CN110580565B - AI heat prediction-based public population dispersion scheduling method and system - Google Patents
AI heat prediction-based public population dispersion scheduling method and system Download PDFInfo
- Publication number
- CN110580565B CN110580565B CN201910448060.2A CN201910448060A CN110580565B CN 110580565 B CN110580565 B CN 110580565B CN 201910448060 A CN201910448060 A CN 201910448060A CN 110580565 B CN110580565 B CN 110580565B
- Authority
- CN
- China
- Prior art keywords
- current driving
- driving event
- content data
- dimension
- influence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 239000006185 dispersion Substances 0.000 title claims abstract description 24
- 239000013598 vector Substances 0.000 claims abstract description 107
- 230000002776 aggregation Effects 0.000 claims abstract description 38
- 238000004220 aggregation Methods 0.000 claims abstract description 38
- 238000013139 quantization Methods 0.000 claims abstract description 29
- 238000003062 neural network model Methods 0.000 claims abstract description 26
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000010606 normalization Methods 0.000 claims abstract description 16
- 238000011156 evaluation Methods 0.000 claims description 44
- 238000013507 mapping Methods 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 230000003370 grooming effect Effects 0.000 description 16
- 230000008520 organization Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000007405 data analysis Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000007935 neutral effect Effects 0.000 description 4
- 230000001737 promoting effect Effects 0.000 description 3
- 238000010187 selection method Methods 0.000 description 3
- 230000009154 spontaneous behavior Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The utility model provides a public population dispersion scheduling method based on AI heat prediction, includes: acquiring content data associated with the current driving event from a public data source, and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data; determining an influence coefficient of the heat of the current driving event according to the content data; respectively carrying out normalization processing on the multiple quantization indexes; determining a positive influence dimension, a negative influence dimension and an additional dimension of a heat vector of the current driving event, integrating the positive influence dimension, the negative influence dimension and the additional dimension into the heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event; and executing corresponding public population dispersion scheduling according to the personnel aggregation degree. The present application enables prediction of the occurrence of people gathering and the degree of gathering in public places caused by driving events.
Description
Technical Field
The application relates to the technical field of public population dispersion scheduling, in particular to a public population dispersion scheduling method and system based on AI heat prediction.
Background
Public population is dredged, namely when a certain public place possibly presents personnel gathering, the public place is dredged and dispatched by adopting the modes of current limiting, shunting, opening standby places and standby roads, increasing police strength and the like, so that the events of crowding and trampling, large-scale detention of personnel and the like are avoided.
The gathering of people present in public places is often caused by some driving event, such as holding a sporting event or concert, meeting a certain star, vigorously promoting a certain market, watching a flag, meeting traditional customs over the night, and so on. Some of the driving events need to be recorded by an organizer to a public security organization or a management department, but many driving events have no recording requirement or do not have the organizer and belong to a group spontaneous behavior, so that the public security organization or the management department cannot timely master the driving event. Although some driving events can be known by the public security organization or the management department, the popularity of the driving events is difficult to predict in advance, for example, a star holds a fan meeting and records information is filled and arranged for 500 people, but in practice, thousands or even tens of thousands of fans may be gathered around the calling field of the star, so that the public security organization or the management department is difficult to predict the occurrence of people gathering or the scale of people gathering completely in advance, and therefore, the public security organization or the management department is difficult to lead the scheduling plan in advance.
Disclosure of Invention
In view of this, an object of the present application is to provide a public population dispersion scheduling method and system based on AI popularity prediction, so as to solve the technical problem that in the prior art, occurrence and aggregation degree of people in a public place caused by a driving event are difficult to predict in advance, thereby affecting dispersion scheduling of aggregated people.
In view of the above, in a first aspect of the present application, a method for public demographic breakout scheduling based on AI popularity prediction is provided, including:
acquiring content data associated with a current driving event from a public data source, and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data;
determining an influence coefficient of the heat of the current driving event according to the content data, wherein the influence coefficient comprises a positive influence coefficient and a negative influence coefficient;
respectively carrying out normalization processing on the quantization indexes, and quantizing the quantization indexes into index data of the same order of magnitude;
determining a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data;
determining an additional dimension of the heat vector of the current driving event according to the ambient environment information and the position information of the current driving event;
integrating the positive influence dimension, the negative influence dimension and the additional dimension into a heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event;
and executing corresponding public population dispersion scheduling according to the personnel gathering degree.
In some embodiments, the common data source comprises:
one or more of a microblog server, a search engine server, a forum server and a web page server;
the obtaining content data associated with the current driving event from a common data source comprises:
and searching content data including keywords related to the current driving event in the content data based on the keywords as effective content data, and counting corresponding microblog forwarding amount and/or engine search amount and/or forum posting amount and/or webpage browsing amount as quantitative indexes.
In some embodiments, the determining an influence coefficient of the heat of the current driving event from the content data includes:
counting the number of content data including positive influence words and the number of content data including negative influence words in the content data, determining a positive influence coefficient of the current driving event according to the proportion of the content data including the positive influence words in all the content data, and determining a negative influence coefficient of the current driving event according to the proportion of the content data including the negative influence words in all the content data.
In some embodiments, further comprising:
the method comprises the steps of establishing a positive influence word lexicon and a negative influence word lexicon in advance, wherein the positive influence word lexicon comprises common positive evaluation words in life vocabularies and network vocabularies, and the negative influence word lexicon comprises common negative evaluation words in life vocabularies and network vocabularies.
