FR3069686A1 - METHOD AND SYSTEM FOR EVALUATING A SECURITY RISK RELATING TO A GEOGRAPHICAL REGION - Google Patents

Abstract

The invention relates to a method for evaluating a security risk relating to a geographic region (R) in which an individual is able to move, the method comprising a generation step (29) by a server (3) of a contextualized value (Vc) of risk quantization relative to said region (R) and a transmission step (33) by an electronic device (2) able to exchange (13) data with said server (3), a warning message to said user including this quantized context value (Vc).

Description

METHOD AND SYSTEM FOR ASSESSING A SECURITY RISK RELATING TO A GEOGRAPHICAL REGION

The present invention relates to a method and a system for evaluating a security risk relating to a geographic region.

The invention also relates to an electronic device of this system carried by a user likely to move in such a region as well as on a computer program.

In the state of the art, methods are known for assessing security risks linked to so-called "risky" events. Such methods are generally limited to certain types of events such as those linked for example to natural disasters of the cyclone or hurricane type which are events which can harm the security of the population. Such methods generally make it possible to quantify the risks for the populations concerned linked to the effects of the occurrence of such events in identified geographic regions where these populations are present.

The present invention aims to propose an alternative design to existing ones and to meet an increasingly pressing need linked to an increasing increase in risk events that are often different and difficult to predict.

To this end, the invention relates to a method for evaluating a security risk relating to a geographic region in which an individual is likely to move, the method comprising a step of generation by a server of a contextualized value of risk quantification relating to said region and a step of transmission by an electronic device capable of exchanging data with said server, of a warning message to said user comprising this contextualized quantification value, said generation step comprising the sub-steps following:

- location of said region from geolocation coordinates generated and transmitted by the electronic device carried by said user to said server;

- estimation of a global risk quantification value relating to said localized region from a determination of a probability of occurrence of at least one risk event in this localized region, and

- determination of the contextualized risk quantification value from the application to said global quantification value of an adjustment value estimated on the basis of:

• at least one element of statistics related to a criterion of data of profile of the user and of said at least one risk event, and • a time value comprising a time parameter notably relating to an instant of passage of the user in said localized region.

In other embodiments:

- the determination sub-step comprises a phase of selection from the server database of at least one element of statistics relating to at least one identified risk event;

- the determination sub-step includes a selection phase in the database of the profile data server of the user of the electronic device;

the determination step comprises a phase of recovering, in intelligence elements, a statistical element of a statistical value relating to a common profile criterion if such a common criterion is identified in the profile data of the user and in these elements of information relating to the statistical element, said statistical value participating with the time value in determining the adjustment value;

- the method includes a step of automatic management of the archiving of data relating to risk events;

- the method includes a step of automatic management of the archiving of data relating to statistical elements linked to risk events stored in the database;

the transmission step comprises a sub-step of generation by the processing unit of the server of the message comprising geographic coordinates making it possible to position in the localized region at least one risk event identified during the estimation sub-step the step of generating the contextualized risk quantification value;

the transmission step comprises a substep of generation by the processing unit of the server of the message comprising geographic coordinates making it possible to divide the localized region into different zones which may present different security risks;

- the transmission step comprises a substep of generation by the processing unit of the server of the message comprising a descriptive text summarizing the nature of said at least one identified risk event.

The invention also relates to a system for evaluating a security risk relating to a geographic region in which an individual is likely to move, implementing such an evaluation method, the system comprising an electronic device and a server. able to exchange data between them.

Advantageously, the system comprises sources of information data likely to include data of information of risk events which took place in the world and data of statistical information relating to these events.

The invention also relates to an electronic device for / adapted to an evaluation system comprising a processing unit, a communication module and a module for transmitting a warning message linked together so as to participate in the setting implementing this evaluation process.

Advantageously, the transmission module comprises a display interface and / or a sound interface capable of broadcasting such a message.

The invention also relates to a computer program comprising program code instructions for the execution of the steps of this method when said program is executed by processing units of this server and of this electronic device included in this system. .

Other advantages and characteristics of the invention will appear better on reading the description of a preferred embodiment which will follow, with reference to the figures, produced by way of indicative and nonlimiting example: FIG. 1 is a schematic representation of a security risk assessment system relating to a geographic region in which an individual is likely to move, according to the embodiment of the invention;

FIG. 2 represents a flow diagram of a process for evaluating the security risk relating to this region, according to the embodiment of the invention;

FIG. 3 is a flow diagram relating to steps for automatic management of the archiving of data relating to risk events and to statistical elements of said method, according to the embodiment of the invention, and FIG. 4 illustrates a graphic representation comprising a message comprising said security risk generated by a processing unit of an electronic device of the system and broadcast by a transmission module of this device, according to the embodiment of the invention.

