and accuracy of event prevention and control.

[0164] In another optional embodiment, the acquisition module 301 is further configured to acquire the real-time event occurrence status of the target area after the determination module 302 determines the event prediction result corresponding to the target area based on the distribution feature information and the geographic information corresponding to the target area.

[0165] like Figure 4 As shown, the device may further include:

[0166] Analysis module 306 is used to analyze the event prediction results and the real-time event occurrence to obtain the prediction accuracy.

[0167] The judgment module 304 is also used to determine whether the prediction accuracy is greater than or equal to a preset prediction accuracy threshold.

[0168] The determination module 302 is also used to determine that the event prediction result is accurate and effective when the judgment module 304 determines that the prediction accuracy is greater than or equal to the prediction accuracy threshold.

[0169] The determining module 302 is also used to determine the prediction adjustment parameters based on the event prediction results, the real-time event occurrence, and the prediction accuracy when the judgment module 304 determines that the prediction accuracy is less than the prediction accuracy threshold. The prediction adjustment parameters are used to adjust the intelligent prevention and control operation to improve the prediction accuracy of the event.

[0170] It is evident that implementation Figure 4The described device can also analyze the prediction accuracy of event prediction results and adjust the parameters of intelligent event prevention and control, which enriches the intelligent functions of an intelligent event prevention and control method. This is conducive to improving the comprehensiveness and rationality of an intelligent event prevention and control method, improving the timeliness of event prevention and control adjustments, and thus improving the prediction accuracy and reliability of events, thereby improving the accuracy and reliability of event prevention and control.

[0171] In yet another optional embodiment, the method by which the determination module 304 determines whether the spatiotemporal information meets the preset quality verification conditions specifically includes:

[0172] Analyze the spatiotemporal information and the preset information quality requirements to obtain the quality compliance. The information quality requirements include one or more of the following: information format requirements, information type attribute requirements, and information content requirements.

[0173] Determine whether the quality compliance rate is greater than or equal to the preset quality compliance rate threshold;

[0174] When it is determined that the quality compliance degree is greater than or equal to the quality compliance degree threshold, the spatiotemporal information is determined to meet the preset quality verification conditions.

[0175] When the quality compliance is determined to be less than the quality compliance threshold, it is determined that the spatiotemporal information does not meet the quality verification conditions.

[0176] It is evident that implementation Figure 4 The described device can also spatialize the quality compliance of information and determine the satisfaction of the quality verification conditions of the spatialized information based on the comparison between the quality compliance and the quality compliance threshold. This helps to improve the rationality and feasibility of the method for determining the satisfaction of quality verification conditions, thereby improving the accuracy and reliability of the determined satisfaction of quality verification conditions, as well as the efficiency and convenience of determining the satisfaction of quality verification conditions.

[0177] In another optional embodiment, the event prediction module 303 is further configured to, after determining the event prediction result corresponding to the target area based on the distribution feature information and the geographic information corresponding to the target area, predict the event occurrence trend corresponding to the target area based on the event prediction result, the event occurrence trend including the occurrence trend of the event corresponding to the event prediction result and / or the occurrence trend of related events corresponding to the event.

[0178] Analysis module 306 is also used to analyze the trend of events and obtain the urgency of prevention and control in the target area.

[0179] The judgment module 304 is also used to determine whether the urgency of prevention and control is greater than or equal to the preset urgency threshold.

[0180] The determination module 302 is also used to determine the event prevention and control information corresponding to the target area based on the event occurrence trend, the prevention and control urgency and the set event prevention and control conditions when the judgment module 304 determines that the prevention and control urgency is greater than or equal to the prevention and control urgency threshold.

[0181] It is evident that implementation Figure 4 The described device can also provide intelligent event prevention and control suggestions. When the urgency of the prevention and control is greater than or equal to the urgency threshold, the device determines the event prevention and control suggestions, thereby realizing the intelligent event prevention and control suggestion function. This enriches the intelligent function of an event prevention and control method, which is conducive to improving the comprehensiveness and rationality of the event prevention and control method. In turn, it is conducive to improving the selectivity and efficiency of event prevention and control, and also conducive to reducing unnecessary waste of prevention and control resources.