In some embodiments, the counting the number of content data including positive influence words and the number of content data including negative influence words in the content data includes:
and counting the number of content data including positive influence words in the positive influence word bank and the number of content data including negative influence words in the negative influence word bank in the content data.
In some embodiments, said determining a positive influence dimension and a negative influence dimension of a heat vector of said current drive event from said influence coefficients and said indicator data comprises:
determining the product of the quantity of content data including positive influence words in the positive influence word bank and the positive influence system in the content data after the number quantization as the positive influence dimension of the heat vector of the current driving event, and determining the product of the quantity of content data including negative influence words in the negative influence word bank and the negative influence system in the content data after the number quantization as the negative influence dimension of the heat vector of the current driving event.
In some embodiments, the determining an additional dimension of the heat vector of the current driving event according to the weather of the surrounding environment of the current driving event and the location information of the current driving event comprises:
mapping the weather of the surrounding environment of the current driving event into a corresponding numerical value according to a preset mapping rule, wherein the numerical value is used as the weather dimension of the heat vector of the current driving event;
and mapping the distance between the position coordinate of the current driving event and a preset reference coordinate into a corresponding numerical value according to a preset mapping rule, wherein the corresponding numerical value is used as the position dimension of the heat vector of the current driving event.
In accordance with the above objectives, in a second aspect of the present application, there is provided a public demographic dispersion scheduling system based on AI popularity prediction, including:
the system comprises a content data acquisition module, a data processing module and a data processing module, wherein the content data acquisition module is used for acquiring content data associated with a current driving event from a public data source and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data;
an influence coefficient determining module, configured to determine an influence coefficient of the heat of the current driving event according to the content data, where the influence coefficient includes a positive influence coefficient and a negative influence coefficient;
the normalization processing module is used for respectively carrying out normalization processing on the quantization indexes and quantizing the quantization indexes into index data of the same order of magnitude;
the influence dimension determining module is used for determining a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data;
an additional dimension determining module, configured to determine an additional dimension of the heat vector of the current driving event according to the ambient environment information and the location information of the current driving event;
the heat vector analysis module is used for integrating the positive influence dimension, the negative influence dimension and the additional dimension into a heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event;
and the dispersion scheduling module is used for executing corresponding public population dispersion scheduling according to the personnel aggregation degree.
In some embodiments, the influence coefficient determining module is specifically configured to:
counting the number of content data including positive influence words and the number of content data including negative influence words in the content data, determining a positive influence coefficient of the current driving event according to the proportion of the content data including the positive influence words in all the content data, and determining a negative influence coefficient of the current driving event according to the proportion of the content data including the negative influence words in all the content data.
In some embodiments, further comprising:
and the storage module is used for storing the pre-established positive influence word bank and negative influence word bank.
The embodiment of the application provides a public population dispersion scheduling method and system based on AI heat prediction, wherein the method comprises the following steps: acquiring content data associated with a current driving event from a public data source, and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data; determining an influence coefficient of the heat of the current driving event according to the content data, wherein the influence coefficient comprises a positive influence coefficient and a negative influence coefficient; respectively carrying out normalization processing on the quantization indexes, and quantizing the quantization indexes into index data of the same order of magnitude; determining a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data; determining an additional dimension of the heat vector of the current driving event according to the ambient environment information and the position information of the current driving event; integrating the positive influence dimension, the negative influence dimension and the additional dimension into a heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event; and executing corresponding public population dispersion scheduling according to the personnel gathering degree. The security equipment intelligent site selection method and system based on GIS space big data analysis can predict the occurrence and aggregation degree of personnel aggregation in public places caused by driving events, and therefore dredging and scheduling of the aggregated personnel are facilitated.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a flowchart of a public demographic grooming scheduling method based on AI popularity prediction according to a first embodiment of the present application;
fig. 2 is a flowchart of a public demographic grooming scheduling method based on AI popularity prediction according to a second embodiment of the present application;
fig. 3 is a functional block diagram of a public demographic grooming scheduling system based on AI popularity prediction according to a third embodiment of the present application;
fig. 4 is a functional block diagram of a public demographic grooming scheduling system based on AI popularity prediction according to a fourth embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 is a flowchart of a public demographic grooming scheduling method based on AI popularity prediction according to an embodiment of the present application. As can be seen from fig. 1, as an embodiment of the present application, the method for scheduling a public demographic breakout based on AI popularity prediction may include the following steps:
s101: acquiring content data associated with a current driving event from a common data source, and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data.
In this embodiment, when a driving event is about to occur, content data associated with the about to occur driving event, i.e., the current driving event, may be acquired from a common data source. The driving events in the implementation can be, for example, traditional customs such as holding a sports match or a concert, meeting a certain star, vigorously promoting a certain market, watching a flag, meeting and crossing the year and night, and under a common condition, the driving events can cause people to gather, so that the risk of crowding and trampling is increased, and further, the people are detained in a large scale. The public data source in this embodiment generally refers to a data source from which the public can obtain information, and may be one or more of a microblog server, a search engine server, a forum server, and a web page server, for example. Specifically, content data including a keyword associated with the current driving event may be searched as valid content data in the content data based on the keyword, and corresponding microblog forwarding amount and/or engine search amount and/or forum posting amount and/or web browsing amount may be counted as a quantitative index. For example, the keyword may be zhou jieren AND concert, XX square AND overnight, or XX market AND promotion, AND the content data covering the keyword may be used as the valid content data. Before the current driving event happens, some public information is generally sent through a public data source, such as the time or the place of holding a sports game or a concert, or the time or the place of a celebrity meeting, or the time or the place of watching traditional customs such as raising flags, meeting cross-year and night, and the like, and meanwhile, content data associated with the current driving event, such as microblog forwarding, engine searching, forum posting, web browsing and the like, is displayed on the public data source,
therefore, the content data associated with the current driving event can be counted, and a plurality of quantitative indexes corresponding to the current driving event, such as microblog forwarding amount, engine search amount, forum posting amount or web page browsing amount, can be determined.