FIG. 1 shows a system 1 for evaluating a security risk relating to a geographic region R in which an individual is likely to move.

In this context, the security risk is a possible danger, more or less foreseeable, inherent in a situation or an activity and which is engendered by an accidental or voluntary event likely to endanger the security of an individual by causing an attack to his health or to his physical or mental integrity and which could in certain cases lead to his death.

This system 1 includes an electronic device 2, a server 3, in particular a remote server, and sources of information data 4 comprising event information data 5a and statistics information data 5b.

In this system 1, the electronic device 2 also called a mobile electronic device, must be understood as being a portable or even portable electronic device. Such an electronic device 2 can be a connected watch, a remote computer, a smartphone or even a digital tablet. Such a device 2 is therefore in the context of the implementation of the invention carried and / or in the possession of the user so in particular that the latter can be located geographically and receive warning messages relating to the related security risk with the region R where it is located or where it wishes to go later.

This electronic device 2 includes, without limitation and not being exhaustive:

a man-machine interface 6a comprising input elements such as a touch screen or a keyboard or even a voice control system;

-a transmission module 6b including a warning message comprising:

a display interface;

a sound interface comprising at least one speaker; a processing unit 7 comprising hardware and / or software elements;

at least one receiver of mechanical and / or sound vibrations which is an electroacoustic transducer which is capable of converting a sound / acoustic signal into an electrical signal, such a receiver corresponds for example to an omnidirectional microphone;

-a geolocation module 6c for example of the GPS type (acronym for “Global Positioning System”), GLONASS or BEIDOU, and

a communication module 6d compatible with that of the server 3 and capable of implementing different wired or wireless communication protocols.

This electronic device 2 is capable of exchanging data 13 with the server 3. This server 3 includes, without limitation and without limitation:

- a database 8;

a processing unit 10 comprising hardware and / or software elements;

a communication module 11 compatible with the module 6d of the electronic device 2 and capable of implementing different wired or wireless communication protocols, and

- a communication interface 12 with the data sources 4.

The server 3 comprising the database 8 in which digital files 9a, 9b, 9c are recorded, also includes software executed by the processing unit 10 which is capable of carrying out the management of this database 8. This database 8 comprises three categories of digital cards 9a, 9b, 9c including so-called event cards 9a and statistics 9b which are described below as well as profile cards 9c.

These profile sheets 9c constitute profile data of the users of this system and each include profile data criteria. For a 9c profile sheet, the criteria include, without limitation and not being exhaustive: surname, first name, age, language, gender, sexual affiliation, ethnic affiliation, religious confession, nationality, profession, etc.

Furthermore, it will be noted that the hardware and / or software elements of the processing units 7, 10 of the electronic device 2 and of the server 3 more specifically comprise at least one microprocessor cooperating with memory elements. These processing units 7, 10 are capable of executing program code instructions for the implementation of the computer program.

In this system, the server 3 is connected via its communication interface 12 to the sources of information data 4. These sources of data 4 are likely to include data of information of risk events 5a which took place in the world and statistical information data 5b relating to these events and to events already archived. Such data sources 4 can include in a nonlimiting and / or exhaustive manner flows with informative content relating to:

- emails generated, for example, by a newsletter service, RSS feeds (acronym for "really simple syndication" meaning "really simple syndication"), particularly news or even spreadsheets of CSV / API type;

- social media bringing together different websites and social functions of the web such as, for example, blogs, discussion forums or wikis, etc ...;

- social networks whose primary purpose is to put users in touch with each other, such as Facebook ™, Twitter ™, Google + ™, etc.

- media such as the written press, radio and television, written press and / or audiovisual media broadcasting news or thematic information;

- administrations and / or private, governmental, supra-governmental, national, supranational or even international organizations intervening for example in the fields of security and / or protection of populations, health, meteorology, ecology and the environment, and being able to communicate for example information data relating to risky events in the world such as attacks, natural disasters (earthquake, volcanoes, etc.), climatic disasters (heat waves, torrential rain, floods, drought, storms, tropical cyclones, hurricanes, tornadoes, etc.), health disasters, industrial disasters (pollution, oil spill, etc.), epidemic disasters ( pandemic influenza, SARS, plague, rabies, chikungunya, dengue, smallpox, Ebola virus), popular / civil uprisings, demonstrations, etc ...;

- official statistical institutes capable in particular of delivering statistical values relating to the effects resulting from these events, in particular on a segment of the population affected by these effects.