[0182] Example 4

[0183] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of another intelligent event prevention and control device disclosed in an embodiment of the present invention. Wherein, Figure 5 The described apparatus may include a server, wherein the server includes a local server or a cloud server, and the embodiments of the present invention are not limited thereto. Figure 5 As shown, the device may include:

[0184] Memory 401 storing executable program code;

[0185] Processor 402 coupled to memory 401;

[0186] Furthermore, it may also include an input interface 403 coupled to the processor 402 and an output interface 404;

[0187] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the intelligent event prevention and control method described in Embodiment 1 or Embodiment 2.

[0188] Example 5

[0189] This invention discloses a computer storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of an intelligent event prevention and control method described in Embodiment 1 or Embodiment 2.

[0190] Example 6

[0191] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in an intelligent event prevention and control method described in Embodiment 1 or Embodiment 2.

[0192] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0193] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0194] Finally, it should be noted that the intelligent event prevention and control method and device disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent prevention and control method for incidents, characterized in that, The method includes: Collect spatiotemporal data of users corresponding to the target area, and determine basic data based on the spatiotemporal data and the set basic information processing conditions. Then, determine the user type of the user based on the obtained user intersection information and the set user type classification conditions. Based on the basic data, the user type information, and the parameter weighting processing conditions corresponding to the user type information, the spatiotemporal trajectory information is determined. Based on the spatiotemporal trajectory information and the key location information of the target area obtained, the spatiotemporal information corresponding to the user is determined; Based on the spatiotemporal information and the set distribution feature processing conditions, the distribution feature information corresponding to the target region is determined; Based on the distribution feature information and the geographical information corresponding to the target area, the event prediction result corresponding to the target area is determined, and the event prediction result is used to represent the probability of the event corresponding to the target area occurring. The parameter weighting processing conditions corresponding to the user type include: When the user type is the first user type, the spatiotemporal feature representation of each relevant trajectory information is extracted based on the activity location information of the user of the first user type in the past time period, and used as the spatiotemporal information of the user of the first user type. When the user type is the second user type, the spatiotemporal feature representation of each relevant trajectory information is extracted based on the activity location information of the second user type in the past time period, and weights and penalty coefficients are added to obtain the spatiotemporal information of the second user type. Among them, when the user is in the same place as the first user type, the weight coefficient is a; when the user is not in the same place as the first user type, the weight coefficient is b; when the user is not in the same place as the first user type, the penalty coefficient is c. When the user type is the third user type, the spatiotemporal feature representation of each relevant trajectory information is extracted based on the activity location information of the third user type in the past time period, and weights and penalty coefficients are added to obtain the spatiotemporal information of the third user type. Among them, when the user is in the same location as the first user type user and the second user type user, the weight coefficient is d; when the user is in the same location as the first user type user but not in the same location as the second user type user, the weight coefficient is e; when the user is not in the same location as the first user type user but is in the same location as the second user type user, the weight coefficient is f; when the user is in the same location as neither the first user type user nor the second user type user, the penalty coefficient is g.

2. The intelligent event prevention and control method according to claim 1, characterized in that, The step of determining the distribution feature information corresponding to the target region based on the spatiotemporal information and the set distribution feature processing conditions includes: Based on the spatiotemporal information and the set information dimension analysis conditions, the information dimension situation corresponding to the spatiotemporal information is determined; Based on the information dimensions, a target distribution feature processing model that matches the spatiotemporal information is selected from the pre-constructed set of distribution feature processing models; Based on the spatiotemporal information and the target distribution feature processing model, the corresponding distribution feature processing result is determined and used as the distribution feature information corresponding to the target region.

3. The intelligent event prevention and control method according to claim 1, characterized in that, Before determining the distribution feature information corresponding to the target region based on the spatiotemporal information and the set distribution feature processing conditions, the method further includes: Determine whether the spatiotemporal information meets the preset quality verification conditions; When the judgment result is yes, the operation of determining the distribution feature information corresponding to the target area based on the spatiotemporal information and the set distribution feature processing conditions is executed; When the judgment result is negative, the information that needs to be adjusted is selected based on the spatiotemporal information and the quality verification conditions; the information that needs to be adjusted is subjected to quality adjustment operation according to the set information quality adjustment conditions to obtain the spatiotemporal information after quality adjustment; and based on the spatiotemporal information after quality adjustment, the operation of determining the distribution feature information corresponding to the target area according to the spatiotemporal information and the set distribution feature processing conditions is executed.