S102: and determining influence coefficients of the heat degree of the current driving event according to the content data, wherein the influence coefficients comprise positive influence coefficients and negative influence coefficients.
The quantitative index can only indicate the attention level of the driving event on the network, but does not completely correspond to the heat degree, and the influence coefficient of the heat degree of the driving event needs to be determined aiming at the content data associated with the driving event. Therefore, after counting a plurality of quantization indexes corresponding to the current driving event, an influence coefficient of the heat of the current driving event may be determined according to the content data associated with the current driving event, where the influence coefficient includes a positive influence coefficient and a negative influence coefficient. Specifically, the process of forwarding the microblog or posting the microblog or browsing the webpage in the forum by the user or the process of posting or browsing the webpage in the forum is often accompanied by some evaluations on the current driving event, some of the evaluations are positive evaluations, some of the evaluations are negative evaluations, and some of the evaluations are neutral evaluations, wherein the positive evaluations have a positive influence on the number of people gathering caused by the current driving event, the negative evaluations have a negative influence on the number of people gathering caused by the current driving event, and the neutral evaluations have no influence on the number of people gathering caused by the current driving event, so that the data volume of the content data targeted by the positive evaluations can be counted, the proportion of the content data targeted by the positive evaluations to all the content data can be used as a positive influence coefficient, and the data volume of the content data targeted by the negative evaluations can be counted, and the proportion of the content data for which negative evaluation is made to the entire content data is taken as a negative influence coefficient.
S103: and respectively carrying out normalization processing on the quantization indexes, and quantizing the quantization indexes into index data of the same order of magnitude.
In this embodiment, because the content data associated with the current driving event are represented in different manners, statistics of a plurality of quantitative indicators corresponding to the current driving event are different, for example, the magnitude of forwarding amount of the microblog may be counted in "ten thousand", the posting amount of the forum may be counted in "hundred", the quantitative indicators are not in the same magnitude and are difficult to reflect the heat degree of the current driving event, therefore, the plurality of quantitative indicators may be normalized respectively, each quantitative indicator is converted into a normalized score, the score range may be, for example, 0-100 minutes, the forwarding amount of the microblog may be counted in "ten thousand", the count amount of the forwarding amount of the microblog is taken as the score of the forwarding amount of the microblog, a value exceeding 100 is taken as 100, and thus, for example, the forwarding amount of the microblog is 834673, counting by ten thousand is 83.4673 ten thousand, rounded to 83 ten thousand, and the corresponding score is 83 points. For example, the number of posts of the forum is 3467, which is 34.67 hundred when counted in hundreds, rounded up to 35 ten thousand, and the corresponding score is 35 minutes when counted in hundreds. Similarly, the engine search volume or the web browsing volume can be converted into the same normalized score according to the method.
S104: and determining a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data.
After the positive influence coefficient and the negative influence coefficient of the heat of the current driving event and the normalized scores of the multiple quantitative indexes corresponding to the current driving event are determined, the product of the positive influence coefficient of the heat of the current driving event and the normalized scores of the quantitative indexes can be used as the positive influence dimension of the heat vector of the current driving event, and the product of the negative influence coefficient of the heat of the current driving event and the normalized scores of the quantitative indexes can be used as the negative influence dimension of the heat vector of the current driving event.
S105: and determining the additional dimension of the heat vector of the current driving event according to the ambient environment information and the position information of the current driving event.
In this embodiment, after determining the positive influence dimension and the negative influence dimension of the heat vector of the current driving event, the additional dimension of the heat vector of the current driving event may be further determined by combining the ambient environment information and the location information of the current driving event venue. Specifically, the current driving event occurrence location may be mapped to a corresponding numerical value as an ambient environment information dimension of the heat vector of the current driving event, the current driving event occurrence location may be obtained through a relevant channel, for example, a weather forecast on a smart phone, and the mapping relationship may be: the numerical value corresponding to sunny days is 100, the numerical value corresponding to cloudy days is 80, the numerical value corresponding to haze days is 60, the numerical value corresponding to rainy and snowy days is 40, and the numerical value corresponding to other severe weather (such as rainstorm, thunderstorm and the like) is 20. Further, the distance between the current driving event place and a preset reference coordinate can be used as the position information dimension of the heat vector of the current driving event, the distance may be in units of "hectometer", the distance of the current driving event place from a preset reference coordinate may be acquired through a relevant channel, such as a map function on a smart phone, for example, the current driving event venue is in beijing, the distance of the current drive event venue from the wangfu well square may be taken as the location information dimension of the heat vector for the current drive event, alternatively, the distance between the current driving event occurrence place and the Qinghua university may be used as the position information dimension of the heat vector of the current driving event, where the Wangfu Square and the Qinghua university are the preset reference coordinates.
S106: integrating the positive influence dimension, the negative influence dimension and the additional dimension into a heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event.