In this context, the population segment must be understood as constituting a part of the population sharing one or more criteria of profile data such as for example gender, nationality, sexual affiliation or even religious affiliation, etc. ..

It is understood that these data sources 4 can be, without limitation and / or not exhaustive, flows of informative content making it possible to identify risky events able to participate in the assessment of security risks in different geographic regions R given as well as statistical values that concern them.

With reference to FIGS. 2 to 4, the invention also relates to a method for evaluating the security risk relating to the geographic region R in which an individual is likely to move. It can here be an R region in which the user is already present or an R region in which he is not yet present but plans to go / cross later.

Such a method includes an automatic management step 14 for archiving data relating to risk events. The data relating to each risk event is stored in the database 8 of the server 3 in the event file 9a. In other words, this database 8 includes a plurality of event files 9a which can be grouped / classified in this database 8 by type of event or according to their geographic location. This step 14 of the method which is implemented by the processing unit 10 of the server 3, in particular provides for the execution of the database management software 8 to perform operations of creation, addition, deletion, updating, modification of the data relating to these events archived in the database 8 of this server 3. As we have already mentioned, a risk event allows us to participate in quantifying the security risk for this individual before or being brought to move in a defined geographic region R. This risky event is an essentially unpredictable external element that can have effects on the security of the individual by altering their physical, physiological and / or mental integrity. In other words, this event can then jeopardize the security of this individual by causing damage to his health or his physical or mental integrity or even his death. This event can for example be non-limiting and non-exhaustive of different types, it can for example be: a malicious act carried out by other individuals, a terrorist act, a criminal act, an act of sexual violence, of an kidnapping, of a popular / civil uprising, of a health catastrophe, of a demonstration, of an attack, of a physical assault, of a road accident, of '' a climatic phenomenon, a health disaster, an industrial disaster, an epidemic disaster, etc ...

In this method, this step 14 comprises a substep of connection 15 to the sources of information data 4 comprising event information data 5a which are therefore likely to understand the events at risk. This connection sub-step 15 includes a phase of collecting and / or aggregating information data coming from these sources 4 and which include the risk events which took place in the world, in order to archive them in the server database 3.

It will be noted that this information data coming from these data sources 4 is for the most part in a textual format, that is to say in a dematerialized textual form. However, some of them can be in audio format and then be automatically converted to text format by the implementation by the processing unit 10 of the server 3 of at least one format conversion / transcription algorithm. audio in text format and / or at least one speech recognition algorithm.

This step 14 further comprises a sub-step of automatic processing 16 of this information data in particular in text format. During this sub-step 16, the processing unit 10 executes at least one automatic NLP natural language processing algorithm implementing stochastic, probabilistic and / or statistical methods as well as automatic learning and "data" processes. mining ”(meaning in French extraction of knowledge from data). Such a sub-step 16 can for example provide for information data constituting a text comprising a set of words / terms / expressions / numbers / numbers, a reorganization / restructuring / modification of these words / terms / expressions / numbers / numbers of this text in order to allow optimal realization of a detection of at least one risk event and / or of extraction of information relating to this at least one risk event. It will be noted that this sub-step 16 is capable of carrying out semantic processing operations (automatic translation, automatic generation of texts / words, reformulation and paraphrasing, lexical disambiguation, orthographic correction, conversational agents, etc.), syntactic (lemmatization , morphology, morpho-syntactic labeling, sentence delimitation, word separation, etc.) and / or lexical. During this sub-step 17, a sentiment analysis algorithm otherwise called "opinion mining" can also be implemented by the processing unit 10 in order to analyze this information data.

Then, this step 14 includes a detection sub-step 17 in this information data thus processed of at least one risk event. During this sub-step 17, the processing unit 10 executes at least one machine learning algorithm ML (known in English as "machine learning") or statistical learning.

During this sub-step 17, the processing unit 10 is then capable, by executing this algorithm, of automatically detecting words / terms / expressions / numbers / figures relating to at least one risk event and this, from data information relating to such events and already archived in the database 8 of the server 3 and / or dictionaries made up of thousands of words / terms / expressions / numbers / figures in all the languages related to such events. It will be noted that during this sub-step 17, the processing unit 10, by implementing these algorithms, is able to differentiate between risk events and other different events which are not.