4. The intelligent event prevention and control method according to claim 3, characterized in that, After determining the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area, the method further includes: The real-time event occurrence status corresponding to the target area is obtained, and the event prediction results and the real-time event occurrence status are analyzed to obtain the prediction accuracy. Determine whether the prediction accuracy is greater than or equal to a preset prediction accuracy threshold; When it is determined that the prediction accuracy is greater than or equal to the prediction accuracy threshold, the event prediction result is determined to be accurate and effective. When it is determined that the prediction accuracy is less than the prediction accuracy threshold, prediction adjustment parameters are determined based on the event prediction result, the real-time event occurrence, and the prediction accuracy. The prediction adjustment parameters are used to adjust the intelligent prevention and control operation to improve the prediction accuracy of the event.

5. The intelligent event prevention and control method according to claim 4, characterized in that, The step of determining whether the spatiotemporal information meets the preset quality verification conditions includes: Analyze the spatiotemporal information and the preset information quality requirements to obtain the quality compliance. The information quality requirements include one or more of the following: information format requirements, information type attribute requirements, and information content requirements. Determine whether the quality compliance degree is greater than or equal to a preset quality compliance degree threshold; When it is determined that the quality compliance degree is greater than or equal to the quality compliance degree threshold, it is determined that the spatiotemporal information meets the preset quality verification conditions. When it is determined that the quality compliance degree is less than the quality compliance degree threshold, it is determined that the spatiotemporal information does not meet the quality verification conditions.

6. The intelligent event prevention and control method according to claim 5, characterized in that, After determining the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area, the method further includes: Based on the event prediction results, predict the event occurrence trend corresponding to the target area. The event occurrence trend includes the event occurrence trend corresponding to the event prediction results and / or the occurrence trend of related events corresponding to the event. Analyze the trend of the events to determine the urgency of prevention and control in the target area; Determine whether the urgency level of the prevention and control measures is greater than or equal to a preset urgency level threshold. When it is determined that the urgency of the prevention and control is greater than or equal to the urgency threshold, the event prevention and control information corresponding to the target area is determined based on the event occurrence trend, the urgency of the prevention and control, and the set event prevention and control conditions.

7. An intelligent event prevention and control device, characterized in that, The device includes: The acquisition module is used to collect spatiotemporal data of users corresponding to the target area; The determination module is used to determine basic data based on the spatiotemporal data and set basic information processing conditions; determine the user type of the user based on the acquired user intersection information and set user type classification conditions; determine spatiotemporal trajectory information based on the basic data, the user type information, and the parameter weight processing conditions corresponding to the user type information; determine the spatiotemporal information corresponding to the user based on the spatiotemporal trajectory information and the acquired key location information of the target area; and determine the distribution feature information corresponding to the target area based on the spatiotemporal information and set distribution feature processing conditions. The event prediction module determines the event prediction result corresponding to the target area based on the distribution feature information and the geographical information corresponding to the target area. The event prediction result is used to represent the probability of the event occurring in the target area. The parameter weighting processing conditions corresponding to the user type include: When the user type is the first user type, the spatiotemporal feature representation of each relevant trajectory information is extracted based on the activity location information of the user of the first user type in the past time period, and used as the spatiotemporal information of the user of the first user type. When the user type is the second user type, the spatiotemporal feature representation of each relevant trajectory information is extracted based on the activity location information of the second user type in the past time period, and weights and penalty coefficients are added to obtain the spatiotemporal information of the second user type. Among them, when the user is in the same place as the first user type, the weight coefficient is a; when the user is not in the same place as the first user type, the weight coefficient is b; when the user is not in the same place as the first user type, the penalty coefficient is c. When the user type is the third user type, the spatiotemporal feature representation of each relevant trajectory information is extracted based on the activity location information of the third user type in the past time period, and weights and penalty coefficients are added to obtain the spatiotemporal information of the third user type. Among them, when the user is in the same location as the first user type user and the second user type user, the weight coefficient is d; when the user is in the same location as the first user type user but not in the same location as the second user type user, the weight coefficient is e; when the user is not in the same location as the first user type user but is in the same location as the second user type user, the weight coefficient is f; when the user is in the same location as neither the first user type user nor the second user type user, the penalty coefficient is g.

8. An intelligent event prevention and control device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute an intelligent event prevention and control method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute an intelligent event prevention and control method as described in any one of claims 1-6.

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