After determining the positive influence dimension, the negative influence dimension, and the additional dimensions (the ambient information dimension and the location information dimension) of the heat vector of the current driving event, the above dimensions may be integrated into the heat vector of the current driving event. For example, values of positive influence dimensions corresponding to a microblog forwarding amount, an engine search amount, a forum posting amount, a web browsing amount and the like are 35, 47, 26 and 63, values of negative influence dimensions corresponding to a microblog forwarding amount, an engine search amount, a forum posting amount, a web browsing amount and the like are 27, 24, 18 and 15, a value of a surrounding environment information dimension is 84, and a value of a location information dimension is 65, then an integrated hot vector is (35, 47, 26, 63, 27, 24, 18, 15, 84 and 65), and then a pre-trained neural network model is used for analyzing the hot vector to determine a people aggregation degree corresponding to the current driving event. The neural network model in this embodiment may be a neural network model generated by collecting in advance the heat vectors of some already held driving events and the corresponding degree of aggregation of people as samples, and training the BP neural network by using the BP neural network with the heat vectors of the samples as input and the corresponding degree of aggregation of people as output, and may output the degree of aggregation of people corresponding to the current driving event by using the generated neural network model with the heat vectors of the current driving event as input.
S107: and executing corresponding public population dispersion scheduling according to the personnel gathering degree.
After the people gathering degree corresponding to the current driving event is determined, corresponding dredging scheduling measures can be taken so as to conduct dredging scheduling on the gathered population caused by the current driving event. The grooming scheduling measure in this embodiment may be a grooming scheduling measure in the prior art, which is not listed here.
The security equipment intelligent address selection method based on GIS space big data analysis can predict the occurrence and aggregation degree of personnel aggregation in public places caused by driving events, and therefore dredging and scheduling of the aggregated personnel are facilitated.
Fig. 2 is a flowchart of a public demographic grooming scheduling method based on AI popularity prediction according to a second embodiment of the present application. As can be seen from the figure, the method for scheduling public demographic dispersion based on AI popularity prediction according to the embodiment may include the following steps:
s201: the method comprises the steps of establishing a positive influence word lexicon and a negative influence word lexicon in advance, wherein the positive influence word lexicon comprises common positive evaluation words in life vocabularies and network vocabularies, and the negative influence word lexicon comprises common negative evaluation words in life vocabularies and network vocabularies.
In this embodiment, a positive influence word lexicon and a negative influence word lexicon may be established in advance, the positive influence word lexicon includes the common positive evaluation words in the life vocabularies and the network vocabularies, and the negative influence word lexicon includes the common negative evaluation words in the life vocabularies and the network vocabularies. The positive influence word bank and the negative influence word bank can be two word banks and can also be two files stored in the same word bank.
S202: acquiring content data associated with a current driving event from a common data source, and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data.
In this embodiment, when a driving event is about to occur, content data associated with the about to occur driving event, i.e., the current driving event, may be acquired from a common data source. The public data source in this embodiment generally refers to a data source from which the public can obtain information, and may be one or more of a microblog server, a search engine server, a forum server, and a web page server, for example. Before a current driving event occurs, some public information is generally sent through a public data source, for example, time or place of holding a sports game or a concert, or time or place of a celebrity meeting, or time or place of watching traditional customs such as raising flags, welcoming cross-year and night, and the like, and content data associated with the current driving event, for example, microblog forwarding, engine search, forum posting, web browsing and the like, is displayed on the public data source, so that the content data associated with the current driving event can be counted, and a plurality of quantitative indexes corresponding to the current driving event, for example, microblog forwarding amount, engine search amount, forum posting amount, web browsing amount and the like, are determined.
S203: counting the number of content data including positive influence words and the number of content data including negative influence words in the content data, determining a positive influence coefficient of the current driving event according to the proportion of the content data including the positive influence words in all the content data, and determining a negative influence coefficient of the current driving event according to the proportion of the content data including the negative influence words in all the content data.
S204: and respectively carrying out normalization processing on the quantization indexes, and quantizing the quantization indexes into index data of the same order of magnitude.
S205: determining the product of the quantity of content data including positive influence words in the positive influence word bank and the positive influence system in the content data after the number quantization as the positive influence dimension of the heat vector of the current driving event, and determining the product of the quantity of content data including negative influence words in the negative influence word bank and the negative influence system in the content data after the number quantization as the negative influence dimension of the heat vector of the current driving event.
S206: mapping the weather of the surrounding environment of the current driving event into a corresponding numerical value according to a preset mapping rule, wherein the numerical value is used as the weather dimension of the heat vector of the current driving event; and mapping the distance between the position coordinate of the current driving event and a preset reference coordinate into a corresponding numerical value according to a preset mapping rule, wherein the corresponding numerical value is used as the position dimension of the heat vector of the current driving event.
S207: integrating the positive influence dimension, the negative influence dimension and the additional dimension into a heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event.
S208: and executing corresponding public population dispersion scheduling according to the personnel gathering degree.
For details of the process from step S203 to step S208, refer to embodiment one, and detailed description is omitted here.
The security equipment intelligent address selection method based on GIS space big data analysis can predict the occurrence and aggregation degree of personnel aggregation in public places caused by driving events, and therefore dredging and scheduling of the aggregated personnel are facilitated.
Fig. 3 is a functional block diagram of a public demographic grooming scheduling system based on AI popularity prediction according to a third embodiment of the present application. The public population dispersion scheduling system based on AI popularity prediction of this embodiment may include:
a content data obtaining module 301, configured to obtain content data associated with a current driving event from a common data source, and count, according to the content data, a plurality of quantization indexes corresponding to the current driving event.