Once said at least one event has been detected, this step 14 includes a sub-step for extracting 18 this information data, information elements relating to said at least one detected event. These elements of information for a given risk event relate essentially to the detailed characteristics linked to this event. These pieces of information include, for example, in a non-limiting and non-exhaustive manner:

- the type of event (criminal act, terrorist act, natural disaster, etc.);

- geographic location (geographic coordinates, street, city, country, continent, etc.)

- the date and time when this event occurred;

- the duration of this event;

- number of people who died;

- number of people not fatally injured;

- the actor (s) (individual at the origin of the event if it relates for example to an act committed by this individual) • number of actors;

• ethnicity of this or these actors;

• gender of this or these actors;

- type of weapon (if the event relates for example to an act committed by an individual);

- a descriptive text TXT1, TXT2, TXT3, TXT4, TXT5 summarizing the nature of the identified event;

- source of information relating to this event.

To carry out such a sub-step 18, the processing unit 10 of the server 3 implements at least one machine learning algorithm ML and / or at least one machine processing algorithm for natural language NLP mentioned above.

In a variant, of the automatic management step 14, this extraction sub-step 18 can be directly implemented by the processing unit 10 as soon as the data sources 4 comprise streams with information content relating to files CSV or API type spreadsheets, without the need to implement the processing 16 and detection 17 substeps of this step 14. In such a context, during this extraction substep 18 , the processing unit 10 of the server 3 can then implement at least one fuzzy logic algorithm FL (known in English as "fuzzy logic") based on the mathematical theory of fuzzy sets of Lotfi Zadeh and which is susceptible in particular to implement a probabilistic logic.

This step 14 then comprises a sub-step for archiving 19 said at least one event with the information elements associated with it in the event file 9a included in the database 8. This sub-step 19 then comprises a detection phase 34 of duplication of an event file 9a, during which the processing unit 10 of the remote server executes at least one machine learning algorithm ML and / or at least one fuzzy logic algorithm FL mentioned above in order to identify if such an event is not already archived in database 8 with the information elements associated with it. If this is the case, during an update phase 35 of the corresponding event file 9a, the processing unit 10 of the server 3 determines to what extent the items of information archived in the database 8 relating to said event must be completed / updated from those recently extracted from the information data for this event. If, on the contrary, this event is not already archived in the database 8, the processing unit 10 during a creation phase 36 of a new event record 9a in the database 8 , then generates in the latter a new event sheet 9a then comprising the type of risk event detected and the information elements associated with it.

This method also includes a step of automatic management of the archiving 20 of the archiving of data relating to statistical elements linked to the risk events stored in the database 8. Such a step 20 provides for archiving said at least a statistical element relating to the corresponding risk event in a statistical file 9b included in the database 8 of the server 3. In other words, this database 8 comprises a plurality of statistical files 9b linked to data sheets 'corresponding event 9a, there may moreover be several statistical files 9b linked to the same event file 9a.

This step 20 comprises a sub-step of connection 21 to the sources of information data 4 comprising statistical information data 5b relating to risk events. These statistical information data may include statistical values which are not limited to a segment of the population affected by the effects resulting from a risky event in a given geographic region R. Remember that the population segment must be understood as being a part of the population that shares one or more profile criteria such as gender, nationality, sexual affiliation or religious affiliation, etc.

It is understood under these conditions that these data sources 4 can be non-limiting and / or non-exhaustive flows of informative content participating in identifying statistical elements relating to risk events to allow in particular to carry out the security risk assessment in different given geographic areas.

This statistical information data coming from these data sources 4 is preferably in a text format. However, they can be in audio format and then be automatically converted to text format by the implementation by the processing unit 10 of the server 3 of at least one algorithm for converting / transcribing an audio format into a format text and / or at least one speech recognition algorithm.

Thereafter, this step 20 includes an automatic processing sub-step 22 of this statistical information data which is in text format and a detection sub-step 23 in this statistical information data thus processed of at least one element. statistics relating to at least one risk event. These processing 22 and detection 23 substeps are substantially similar to the processing 16 and detection 17 substeps of the automatic management step 14 of the archiving of data relating to risk events.

This step 20 then includes a sub-step for extracting this statistical information data from intelligence elements relating to said at least one detected statistical element. These pieces of information include, for example, in a non-limiting and non-exhaustive manner:

- the type of event concerned by the element of statistics (criminal act, terrorist act, natural disaster, etc.);

- the geographic location relating to this statistical element (geographic coordinates, street, city, country, continent, etc.);

- date and time;

- segment of the population victim of the event;

- period during which this event is likely to take place;

- a profile criterion of a user concerned by the statistical value identified from the segment of the population victim of the event;

- statistical value relating to the profile criterion of a user concerned by the statistical value identified from the segment of the population victim of the event;

- actors (individual at the origin of the event if it relates for example to an act committed by this individual).

For the realization of such a sub-step 24, the processing unit 10 of the server 3 implements at least one automatic learning algorithm ML and / or at least one automatic processing algorithm of natural language NLP which each have previously mentioned.

In a variant of the automatic management step 20, this extraction sub-step 24 can be directly implemented by the processing unit 10 as soon as the data sources 4 comprise streams with information content relating to CSV or API type spreadsheet files, without it being necessary to implement the processing 22 and detection 23 substeps of this step 20. In such a context, during this extraction substep 24, the processing unit 10 of the server 3 can then implement at least one fuzzy logic algorithm FL.

This step 20 then comprises a sub-step of archiving said at least one statistical element relating to said at least one risk event with the intelligence elements associated with it. This sub-step 25 comprises a detection phase 36 of duplication of a statistical sheet 9b during which the processing unit 10 of the server executes at least one machine learning algorithm ML and / or at least one logic algorithm fuzzy FL mentioned above in order to identify if such a statistical element is not already archived in the database 8 with the intelligence elements associated with it. If this is the case, during an update phase 37 of the corresponding statistical file 9b, the processing unit 10 of the server 3 determines to what extent the items of information archived in the database 8 relating to said audit statistical element should be completed / updated from those recently extracted from statistical information data for this element. If, on the contrary, this element of statistics is not already archived in the database 8, the processing unit 10 during a phase of creation 38 of a new statistical record 9b in the database 8, then generates in this database 8 this new statistical sheet 9b.

This method also includes a step 26 of transmitting a request from the electronic device 2 to the server 3 aimed at obtaining a contextualized value Vc of risk quantification relating to the region R in which the individual is likely to move. Such a contextualized value Vc contributes to evaluating the security risk relating to the region R crossed by the user wearing the electronic device 2 and can for example correspond here to a percentage quantifying this security risk.

This step 26 includes a sub-step 27 of determining geolocation coordinates relating to the region R for which a contextualized value Vc of risk quantification is expected. During this sub-step 27, these geolocation coordinates can be generated by the processing unit 10 which is connected to the geolocation module of the electronic device 2, these coordinates thus being relative to the geographic location of the electronic device 2 and therefore of the user in possession of the latter, that is to say here in the region R for which the contextualized value Vc of risk quantification is expected. In a variant, these geolocation coordinates can be generated by the processing unit 10 which is connected to the man-machine interface 6a of the mobile device. In this variant, the user can thus interact with the electronic device 2 in order to select the region R for which he would like to know the contextualized value Vc of risk quantification. In this variant, a geographic map comprising said region R can be displayed by the message transmission module 6b here the display interface (screen) of the electronic device 2 and the man-machine interface 6a here the touch surface of said screen can allow the user to select this region R and thus trigger the generation of geolocation coordinates by the processing unit 10.

This step 26 then comprises a substep for generating 28 of said request comprising the geolocation coordinates and an element of identification of the electronic device 2 and / or of the user of this device 2. This request can also include a parameter of time T1 in particular relative to the time of passage of the user in said localized region R. In other words, this time parameter T1 can include a date or a date and a time, that is to say the precise moment, where the electronic device 2 and therefore the user will be / is present in the region R for which he wishes to know the contextualized value Vc of risk quantification. This time parameter T1 may correspond to the time (date and / or time) when the request is generated to be transmitted to the server 3 or even at a later time when the user plans to pass through said region R.

This method subsequently comprises a step 29 of generating the contextualized value Vc of risk quantification relating to said region R. This step 29 comprises a sub-step of locating 30 of said region R from geolocation coordinates generated and transmitted by the electronic device 2 worn by said user.