Specifically, when a driving event is about to occur, the content data acquisition module 301 may acquire content data associated with the impending driving event, i.e., the current driving event, from a common data source. The driving events in the implementation can be, for example, traditional customs such as holding a sports match or a concert, meeting a certain star, vigorously promoting a certain market, watching a flag, meeting and crossing the year and night, and under a common condition, the driving events can cause people to gather, so that the risk of crowding and trampling is increased, and further, the people are detained in a large scale. The public data source in this embodiment generally refers to a data source from which the public can obtain information, and may be one or more of a microblog server, a search engine server, a forum server, and a web page server, for example. Before a current driving event occurs, some public information is generally sent through a public data source, for example, time or place of holding a sports game or a concert, or time or place of a celebrity meeting, or time or place of watching traditional customs such as raising flags, welcoming cross-year and night, and the like, and content data associated with the current driving event, for example, microblog forwarding, engine search, forum posting, web browsing and the like, is displayed on the public data source, so that the content data associated with the current driving event can be counted, and a plurality of quantitative indexes corresponding to the current driving event, for example, microblog forwarding amount, engine search amount, forum posting amount, web browsing amount and the like, are determined.
An influence coefficient determining module 302, configured to determine, according to the content data, influence coefficients of the heat of the current driving event, where the influence coefficients include a positive influence coefficient and a negative influence coefficient.
The quantitative index can only indicate the attention level of the driving event on the network, but does not completely correspond to the heat degree, and the influence coefficient of the heat degree of the driving event needs to be determined aiming at the content data associated with the driving event. Therefore, after counting a plurality of quantization indexes corresponding to the current driving event, an influence coefficient of the heat of the current driving event may be determined according to the content data associated with the current driving event, where the influence coefficient includes a positive influence coefficient and a negative influence coefficient. Specifically, the process of forwarding the microblog or posting the microblog or browsing the webpage in the forum by the user or the process of posting or browsing the webpage in the forum is often accompanied by some evaluations on the current driving event, some of the evaluations are positive evaluations, some of the evaluations are negative evaluations, and some of the evaluations are neutral evaluations, wherein the positive evaluations have a positive influence on the number of people gathering caused by the current driving event, the negative evaluations have a negative influence on the number of people gathering caused by the current driving event, and the neutral evaluations have no influence on the number of people gathering caused by the current driving event, so that the data volume of the content data targeted by the positive evaluations can be counted, the proportion of the content data targeted by the positive evaluations to all the content data can be used as a positive influence coefficient, and the data volume of the content data targeted by the negative evaluations can be counted, and the proportion of the content data for which negative evaluation is made to the entire content data is taken as a negative influence coefficient.
A normalization processing module 303, configured to perform normalization processing on the multiple quantization indexes respectively, and quantize the multiple quantization indexes into index data of the same order of magnitude.
In this embodiment, because the content data associated with the current driving event are represented in different manners, statistics of a plurality of quantitative indicators corresponding to the current driving event are different, for example, the magnitude of forwarding amount of the microblog may be counted in "ten thousand", the posting amount of the forum may be counted in "hundred", the quantitative indicators are not in the same magnitude and are difficult to reflect the heat degree of the current driving event, therefore, the plurality of quantitative indicators may be normalized respectively, each quantitative indicator is converted into a normalized score, the score range may be, for example, 0-100 minutes, the forwarding amount of the microblog may be counted in "ten thousand", the count amount of the forwarding amount of the microblog is taken as the score of the forwarding amount of the microblog, a value exceeding 100 is taken as 100, and thus, for example, the forwarding amount of the microblog is 834673, counting by ten thousand is 83.4673 ten thousand, rounded to 83 ten thousand, and the corresponding score is 83 points. For example, the number of posts of the forum is 3467, which is 34.67 hundred when counted in hundreds, rounded up to 35 ten thousand, and the corresponding score is 35 minutes when counted in hundreds. Similarly, the engine search volume or the web browsing volume can be converted into the same normalized score according to the method.
An influence dimension determination module 304, configured to determine a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data.
After the positive influence coefficient and the negative influence coefficient of the heat of the current driving event and the normalized scores of the multiple quantitative indexes corresponding to the current driving event are determined, the product of the positive influence coefficient of the heat of the current driving event and the normalized scores of the quantitative indexes can be used as the positive influence dimension of the heat vector of the current driving event, and the product of the negative influence coefficient of the heat of the current driving event and the normalized scores of the quantitative indexes can be used as the negative influence dimension of the heat vector of the current driving event.
An additional dimension determining module 305, configured to determine an additional dimension of the heat vector of the current driving event according to the ambient environment information and the location information of the current driving event.
In this embodiment, after determining the positive influence dimension and the negative influence dimension of the heat vector of the current driving event, the additional dimension of the heat vector of the current driving event may be further determined by combining the ambient environment information and the location information of the current driving event venue. Specifically, the current driving event occurrence location may be mapped to a corresponding numerical value as an ambient environment information dimension of the heat vector of the current driving event, the current driving event occurrence location may be obtained through a relevant channel, for example, a weather forecast on a smart phone, and the mapping relationship may be: the numerical value corresponding to sunny days is 100, the numerical value corresponding to cloudy days is 80, the numerical value corresponding to haze days is 60, the numerical value corresponding to rainy and snowy days is 40, and the numerical value corresponding to other severe weather (such as rainstorm, thunderstorm and the like) is 20. Further, the distance between the current driving event place and a preset reference coordinate can be used as the position information dimension of the heat vector of the current driving event, the distance may be in units of "hectometer", the distance of the current driving event place from a preset reference coordinate may be acquired through a relevant channel, such as a map function on a smart phone, for example, the current driving event venue is in beijing, the distance of the current drive event venue from the wangfu well square may be taken as the location information dimension of the heat vector for the current drive event, alternatively, the distance between the current driving event occurrence place and the Qinghua university may be used as the position information dimension of the heat vector of the current driving event, where the Wangfu Square and the Qinghua university are the preset reference coordinates.