This step 29 then comprises a sub-step of estimating 31 of a global value Vg of risk quantification relating to said localized region R from a determination of a probability of unfolding of at least one event at risk in this localized region R. During this sub-step 31, the processing unit 10 of the server 3 then generates a request aiming to identify at least one risk event archived in this database 8 which took place in the past in an R region comprising these location coordinates and whose limits of said R region are defined within a configurable radius of a few meters to several hundred kilometers around these geolocation coordinates, depending on the relevance and the choice of the location 'user.

Such a request includes:

- the geolocation coordinates received from the electronic device 2, and

- a time interval T2 aimed at limiting temporally the search for at least one risk event over a defined period (day, week, month, year) which ends on the date and / or the date and the time included in the time parameter T1 received from the electronic device 2. It will be noted that this time interval T2 can correspond to a few minutes, hours, months or even a few years preceding the date and / or the date and time included in the parameter time T1.

This request generated by the processing unit 10 of the server 3 is then transmitted to the database 8. In response to the latter, the database 8 transmits to said processing unit 10 at least one risk event and in particular the corresponding event sheet 9a comprising the information elements relating to this risky event.

In fact, during this sub-step 31, the processing unit 10 of the server 3 evaluates the global value Vg of risk quantification from calculation operations implementing a first equation relating to a discrete probability law such that the following fish law:

λ * β ~ ^λ k

P (k) = with λ = - where:

- k corresponds to the number of occurrences of a type of event identified in the database 8, indeed an event can occur in the time interval T2, 0 times (never), 1 time, 2 times, n times etc ... (ne H);

- e is the base of the exponential (2,718 ...);

- k! is the factorial of k;

- A is a strictly positive real number relative to the average of a type of event that has occurred k times in the time interval T2.

The processing unit 10 determines the overall value Vg of risk quantification from a second following equation:

Vg = 100 x (1 - P (kf) where:

- P (k) corresponds to the result obtained for a type of event of first relative equation a discrete probability law such as the fish law;

- I is the time interval between the date and / or the date and time of the event or the last event of the same type if there are several, and the date or date and the time included in the time parameter T1, that is to say the precise moment, when the electronic device 2 and therefore the user will be / is present in the localized region R.

This overall value Vg is preferably expressed as a percentage of the risk of a reiteration of such a type of event in said localized region R.

Note that when several risk events are identified and transmitted by the database 8 to the processing unit 10, the latter classifies them by grouping them by groups of type of risk event, a global value Vg is then calculated for each of these groups. For example, for a first group of events comprising three events of a first type E1, E2, E3 and a second group of events comprising two events of a second type F1, F2, first and second global values Vg1, Vg2 are then respectively determined for the first and second groups of events. Thus, in such a case, the processing unit 10 then determines these first and second global values Vg1, Vg2 by implementing the following equation:

Vgl = 100 x (1 - PUR) ¹ ) and Vg2 = 100 x (1 - PlÇk) ¹ ) where P1 (k) and P2 (k) correspond to the results obtained respectively for the first and second groups of events of different type .

The generation step 29 then comprises a substep for determining 32 of a contextualized value Vc of risk quantification from the application to said global value Vg of quantification of an adjustment value Va estimated from '' at least one element of statistics related to a criterion of user profile and said at least one risk event and time parameter T1 in particular relating to a time of passage of the user in said localized region

R.

This sub-step 32 includes a selection phase in the database 8 of the server 3:

- at least one element of statistics relating to said at least one identified risk event, and

- profile data of the user of the electronic device 2.

During the course of this phase, a request is then generated by the processing unit 10 of the server 3 aimed at:

- identify in the database 8 at least one statistical element relating to said at least one risk event identified for said localized region R during the estimation substage 31 of the global value Vg of risk quantification, and

- select in the database 8 the profile data of the user of the electronic device 2.

Such a request then comprises a reference element making it possible to identify in the database 8 said at least one statistical element relating to said at least one risk event identified during the estimation sub-step 31, and the element of identification of the electronic device 2 and / or of the user of this device transmitted to the server 3 and therefore to its processing unit 10 during the generation sub-step 28 of the step of transmission 26 of a request from the electronic device 2 to server 3.

This request generated by the processing unit 10 of the server 3 is then transmitted to the database 8. In response to the latter, the database 8 transmits to said processing unit 10 at least one element of statistics and in particular the corresponding statistical sheet 9b as well as the user profile data here the profile sheet 9c.

This sub-step 32 then comprises a phase of identification of a common profile criterion in the user profile data and in the information elements relating to a statistical element. During this phase, the processing unit 10 identifies in the statistical sheet 9b and in particular in the information elements included in this sheet 9b, the criterion of profile data of a user concerned by the statistical value. Thus, the processing unit 10 performs a processing of the profile data of the user by performing operations of analysis of the profile sheet 9c in order to detect a correspondence between the profile data criterion identified in the statistical sheet 9b and a criterion present in the profile sheet 9c of the user wearing / in possession of the electronic device 2.

This sub-step 32 subsequently comprises a phase of retrieving in the intelligence elements of the statistical element the statistical value relating to the common profile criterion if such a common criterion is identified in the profile data of the user and in the intelligence elements relating to the statistical element. During this phase, the processing unit 10 then determines the adjustment value Va from the statistical value Vs relative to the identified profile criterion, present in the statistical file 9b and / or from a relative temporal value Vt to the time parameter T1 corresponding to an instant of passage of the user in said localized region R. More specifically, the processing unit 10 determines this adjustment value Va by implementing an equation relating to a probability law as for example the fish law mentioned above or other laws of probability known from the state of the art. In this context, the processing unit 10 performs calculation operations implementing this probability law as well as the statistical value Vs relating to the identified profile criterion and / or the time value Vt relating to the time parameter T1. It will be noted that this time value Vt is a function of the profile criterion identified in the statistical file 9b, namely for example criteria relating to an individual entering into the definition of a segment of the population, that is to say for example gender, nationality, etc.

It will be noted that when several risk events have been identified during the estimation sub-step 31 of the generation step 29 of the contextualized value Vc of risk quantification and that they are grouped by groups of event type at risk, an adjustment value Va is then calculated for each of these groups. In the context of the example mentioned above, first and second adjustment values Va1, Va2 are determined respectively for the first group of events comprising the three events of a first type E1, E2, E3 and the second group d 'events comprising the two events of a second type F1, F2. In addition, it should be noted that several statistical values can be identified for the same type of event E1, E2, E3.

The adjustment value Va can also be called a risk multiplier factor and is applied to the overall quantification value Vg in order to obtain the contextualized risk quantification value Vc of the localized region R by implementing the following equation by the processing unit 10 of the server 3:

Vc = Vg x Va

It will be noted that when several risk events have been identified during the estimation sub-step 31 of the generation step 29 of the contextualized value Vc of risk quantification and that they are grouped by groups of event type at risk, the contextualized value Vc of risk quantification is then equal to the sum of the contextualized values Vc1, Vc2 determined for each group of type of event. Continuing from the previous example, first and second contextualized values Vc1, Vc2 are determined respectively for the first group of events comprising the three events of a first type E1, E2, E3 and the second group of events comprising the two events of a second type F1, F2. Thus, in such a case, the processing unit 10 then determines the contextualized value Vc by implementing the following equation:

Vc = Vc1 + Vc2 = (Vg1 x Va1) + (Vg2 x Va2)

It is understood that this adjustment value Va makes it possible to take into account, within the framework of this process, in particular statistical data relating to security risks which may be present in the region R traversed by the user, these statistical data applying. specifically to the user by being related to at least one profile data of the latter and / or at the time of the day determined by the parameter T1 where this user is brought to cross this region R.

The method also includes a step 33 of transmitting a warning message to said user comprising said contextualized value Vc of quantization. This step 33 includes a sub-step for generation by the processing unit 10 of the server 3 of a message comprising said contextualized value Vc. This message generated by this processing unit 10 can also include:

- geographic coordinates allowing to:

• position in the localized region R at least one risk event identified during the estimation sub-step 31 of the generation step 29 of the contextualized value Vc of risk quantification, and • divide the localized region R into different areas that may present different security risks;

- a descriptive text TXT1, TXT2, TXT3, TXT4, TXT5 summarizing the nature of said at least one identified event.

It is understood that this generated message may include other information data which should make it possible to define the security risk more precisely by providing additional information on this subject.

This message is then transmitted via the communication module 11 of the server 3 to the electronic device 2 and in particular to the communication module 6d of this device 2.

This step 33 then includes a sub-step of broadcasting this message to the user. During this substep, the processing unit 7 of the electronic device 2 which is connected to the latter's transmission module 6b, controls the broadcasting of this message via the display interface and / or the audio interface of this transmission module 6b.

With reference to FIG. 4, this message can therefore be integrated into a graphic representation 40 generated by the processing unit 7 and broadcast on the display interface of the electronic device 2. This graphic representation 40 can comprise three parts P1, P2, P3.