And a heat vector analysis module 306, configured to integrate the positive influence dimension, the negative influence dimension, and the additional dimension into a heat vector of the current driving event, analyze the heat vector by using a pre-trained neural network model, and determine a people aggregation degree corresponding to the current driving event.
After determining the positive influence dimension, the negative influence dimension, and the additional dimensions (the ambient information dimension and the location information dimension) of the heat vector of the current driving event, the above dimensions may be integrated into the heat vector of the current driving event. For example, values of positive influence dimensions corresponding to a microblog forwarding amount, an engine search amount, a forum posting amount, a web browsing amount and the like are 35, 47, 26 and 63, values of negative influence dimensions corresponding to a microblog forwarding amount, an engine search amount, a forum posting amount, a web browsing amount and the like are 27, 24, 18 and 15, a value of a surrounding environment information dimension is 84, and a value of a location information dimension is 65, then an integrated hot vector is (35, 47, 26, 63, 27, 24, 18, 15, 84 and 65), and then a pre-trained neural network model is used for analyzing the hot vector to determine a people aggregation degree corresponding to the current driving event. The neural network model in this embodiment may be a neural network model generated by collecting in advance the heat vectors of some already held driving events and the corresponding degree of aggregation of people as samples, and training the BP neural network by using the BP neural network with the heat vectors of the samples as input and the corresponding degree of aggregation of people as output, and may output the degree of aggregation of people corresponding to the current driving event by using the generated neural network model with the heat vectors of the current driving event as input.
And a grooming scheduling module 307, configured to perform corresponding public population grooming scheduling according to the people aggregation degree.
After the people gathering degree corresponding to the current driving event is determined, corresponding dredging scheduling measures can be taken so as to conduct dredging scheduling on the gathered population caused by the current driving event. The grooming scheduling measure in this embodiment may be a grooming scheduling measure in the prior art, which is not listed here.
The security equipment intelligent site selection system based on GIS space big data analysis can predict the occurrence of personnel aggregation and aggregation degree of public places caused by driving events, and therefore dredging and scheduling of aggregated personnel are facilitated.
Fig. 4 is a functional block diagram of a public demographic grooming scheduling system based on AI popularity prediction according to a fourth embodiment of the present application. The public population dispersion scheduling system based on AI popularity prediction of this embodiment may include:
a content data obtaining module 401, configured to obtain content data associated with a current driving event from a common data source, and count, according to the content data, a plurality of quantization indexes corresponding to the current driving event.
An influence coefficient determining module 402, configured to determine an influence coefficient of the heat of the current driving event according to the content data, where the influence coefficient includes a positive influence coefficient and a negative influence coefficient.
A normalization processing module 403, configured to perform normalization processing on the multiple quantization indexes respectively, and quantize the multiple quantization indexes into index data of the same order of magnitude.
An influence dimension determining module 404, configured to determine a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data.
An additional dimension determining module 405, configured to determine an additional dimension of the heat vector of the current driving event according to the ambient environment information and the location information of the current driving event.
And a heat vector analysis module 406, configured to integrate the positive influence dimension, the negative influence dimension, and the additional dimension into a heat vector of the current driving event, analyze the heat vector by using a pre-trained neural network model, and determine a people aggregation degree corresponding to the current driving event.
And the grooming scheduling module 407 is configured to perform corresponding public population grooming scheduling according to the people aggregation degree.
For the specific working principle of the above modules, reference is made to the third embodiment, which is not repeated herein, and in addition, the embodiments of the present application further include:
the storage module 408 is configured to store a pre-established positive influence word bank and a pre-established negative influence word bank.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A public population dispersion scheduling method based on AI heat prediction is characterized by comprising the following steps:
acquiring content data associated with a current driving event from a public data source, and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data;
determining an influence coefficient of the heat of the current driving event according to the content data, wherein the influence coefficient comprises a positive influence coefficient and a negative influence coefficient; counting the data amount of the content data for positive evaluation, taking the proportion of the content data for positive evaluation to all the content data as a positive influence coefficient, counting the data amount of the content data for negative evaluation, and taking the proportion of the content data for negative evaluation to all the content data as a negative influence coefficient;
respectively carrying out normalization processing on the quantization indexes, and quantizing the quantization indexes into index data of the same order of magnitude; respectively carrying out normalization processing on the quantitative indexes, and converting each quantitative index into a normalized score, wherein the score range is 0-100;
determining a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data; after determining the positive influence coefficient and the negative influence coefficient of the heat degree of the current driving event and the normalized scores of a plurality of quantitative indexes corresponding to the current driving event, taking the product of the positive influence coefficient of the heat degree of the current driving event and the normalized scores of the quantitative indexes as the positive influence dimension of the heat degree vector of the current driving event, and taking the product of the negative influence coefficient of the heat degree of the current driving event and the normalized scores of the quantitative indexes as the negative influence dimension of the heat degree vector of the current driving event;
determining an additional dimension of the heat vector of the current driving event according to the ambient environment information and the position information of the current driving event; after determining the positive influence dimension and the negative influence dimension of the heat vector of the current driving event, further determining an additional dimension of the heat vector of the current driving event by combining the ambient environment information and the position information of the current driving event;
integrating the positive influence dimension, the negative influence dimension and the additional dimension into a heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event; after determining a positive influence dimension, a negative influence dimension and an additional dimension of the heat vector of the current driving event, integrating the dimensions into the heat vector of the current driving event; then analyzing the heat vector by using a pre-trained neural network model to determine the personnel gathering degree corresponding to the current driving event; the neural network model is a neural network model generated by training a BP neural network by taking a previously collected heat vector of a held driving event and a corresponding personnel aggregation degree as samples, taking the heat vector of the sample as input and the corresponding personnel aggregation degree as output by using the BP neural network, and outputting the personnel aggregation degree corresponding to the current driving event by taking the heat vector of the current driving event as input by using the generated neural network model;
and executing corresponding public population dispersion scheduling according to the personnel gathering degree.