The first part P1 of this graphic representation 40 comprises a criterion C of user profile data such as his name or his first name, as well as the time parameter T1 relating to the time of passage of the user in the region localized R and the contextualized value Vc of risk quantification determined in particular according to the geographic location of the electronic device 2 and therefore of the user in the localized region R. In this part an evolution over time of the contextualized value Vc can be generated under the form for example of a histogram.

The second part P2 of this graphic representation 40 comprises a representation of a geographic map comprising:

- the localized region R which is divided into several zones Z which may present different security risks,

a visual identification element 41 of the real-time or envisaged positioning of the electronic device 2 and therefore of the user in the localized region R, and

- two groups of events of different types comprising respectively the three events E1, E2, E3 and two events F1, F2.

The third part of this graphic representation 40 includes descriptive texts TXT1, TXT2, TXT3, TXT4, TXT5 relating respectively to events E1, E2, E3 F1, F2 present in the localized region R represented on the map.

The invention also relates to a computer program comprising program code instructions for the execution of these steps 14 to 33 of the method when said program is executed by processing units 7, 10 of the server 3 and of the electronic device 2 both included in this security risk assessment system 1.

Claims (10)

1. Method for assessing a security risk relating to a geographic region (R) in which an individual is likely to move, the method comprising a step of generation (29) by a server (3) of a contextualized value (Vc) of risk quantification relating to said region (R) and a step of transmission (33) by an electronic device (2) capable of exchanging (13) data with said server (3), of a message of warning said user comprising this contextualized quantization value (Vc), said generation step (29) comprising the following substeps: location (30) of said region (R) from geolocation coordinates generated and transmitted by the electronic device (2) carried by said user to said server (3); estimation (31) of a global risk quantification value relative to said localized region (R) from a determination of a probability of occurrence of at least one risk event in this localized region (R), and determination (32) of the contextualized value (Vc) of risk quantification from the application to said global quantification value of an adjustment value estimated on the basis of: • at least one element of statistics related to a criterion of data of profile of the user and of said at least one risk event, and • a time value comprising a time parameter notably relating to an instant of passage of the user in said localized region (R). 2. Method according to the preceding claim, characterized in that the determination sub-step (32) comprises a selection phase in the database (8) of the server (3): at least one element of statistics relating to at least one identified risk event, and profile data of the user of the electronic device (2). 3. Method according to any one of the preceding claims, characterized in that the determining step (32) comprises a phase of recovery in intelligence elements of a statistical element of a statistical value relating to a common criterion profile if such a common criterion is identified in the user profile data and in these elements of information relating to the statistical element, said statistical value participating with the time value in determining the adjustment value. 4. Method according to any one of the preceding claims, characterized in that it comprises: an automatic management step (14) for the archiving of data relating to risk events; an automatic management step (20) for the archiving of data relating to statistical elements linked to risk events stored in the database (8). 5. Method according to any one of the preceding claims, characterized in that the transmission step (33) comprises a substage of generation by the processing unit (10) of the server (3) of the message comprising: geographic coordinates allowing to: • position at least one risk event identified in the localized region (R) during the estimation sub-step (31) of the generation step (29) of the contextualized value (Vc) of risk quantification; • divide the localized region (R) into different zones that may present different security risks; a descriptive text (TXT1, TXT2, TXT3, TXT4, TXT5) summarizing the nature of said at least one identified risk event. 6. evaluation system (1) for a security risk relating to a geographic region (R) in which an individual is likely to move, implementing an evaluation method according to any one of the preceding claims, the system (1) comprising an electronic device (2) and a server (3) capable of exchanging (13) data between them. 7. System (1) according to the preceding claim, characterized in that it comprises sources of information data (4) capable of including information data of risk events (5a) which took place in the world and statistical information data (5b) relating to these events. 8. Electronic device (2) for an evaluation system (1) according to any one of claims 6 and 7, comprising a processing unit (7), a communication module (6d) and a transmission module (6b ) of a warning message linked together so as to participate in the implementation of the evaluation method according to any one of the preceding claims 1 to 5. 9. Electronic device (2) according to the preceding claim, characterized in that the transmission module (6b) comprises a display interface and / or a sound interface capable of broadcasting such a message. 5 10. Computer program comprising program code instructions for the execution of the steps of the method according to any one of claims 1 to 5 when said program is executed by processing units (7, 10) of the server (3 ) and of the electronic device (2) according to any one of claims 8 and 9 included in the system (1) according to any one of claims 6 and 7.