2. The method of claim 1, wherein the common data source comprises:
one or more of a microblog server, a search engine server, a forum server and a web page server;
the obtaining content data associated with the current driving event from a common data source comprises:
searching content data including keywords related to the current driving event in the content data based on the keywords as effective content data, and counting corresponding microblog forwarding amount and/or engine search amount and/or forum posting amount and/or webpage browsing amount as quantitative indexes; the content data covering the keyword is taken as valid content data.
3. The method of claim 2, wherein determining the impact coefficient of heat of the current driving event from the content data comprises:
counting the number of content data including positive influence words and the number of content data including negative influence words in the content data, determining a positive influence coefficient of the current driving event according to the proportion of the content data including the positive influence words in all the content data, and determining a negative influence coefficient of the current driving event according to the proportion of the content data including the negative influence words in all the content data.
4. The method of claim 3, further comprising:
pre-establishing a positive influence word library and a negative influence word library, wherein the positive influence word library and the negative influence word library are two word libraries or two files stored in the same word library; the positive influence word lexicon comprises positive evaluation words commonly found in life vocabularies and network vocabularies, and the negative influence word lexicon comprises negative evaluation words commonly found in life vocabularies and network vocabularies.
5. The method according to claim 4, wherein the counting the number of content data including positive influence words and the number of content data including negative influence words in the content data comprises:
and counting the number of content data including positive influence words in the positive influence word bank and the number of content data including negative influence words in the negative influence word bank in the content data.
6. The method of claim 5, wherein determining a positive influence dimension and a negative influence dimension of a heat vector of the current drive event from the influence coefficients and the indicator data comprises:
determining the product of the quantity of content data including positive influence words in the positive influence word bank and the positive influence system in the content data after the number quantization as the positive influence dimension of the heat vector of the current driving event, and determining the product of the quantity of content data including negative influence words in the negative influence word bank and the negative influence system in the content data after the number quantization as the negative influence dimension of the heat vector of the current driving event.
7. The method of claim 6, wherein determining additional dimensions of the heat vector of the current driving event based on the weather of the surrounding environment of the current driving event and the location information of the current driving event comprises:
mapping the weather of the surrounding environment of the current driving event into a corresponding numerical value according to a preset mapping rule, wherein the numerical value is used as the weather dimension of the heat vector of the current driving event;
and mapping the distance between the position coordinate of the current driving event and a preset reference coordinate into a corresponding numerical value according to a preset mapping rule, wherein the corresponding numerical value is used as the position dimension of the heat vector of the current driving event.
8. A public population dispersion scheduling system based on AI popularity prediction, comprising:
the system comprises a content data acquisition module, a data processing module and a data processing module, wherein the content data acquisition module is used for acquiring content data associated with a current driving event from a public data source and counting a plurality of quantitative indexes corresponding to the current driving event according to the content data;
the common data source comprises:
one or more of a microblog server, a search engine server, a forum server and a web page server;
the obtaining content data associated with the current driving event from a common data source comprises:
searching content data including keywords related to the current driving event in the content data based on the keywords as effective content data, and counting corresponding microblog forwarding amount and/or engine search amount and/or forum posting amount and/or webpage browsing amount as quantitative indexes; taking the content data covering the keywords as effective content data;
an influence coefficient determining module, configured to determine an influence coefficient of the heat of the current driving event according to the content data, where the influence coefficient includes a positive influence coefficient and a negative influence coefficient; counting the data amount of the content data for positive evaluation, taking the proportion of the content data for positive evaluation to all the content data as a positive influence coefficient, counting the data amount of the content data for negative evaluation, and taking the proportion of the content data for negative evaluation to all the content data as a negative influence coefficient;
the normalization processing module is used for respectively carrying out normalization processing on the quantization indexes and quantizing the quantization indexes into index data of the same order of magnitude; respectively carrying out normalization processing on the quantitative indexes, and converting each quantitative index into a normalized score, wherein the score range is 0-100;
the influence dimension determining module is used for determining a positive influence dimension and a negative influence dimension of the heat vector of the current driving event according to the influence coefficient and the index data; after determining the positive influence coefficient and the negative influence coefficient of the heat degree of the current driving event and the normalized scores of a plurality of quantitative indexes corresponding to the current driving event, taking the product of the positive influence coefficient of the heat degree of the current driving event and the normalized scores of the quantitative indexes as the positive influence dimension of the heat degree vector of the current driving event, and taking the product of the negative influence coefficient of the heat degree of the current driving event and the normalized scores of the quantitative indexes as the negative influence dimension of the heat degree vector of the current driving event;
an additional dimension determining module, configured to determine an additional dimension of the heat vector of the current driving event according to the ambient environment information and the location information of the current driving event; after determining the positive influence dimension and the negative influence dimension of the heat vector of the current driving event, further determining an additional dimension of the heat vector of the current driving event by combining the ambient environment information and the position information of the current driving event;
the heat vector analysis module is used for integrating the positive influence dimension, the negative influence dimension and the additional dimension into a heat vector of the current driving event, analyzing the heat vector by using a pre-trained neural network model, and determining the personnel aggregation degree corresponding to the current driving event; after determining a positive influence dimension, a negative influence dimension and an additional dimension of the heat vector of the current driving event, integrating the dimensions into the heat vector of the current driving event; then analyzing the heat vector by using a pre-trained neural network model to determine the personnel gathering degree corresponding to the current driving event; the neural network model is a neural network model generated by training a BP neural network by taking a previously collected heat vector of a held driving event and a corresponding personnel aggregation degree as samples, taking the heat vector of the sample as input and the corresponding personnel aggregation degree as output by using the BP neural network, and outputting the personnel aggregation degree corresponding to the current driving event by taking the heat vector of the current driving event as input by using the generated neural network model;
and the dispersion scheduling module is used for executing corresponding public population dispersion scheduling according to the personnel aggregation degree.
9. The system of claim 8, wherein the impact coefficient determination module is specifically configured to:
counting the number of content data including positive influence words and the number of content data including negative influence words in the content data, determining a positive influence coefficient of the current driving event according to the proportion of the content data including the positive influence words in all the content data, and determining a negative influence coefficient of the current driving event according to the proportion of the content data including the negative influence words in all the content data.
10. The system of claim 9, further comprising:
the storage module is used for storing a pre-established positive influence word library and a pre-established negative influence word library; the positive influence word lexicon and the negative influence word lexicon are two lexicons or two files stored in the same lexicon.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910448060.2A CN110580565B (en) | 2019-05-27 | 2019-05-27 | AI heat prediction-based public population dispersion scheduling method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910448060.2A CN110580565B (en) | 2019-05-27 | 2019-05-27 | AI heat prediction-based public population dispersion scheduling method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110580565A CN110580565A (en) | 2019-12-17 |
| CN110580565B true CN110580565B (en) | 2020-10-27 |
Family
ID=68810989
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910448060.2A Active CN110580565B (en) | 2019-05-27 | 2019-05-27 | AI heat prediction-based public population dispersion scheduling method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110580565B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832912A (en) * | 2017-10-09 | 2018-03-23 | 中国电子科技集团公司第二十八研究所 | A kind of dense population risk analysis method and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933093B (en) * | 2015-05-19 | 2018-08-07 | 武汉泰迪智慧科技有限公司 | The monitoring of regional public sentiment and decision support system (DSS) based on big data and method |
| CN108133291B (en) * | 2017-12-22 | 2021-05-11 | 北京交通大学 | Correlation analysis method between social events and passenger traffic demands |
-
2019
- 2019-05-27 CN CN201910448060.2A patent/CN110580565B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832912A (en) * | 2017-10-09 | 2018-03-23 | 中国电子科技集团公司第二十八研究所 | A kind of dense population risk analysis method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110580565A (en) | 2019-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Crime prediction using Twitter sentiment and weather | |
| CN107577688B (en) | Original article influence analysis system based on media information acquisition | |
| CN111242493B (en) | Street quality evaluation method, device and system and storage medium | |
| US10936906B2 (en) | Training data acquisition method and device, server and storage medium | |
| CN110704572B (en) | Suspected illegal fundraising risk early warning method, device, equipment and storage medium | |
| WO2021004344A1 (en) | Data analysis-based risk identification method and related device | |
| CN111212383B (en) | Method, device, server and medium for determining number of regional permanent population | |
| Garcillán et al. | Sampling procedures and species estimation: testing the effectiveness of herbarium data against vegetation sampling in an oceanic island | |
| CN108595582B (en) | Social signal-based identification method for disastrous weather hot events | |
| CN106095939B (en) | The acquisition methods and device of account authority | |
| CN113407729A (en) | Judicial-oriented personalized case recommendation method and system | |
| CN109902129A (en) | Insurance agent's classifying method and relevant device based on big data analysis | |
| CN113378668A (en) | Method, device and equipment for determining accumulated water category and storage medium | |
| CN110580565B (en) | AI heat prediction-based public population dispersion scheduling method and system | |
| CN116384844B (en) | Decision method and device based on geographic information cloud platform | |
| CN117291428A (en) | Enterprise management APP-based data background management system | |
| CN107801418B (en) | Floating population estimation device and method using map search record | |
| CN115858598A (en) | Enterprise big data-based target information screening and matching method and related equipment | |
| Ramírez et al. | Accident risk detection in urban trees using machine learning and fuzzy logic | |
| CN114817518A (en) | License handling method, system and medium based on big data archive identification | |
| CN114495137A (en) | Bill abnormity detection model generation method and bill abnormity detection method | |
| Martinig | Habitat suitability modeling for mink passage activity: A cautionary tale | |
| CN113888600A (en) | Trajectory determination method and apparatus, electronic device and computer-readable storage medium | |
| Loman et al. | Landscape capability predicts upland game bird abundance and occurrence | |
| CN113971213A (en) | Smart city management public information sharing system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200916 Address after: 200232 floor 18, building 2, No. 277, Longlan Road, Xuhui District, Shanghai Applicant after: LIGHT CONTROLS TESILIAN (SHANGHAI) INFORMATION TECHNOLOGY Co.,Ltd. Address before: 100027 West Tower 11 floor, Kai Hao building, 8 Xinyuan South Road, Chaoyang District, Beijing. Applicant before: Terminus(Beijing) Technology Co.,Ltd. Applicant before: LIGHT CONTROLS TESILIAN (SHANGHAI) INFORMATION TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |