CN115018215B

CN115018215B - Population residence prediction method, system and medium based on multi-modal cognitive atlas

Info

Publication number: CN115018215B
Application number: CN202210946542.2A
Authority: CN
Inventors: 张广志; 成立立; 于笑博; 刘畔青
Original assignee: Beiling Rongxin Datalnfo Science and Technology Ltd
Current assignee: Beiling Rongxin Datalnfo Science and Technology Ltd
Priority date: 2022-08-09
Filing date: 2022-08-09
Publication date: 2022-11-25
Anticipated expiration: 2042-08-09
Also published as: CN115018215A

Abstract

The embodiment of the application provides a population residence prediction method, a population residence prediction system and a population residence prediction medium based on a multi-mode cognitive map. The method comprises the following steps: the method comprises the steps of constructing a multi-modal cognitive map of a region intelligent body according to region data, establishing a primary region appearance cognitive system, carrying out multi-modal population data recognition and population attribute and region parking event relation extraction on the established population multi-modal data based on the multi-modal cognitive map of the region intelligent body, carrying out population attribute linkage and cognitive fusion on the extracted population data multi-modal to obtain population data multi-modal cognition, and carrying out cognitive processing according to a cognitive map and a preset logical reasoning rule to predict population of a residential region; therefore, the multi-modal cognition map of the regional intelligent agent is constructed to identify and extract the attributes and event relations of the multi-modal population data, the attribute linkage and cognition fusion are carried out on the extracted multi-modal population to obtain the multi-modal cognition of the population data, and the cognition processing is carried out according to the cognition map and the rule to predict the residential population in the region.

Description

Population residence prediction method, system and medium based on multi-modal cognitive atlas

Technical Field

The application relates to the field of knowledge engineering in the field of big data and artificial intelligence, in particular to a population residence prediction method, a population residence prediction system and a population residence prediction medium based on a multi-mode cognitive map.

Background

Artificial intelligence has moved from computational intelligence, perceptual intelligence, to cognitive intelligence stages. Cognition is the process of acquiring, processing and applying knowledge by an individual, which is a high-level information processing mode of human brain; cognitive intelligence enables a machine to have the capabilities of reading and understanding semantics, logical reasoning and learning judgment. Two cores of machine-aware intelligence are "understanding" and "interpretation". The realization of cognitive intelligence needs to take knowledge as a driving force, which relates to key technologies such as knowledge representation, semantic understanding, associative reasoning, intelligent question answering, emotion calculation, decision planning and the like.

With the rise of deep learning, artificial intelligence has been in the way of new development climax. One bottleneck in the development of artificial intelligence is how to let machines know human knowledge, and it is extremely difficult for machines to understand and know a great deal of this knowledge, which is the necessary way to develop strong artificial intelligence.

The appearance of cognitive profiles has injected "accelerators" for the development of cognitive intelligence. However, the application of the current cognitive maps is shallow, particularly the invention is invented for the social application problem related to the processing of big data, the current big data method for the investigation and statistics of regional population is lack of a dynamic, comprehensive and accurate processing means, and a method for comprehensively and accurately predicting the regional population by means of a multi-modal cognitive map is not provided.

In view of the above problems, an effective technical solution is urgently needed.

Disclosure of Invention

The embodiment of the application aims to provide a population living prediction method, a system and a medium based on a multi-modal cognitive map, which can identify and extract the attributes and event relations of population multi-modal data according to a multi-modal cognitive map of a constructed region intelligent agent, perform attribute linking and cognitive fusion on the extracted population multi-modal to obtain population data multi-modal cognition, and perform cognitive processing according to the cognitive map and rules to predict the population of the region living.

The embodiment of the application also provides a population residence prediction method based on the multi-mode cognitive map, which comprises the following steps:

establishing a multi-modal cognitive map of a region intelligent agent according to region data, and establishing a primary region appearance cognitive system;

acquiring population circulation data of the region, establishing population multi-mode data, and performing population data multi-mode recognition and population attribute and region parking event relation extraction on the population multi-mode data based on the region intelligent agent multi-mode cognitive map;

performing population attribute linking and cognition fusion on the extracted population data in a multi-mode manner to obtain population data multi-mode cognition;

and performing cognitive processing according to the cognitive map and a preset logical reasoning rule to predict the residential population of the region.

Optionally, in the method for predicting population occupancy based on a multi-modal cognitive map according to the embodiment of the present application, the building a multi-modal cognitive map of a geographic intelligence entity according to geographic data, and building a preliminary geographic aspect cognitive system includes:

acquiring region characteristic data of a target region, wherein the region characteristic data comprises region characteristic data, house capacity data, region function data and building characteristic data;

constructing a space coordinate system and a scale of the target region and region graphic unit data according to the region characteristic data;

establishing a region scene model according to the region graphic unit data, and carrying out digital description on the region scene model;

extracting color information of the region model scenery model and combining the digital descriptor to perform rasterization processing to construct a virtual reality scene of the target region;

and constructing a multi-modal cognitive map of the region of the virtual reality scene according to the region characteristic data, mapping the position relation of various objects in the region scene on the space and the incidence relation of various logics according to a space coordinate system, and establishing primary cognition for the region appearance.

Optionally, in the method for predicting population occupancy based on the multi-modal cognitive map according to the embodiment of the present application, the acquiring population circulation data of the region and establishing population multi-modal data, and performing population data multi-modal recognition and population attribute and region parking event relationship extraction on the population multi-modal data based on the region intelligent agent multi-modal cognitive map includes:

acquiring population circulation data of the target region, wherein the population circulation data comprises population flow data, people flow image data, people flow video monitoring data and census data;

establishing population multi-modal data according to the population circulation data;

recognizing the population multimodal data according to the region intelligent agent multimodal cognitive map and pre-training data, and establishing correspondence and dependency relationship of the population multimodal data;

population image recognition, population data recognition, target population extraction, population relationship extraction, population attribute extraction and docking event extraction are performed based on the population multimodal data.

Optionally, in the method for predicting population occupancy based on a multi-modal cognitive map according to the embodiment of the present application, the performing population image recognition, population data recognition, target population extraction, population relationship extraction, population attribute extraction and parking event extraction based on the population multi-modal data includes:

the population image recognition comprises people stream image segmentation, target detection and recognition, frequency threshold comparison and appearance similarity calculation are carried out according to segmented people stream individuals and images in the multi-modal cognitive map of the regional intelligent agent, and if the similarity probability exceeds a preset threshold, the same target population individual is judged;

the population data identification comprises data word segmentation processing, keyword labeling and population individual identification;

extracting atomic information elements in the population multimodal data to perform the target population extraction based on a knowledge base and a dictionary;

the population relationship extraction and the population attribute extraction comprise population attribute relationship extraction, human-house relationship extraction, region and stream relationship extraction and building population relationship extraction based on preset rules;

the parking event extraction is to extract and structurally express parking event information between people flow and regional buildings, and comprises open domain or limited domain parking event extraction and parking reason relationship extraction.

Optionally, in the method for predicting population occupancy based on a multi-modal cognitive map according to the embodiment of the present application, performing population attribute linking and cognitive fusion on the extracted population data in a multi-modal manner to obtain multi-modal cognition of population data, includes:

corresponding to the same correct population individual in the cognitive library according to the obtained same population individual;

judging whether the same individual or related individuals exist according to the population individuals in the preset population database;

acquiring population individual objects through population attribute extraction and obtaining multi-mode population data links corresponding to correct population individuals in the cognitive library;

merging the multi-modal cognitive maps of the geographical intelligent agents into the cognitive library according to the constructed multi-modal cognitive maps to complete multi-modal cognitive combination, wherein the merging comprises merging of a data layer and a mode layer;

the data layer fusion comprises fusion of population individuals and fusion of population attributes;

the fusion of the mode layer comprises the fusion of the upper and lower bit relations of the data and the fusion of the definition of the data attribute.

Optionally, in the method for predicting population occupancy based on a multi-modal cognitive map according to the embodiment of the present application, the predicting the population of the region occupancy by performing cognitive processing according to the cognitive map and a preset logical inference rule includes:

performing cognitive processing according to the multi-modal cognitive map of the regional intelligent agent and a preset logical reasoning rule, wherein the cognitive processing comprises body construction, cognitive reasoning and result evaluation;

the ontology construction is carried out in a data automation driving mode, and the ontology construction process comprises population parallel relationship similarity calculation, population superior-inferior relationship extraction and ontology generation;

the cognitive inference enriches the multi-modal cognitive maps of the regional intelligent agents by acquiring new associations between population individuals and new associations between individual regions through acquiring relationships between population regions and relationships between individual region parking events according to a logical inference rule on the basis of the multi-modal cognitive maps of the regional intelligent agents;

the result evaluation includes accuracy and coverage evaluation.

Optionally, in the method for predicting population occupancy based on a multi-modal cognitive map according to the embodiment of the present application, the cognitive inference enriches the multi-modal cognitive map of the domain agent by obtaining new associations between population individuals and new associations between individual domains based on the multi-modal cognitive map of the domain agent according to a logical inference rule based on relationships between population domains and relationships between individual domain parking events, including:

the reasoning mode of the logic reasoning rule comprises deductive reasoning, inductive reasoning, analogy reasoning, cause reasoning, deterministic reasoning and uncertainty reasoning;

and carrying out logical reasoning by the numerical model method of uncertainty reasoning based on a fuzzy theory.

In a second aspect, the present application provides a population occupancy prediction system based on a multi-modal cognitive map, the system including: a memory and a processor, wherein the memory includes a multi-modal cognitive map-based population occupancy prediction method program, and the multi-modal cognitive map-based population occupancy prediction method program when executed by the processor implements the following steps:

performing population attribute linkage and cognitive fusion on the extracted population data in a multi-mode manner to obtain population data multi-mode cognition;

Optionally, in the system for predicting population occupancy based on a multi-modal cognitive map according to the embodiment of the present application, the building a multi-modal cognitive map of a geographic intelligent entity according to geographic data, and building a preliminary geographic aspect cognitive system include:

In a third aspect, the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes a multi-modal cognitive map-based population occupancy prediction method program, and when the multi-modal cognitive map-based population occupancy prediction method program is executed by a processor, the method implements the steps of the multi-modal cognitive map-based population occupancy prediction method described in any one of the above.

As can be seen from the above, the population residence prediction method, the system and the medium based on the multi-modal cognitive map provided in the embodiment of the present application construct the multi-modal cognitive map of the domain agent according to the domain data and establish a preliminary domain appearance cognitive system, perform multi-modal identification of population data and extraction of population attributes and domain parking event relations on the established multi-modal data of the population based on the multi-modal cognitive map of the domain agent, perform population attribute linking and cognitive fusion on the extracted population data in multiple modes to obtain multi-modal cognition of the population data, and perform cognitive processing according to the cognitive map and preset logical reasoning rules to predict the population of the domain residence; therefore, the multi-modal cognition map of the regional intelligent agent is constructed to identify and extract the attributes and event relations of the multi-modal population data, the attribute linkage and cognition fusion are carried out on the extracted multi-modal population to obtain the multi-modal cognition of the population data, and the cognition processing is carried out according to the cognition map and the rule to predict the residential population in the region.

Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a flowchart of a population occupancy prediction method based on a multi-modal cognitive atlas according to an embodiment of the present application;

fig. 2 is a flowchart of a method for predicting population occupancy based on a multi-modal cognitive map for constructing a multi-modal cognitive map of a geographic intelligence entity and a preliminary geographic landscape cognitive system according to an embodiment of the present disclosure;

fig. 3 is a flowchart of multimodal identification of population data and extraction of relationship between population attributes and geographic parking events in the population residence prediction method based on the multimodal cognitive map according to the embodiment of the present application;

fig. 4 is a schematic structural diagram of a population occupancy prediction system based on a multi-modal cognitive atlas according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flow chart of a population occupancy prediction method based on a multi-modal cognitive map in some embodiments of the present application. The population residence prediction method based on the multi-modal cognitive map is used in terminal equipment, such as computers, mobile phone terminals and the like. The population residence prediction method based on the multi-modal cognitive map comprises the following steps of:

s101, establishing a multi-mode cognitive map of a region intelligent agent according to region data, and establishing a primary region appearance cognitive system;

s102, acquiring population circulation data of the region, establishing population multi-mode data, and performing population data multi-mode recognition and population attribute and region parking event relation extraction on the population multi-mode data based on the region intelligent agent multi-mode cognitive map;

s103, performing population attribute linking and cognition fusion on the extracted population data in a multi-mode manner to obtain population data multi-mode cognition;

and S104, performing cognitive processing according to the cognitive map and a preset logical reasoning rule to predict the residential population of the region.

It is to be noted that the technology performs entity recognition, relationship extraction and cognitive fusion on population multi-modal data established by acquired population circulation data in a region by constructing a region intelligent body multi-modal cognitive map to obtain population data multi-modal cognition, finally performs cognitive processing according to the cognitive map and a logical reasoning rule to predict a region resident population to obtain a technology for recognizing, extracting, recognizing and processing the population multi-modal data according to the region cognitive map to obtain the region resident population prediction, wherein the preliminary region physiognomy cognitive system in the scheme comprises a space coordinate system, a virtual reality scene and a multi-modal cognitive map, a space-time reference system can be in a reference system taking a perceptor as a center, and a virtual reality scene is simulated by switching between reference systems established by things except the perceptron, the multi-mode cognitive map is a multi-mode network map which depends on the spatial position relation and various logic incidence relations of various things in a space coordinate system and a time sequence mapping region scene, the cognitive processing according to the cognitive map and the preset logical inference rule comprises a body construction process, a cognitive inference process and a result evaluation cognitive processing process, wherein the body construction process is a semantic basis for communication of events in the intelligent multi-modal cognitive map and is automatically constructed by deep learning drive, the cognitive inference process is based on the existing intelligent multi-modal cognitive map, new relations among population individuals and new relations among individual domains are found by calculating the relations among human mouth domains and the relations among individual domain parking events according to the preset logical inference rule, the cognitive inference process is an important means for updating the intelligent multi-modal cognitive map, the result evaluation is a final checking link of the cognitive processing, and the reasonability of the intelligent multi-modal cognitive map is ensured.

Referring to fig. 2, fig. 2 is a flowchart illustrating a geographic intelligence multi-modal cognitive map and a preliminary geographic facies cognitive system in a multi-modal cognitive map-based population prediction method according to some embodiments of the present disclosure. According to the embodiment of the invention, the method specifically comprises the following steps:

s201, obtaining region characteristic data of a target region, wherein the region characteristic data comprises region characteristic data, house capacity data, region function data and building characteristic data;

s202, constructing a space coordinate system and a scale of the target region and region graphic unit data according to the region characteristic data;

s203, establishing a region scene model according to the region graphic unit data, and carrying out digital description on the region scene model;

s204, extracting color information of the region model scenery model and carrying out rasterization processing by combining the digital description element to construct a virtual reality scene of the target region;

s205, a regional multi-modal cognitive map of the virtual reality scene is constructed according to the regional characteristic data, the spatial position relation and various logic incidence relation of various things in the regional scene are mapped according to a spatial coordinate system, and primary cognition is built on the regional appearance.

It should be noted that, in order to establish initial cognition on the geographical features, a geographical multi-modal cognitive map generating a virtual reality scene is constructed through the acquired characteristic data of the target geographical region, and a spatial coordinate system is combined to map the spatial position relationship and logical association relationship of each characteristic object in the geographical scene, including regional building buildings and the like, wherein the spatial coordinate system includes a model coordinate system, a world coordinate system and an observation coordinate system; the model in the model coordinate system is a three-dimensional object, each object has a model coordinate system of the object, the model coordinate system is an imaginary coordinate system, the relative position of the coordinate system and the object is invariable all the time, the world coordinate system is a real 3D scene of our life, the model coordinate in the model coordinate system is transformed into world coordinates after being multiplied by a model matrix, the observation coordinate system is a Camera coordinate system, the Camera view coordinate system can also be called a uvn coordinate system, and corresponds to three XYZ axes of the world coordinate system; the method comprises the steps of constructing a multi-modal cognitive map of a region scene model, mapping position relations of various objects in a region scene in space and incidence relations of various logics by means of a space coordinate system and a time sequence, wherein entities in the cognitive map are divided into logic entities and three-dimensional graphic entities, the logic entities refer to entities on a word concept, the three-dimensional graphic entities refer to visual three-dimensional graphics, the logic entities are further divided into logic entities and event entities, the logic entities can correspond to the three-dimensional graphic entities one by one, the event entities are a combination of a series of dynamic change processes of related objects, the entities can have various attributes such as flow, identity, gender, age, flow direction, time density and the like of population flows, the attributes can be in a word concept form such as census data, can also be in a graph or video form such as capturing video pictures or screen capture images, the entities can have various relations, and can be in a space and time sequence relation such as individual relations, group classification, group attribute relations and the like among population groups; establishing a region scene model including objects of various real regions such as region landform, building layout, region arrangement, region space and the like, wherein the basic steps of establishing main graphic operation of the scene are firstly establishing a scene model according to a basic graphic unit and mathematically describing the established model, then placing the scene model at a proper position in a three-dimensional space and setting a view point to observe a target scene and then calculating the colors of all objects in the model, wherein the colors are determined according to application requirements, simultaneously determining an illumination condition and a texture pasting mode, and finally converting the mathematical description of the scene model and the color information thereof to a computer screen for rasterization.

Referring to fig. 3, fig. 3 is a flow chart of multimodal identification of population data and population attribute and geographic parking event relationship extraction in a population residence prediction method based on multimodal cognitive mapping according to some embodiments of the present application. According to the embodiment of the invention, the acquiring of the population circulation data of the region and the establishing of the population multi-modal data, and the performing of the population data multi-modal identification and the population attribute and region parking event relationship extraction on the population multi-modal data based on the region intelligent agent multi-modal cognitive map specifically comprise:

s301, acquiring population circulation data of the target region, wherein the population circulation data comprises population flow data, people flow image data, people flow video monitoring data and census data;

s302, establishing population multimodal data according to the population circulation data;

s303, identifying the population multi-modal data according to the region intelligent agent multi-modal cognitive map and pre-training data, and establishing correspondence and dependency relationship of the population multi-modal data;

and S304, performing population image identification, population data identification, target population extraction, population relation extraction, population attribute extraction and parking event extraction based on the population multimodal data.

It should be noted that the geographic intelligence multi-modal cognitive map has multi-modal data recognition capability, the cognitive map is trained by using the known corresponding relation and classification attribute multi-modal data as pre-training data, so that the ability of recognizing population multi-modal data by using the region intelligent multi-modal cognitive map can be realized, establishing population multi-mode data for data, images, audio and video and other population flow data, people flow image data, people flow video monitoring data and population census data acquired by collecting population circulation conditions of a target region, identifying and establishing the corresponding and dependency relationship of the population multi-modal data in a region intelligent agent multi-modal cognitive map according to the population multi-modal data, then performing classification entity extraction and event extraction, wherein the entity extraction refers to identifying a specific element label in a multi-modal data source, and is linked with the tags in the entity library, the target population extraction is to identify the attribute tags which accord with the target population according to the population attributes, and linked with tags in the population attribute library, the entity relationship extraction is to find the relationship among the entities in the multi-modal data source, the population relation extraction can be divided into global extraction and local extraction, wherein the population relation extraction is to find the relations between population individuals and between individuals and populations in a population multi-mode data source, the entity attribute extraction is the relation between entities and attributes thereof, namely the correlation between population individuals and population attributes, and the event extraction is to extract and structurally express the event information in the multi-modal data source and comprises the steps of event extraction, event relation extraction, the system comprises a multi-modal population data source, a multi-modal population data source and a multi-modal population data source, wherein population individuals and populations in the multi-modal population data source are extracted and structurally represented in regional parking time, places, reasons, parking passes, front and back parking processes and the relationship between parking and regions.

According to the embodiment of the invention, the population image recognition, population data recognition, target population extraction, population relationship extraction, population attribute extraction and parking event extraction based on the population multimodal data specifically comprise:

the population image recognition comprises people stream image segmentation, target detection and recognition, and comprises the steps of carrying out frequency threshold comparison and appearance similarity calculation according to segmented people stream individuals and images in the multi-modal cognitive map of the regional intelligent agent, and judging the same target population individual if the similarity probability exceeds a preset threshold;

the parking event extraction is to extract and structurally express the parking event information between people flow and regional buildings, and comprises open domain or limited domain parking event extraction and parking reason relationship extraction.

It should be noted that, the image segmentation is to input an image into a network to obtain a corresponding feature map, then use an RPN structure to generate a candidate frame to project the candidate frame onto the feature map to obtain a corresponding feature matrix, scale each feature matrix to obtain the feature map and flatten the feature map, then scale each feature matrix through a series of full connection layers, perform a deeper feature extraction through convolution, and finally attach the feature matrix to a corresponding position in an original image to obtain a result map of example segmentation, the target detection and identification is to compare a segmented population individual with an existing individual image in a cognitive map, calculate a similarity probability through a similarity calculation method, if the similarity probability exceeds a preset threshold, determine the same individual, if the similarity probability exceeds the preset threshold, compare the result with the existing image in the cognitive map, and then perform image comparison through searching, the data word segmentation processing comprises a dictionary-based method (a forward maximum matching algorithm, a reverse maximum matching algorithm and a bidirectional maximum matching method) and a statistic-based method, wherein keyword labeling adopts a hidden Markov model, a perceptron and a conditional random field method, population individual identification is to firstly combine a population attribute library of an existing cognitive map to assign weights to each rule, then judge types according to the conformity degree of individuals and the rules, and then label a locked individual identification task as a sequence by using sentences which are pre-labeled based on the hidden Markov model, the maximum entropy model and the conditional random field, and a target population mainly extracts atomic information elements in population multimodal data, wherein the method based on the knowledge base and the dictionary mainly comprises matching patterns and character strings by means of a knowledge base and a dictionary which are established by means of the existing cognitive map, and the statistic-based method is based on the hidden Markov model, the conditional random field and the statistical method based on a machine learning method, the event extraction comprises open domain or limited domain resident event extraction and resident reason relationship extraction, the building is divided into building meta-event extraction and subject event extraction, wherein meta-events represent occurrence of resident actions or change of resident states of population individuals, the building meta-event extraction and the subject event extraction are driven by verbs and can also be triggered by nouns capable of representing the actions, the building meta-event extraction comprises individual resident places, time and associated individuals or groups participating in the resident action behaviors, the meta-event extraction method comprises meta-event extraction based on pattern matching, meta-event extraction based on machine learning and an extraction method based on a neural network, the subject events comprise core events or activities and all events and activities directly related to the core events or activities and can be composed of a plurality of meta-event segments, the subject event extraction method comprises subject event extraction based on an event frame, subject event extraction based on an ontology, namely resident event extraction based on the resident event frame and resident relationship event extraction based on the resident individuals, the subject relationship extraction and population attribute extraction based on the resident individuals, the relationship extraction and the building regional individual relationship extraction and the building relationship extraction, and the population region and building individual relationship extraction, and the building regional relationship extraction.

According to the embodiment of the invention, the extracted population data is subjected to population attribute linking and cognition fusion in a multi-mode manner to obtain the multi-mode cognition of the population data, and the method specifically comprises the following steps:

corresponding the obtained same population individual to the same correct population individual in the cognitive library;

The method is characterized in that population attributes, population individuals and resident events in a plurality of cognitive maps or information sources are linked through aligning, associating, merging and other modes of cognitive libraries from a population attribute layer and a population individual layer to form a more uniform and dense intelligent multi-mode cognitive map, and the method is an important method for realizing cognitive sharing and reasoning.

According to the embodiment of the invention, the cognitive processing is performed according to the cognitive map and the preset logical reasoning rule to predict the residential population of the region, and the method specifically comprises the following steps:

performing cognitive processing according to the regional intelligent agent multi-modal cognitive atlas and a preset logical reasoning rule, wherein the cognitive processing comprises ontology construction, cognitive reasoning and result evaluation;

the ontology is constructed in a data automation driving mode, and the ontology construction process comprises population parallel relationship similarity calculation, population superior-inferior relationship extraction and ontology generation;

the cognitive inference enriches the multi-modal cognitive atlas of the regional intelligent agent by acquiring new associations between population individuals and new associations between individual regions through the relationship between population regions and the relationship between individual regional parking events according to a logical inference rule based on the multi-modal cognitive atlas of the regional intelligent agent;

the result evaluation includes accuracy and coverage evaluation.

It should be noted that the population parallel relationship similarity calculation is suitable for examining the index measure of how much any given two population individuals belong to the same attribute classification, and the higher the similarity is, the more likely the two population individuals belong to the same classification, so that the parallel relationship is relative to the longitudinal concept membership, and there are two methods for calculating the population parallel relationship similarity: the method comprises a mode matching method and a distribution similarity, wherein the mode matching method adopts a method of predefining individual pair modes, the frequency of common appearance of given keyword combinations in the same semantic unit is obtained through mode matching, the similarity between individuals is calculated according to the frequency, the premise of the distribution similarity method is that semantic similarity exists between individuals frequently appearing in similar context pipe diameters, the extraction of upper and lower relation of population is used for determining membership relation between concepts, the main method is to extract individual pairs based on grammar mode or judge individual relation and distinguish upper and lower terms by using a probability model, and help to train a model by means of concept classification knowledge to improve algorithm precision, the main task of generating the body is to cluster concepts obtained by each level of human mouth and calibrate semantic classes thereof, one or more common upper terms are assigned to the individuals in the class, result evaluation is the final inspection link of cognitive processing, and reasonability of an intelligent cognitive map is ensured, wherein the accuracy rate refers to the degree that the individual and relation correctly represent phenomena in real life, and the accuracy rate can be further subdivided into three dimensions: syntactic accuracy, semantic accuracy and timeliness, and coverage means avoiding missing elements related to a domain or a model which may produce incomplete query results or derived results or deviations.

According to the embodiment of the invention, the cognitive inference is based on the region intelligent multi-mode cognitive map, and the region intelligent multi-mode cognitive map is enriched by acquiring new relations among population individuals and new relations among individual regions through relations among population regions and relations among individual region parking events according to a logical inference rule, and specifically comprises the following steps:

the reasoning mode of the logical reasoning rule comprises deductive reasoning, inductive reasoning, analogy reasoning, cause reasoning, deterministic reasoning and uncertainty reasoning;

and carrying out logical reasoning by the numerical model method of uncertainty reasoning based on the fuzzy theory.

The method is characterized in that a multi-mode cognitive map of a regional intelligent agent is enriched by acquiring new relations among population and individuals and new relations among individual regions according to a logic reasoning rule, the relations among the population and the individuals and the parking events of the individual regions are enriched by acquiring new relations among the population and the individuals and the new relations among the individual regions according to the logic reasoning rule, the cognitive map is enriched by acquiring the new relations among the population and the individuals and the new relations among the individual regions according to the logic reasoning rule, deductive reasoning is also called logic reasoning from general to special, inductive reasoning is from special to special, and the inference is also called reverse reasoning because the reasoning is from special to special, and the deterministic reasoning refers to the fact that knowledge and evidence used in reasoning are determined, the deduced conclusion is also determined, the true value is true or false, the knowledge and the evidence used in the reasoning of the uncertain reasoning are not determined, the conclusion is also uncertain, the reasoning method adopts a numerical model method, and the numerical model method is deduced through a credibility method based on a fuzzy theory evidence method, a Bayesian theory and a probability-based reasoning method.

As shown in fig. 4, the present invention further discloses a system for predicting population occupancy based on a multi-modal cognitive spectrum, which includes a memory 41 and a processor 42, wherein the memory includes a program of a method for predicting population occupancy based on a multi-modal cognitive spectrum, and when executed by the processor, the program of the method for predicting population occupancy based on a multi-modal cognitive spectrum implements the following steps:

It should be noted that, the technology obtains the multi-modal cognition of the population data by constructing the multi-modal cognitive map of the regional intelligent agent to perform entity recognition, relationship extraction and cognitive fusion on the multi-modal population data established by the population circulation data in the obtained region, finally predicts the residential population of the region by performing cognitive processing according to the cognitive map and the logical reasoning rule, and obtains the technology of obtaining the residential prediction of the region population by recognizing, extracting, recognizing and processing the multi-modal population data according to the regional cognitive map, in the scheme, the primary region physiognomy cognitive system comprises a spatial coordinate system, a virtual reality scene and a multi-modal cognitive map, the spatial-temporal reference system can be in a reference system taking a perceptron as the center, and a virtual reality scene is simulated by switching between reference systems established by things except a perceptron, the multi-modal cognitive map is a multi-modal network map which depends on the spatial position relationship and various logic association relationships of various things in a spatial coordinate system and a time sequence mapping region scene, the cognitive processing according to the cognitive map and the preset logical inference rule comprises a body construction process, a cognitive inference process and a result evaluation cognitive processing process, wherein the body construction process is a semantic basis for communication of events in the intelligent multi-modal cognitive map and is automatically constructed by deep learning drive, the cognitive inference process is an important means for updating the intelligent multi-modal cognitive map, the result evaluation process is a final inspection link of the cognitive processing and ensures the reasonability of the intelligent multi-modal cognitive map by calculating new associations among population individuals and new associations among individual regions based on the existing intelligent multi-modal cognitive map according to the preset logical inference rule and the relationships among human mouth inter-region relationships and individual region parking events.

According to the embodiment of the invention, the multi-modal cognitive map of the region intelligent agent is constructed according to the region data, and a preliminary region appearance cognitive system is established, and the method specifically comprises the following steps:

and constructing a regional multi-modal cognitive map of the virtual reality scene according to the regional characteristic data, mapping the spatial position relation and various logical incidence relations of various objects in the regional scene according to a spatial coordinate system, and establishing primary cognition for the regional appearance.

According to the embodiment of the invention, the acquiring of the population circulation data of the region and the establishing of the population multi-modal data, and the performing of the population data multi-modal identification and the population attribute and region parking event relationship extraction on the population multi-modal data based on the region intelligent agent multi-modal cognitive map specifically comprise:

establishing population multimodal data according to the population circulation data;

It should be noted that the geographic intelligence multi-modal cognitive map has multi-modal data recognition capability, the cognitive map is trained by using the known corresponding relation and classification attribute multi-modal data as pre-training data, so that the ability of recognizing population multi-modal data by using the region intelligent multi-modal cognitive map can be realized, establishing population multi-mode data for data, images, audio and video and other population flow data, people flow image data, people flow video monitoring data and population census data acquired by collecting population circulation conditions of a target region, identifying and establishing correspondence and dependency relationship of the population multi-modal data in a region intelligent agent multi-modal cognitive map according to the population multi-modal data, then performing classification entity extraction and event extraction, wherein the entity extraction refers to identifying a specific element label in a multi-modal data source, and is linked with the tags in the entity library, the target population extraction is to identify the attribute tags which accord with the target population according to the population attributes, and linked with tags in the population attribute library, the entity relationship extraction is to find the relationship among the entities in the multi-modal data source, the population relation extraction can be divided into global extraction and local extraction, wherein the population relation extraction is to find the relations between population individuals and between individuals and populations in a population multi-mode data source, the entity attribute extraction is the relation between entities and attributes thereof, namely the correlation between population individuals and population attributes, and the event extraction is to extract and structurally express the event information in the multi-modal data source and comprises the steps of event extraction, event relation extraction, the system comprises a multi-modal population data source, a multi-modal population data source and a multi-modal population data source, wherein population individuals and populations in the multi-modal population data source are extracted and structurally represented in regional parking time, places, reasons, parking passes, front and back parking processes and the relationship between parking and regions.

According to the embodiment of the invention, the population image recognition, population data recognition, target population extraction, population relationship extraction, population attribute extraction and parking event extraction are carried out based on the population multimodal data, and the method specifically comprises the following steps:

It should be noted that, the image segmentation is to input an image into a network to obtain a corresponding feature map, then use an RPN structure to generate a candidate frame to project the candidate frame onto the feature map to obtain a corresponding feature matrix, scale each feature matrix to obtain the feature map and flatten the feature map, scale each feature matrix through a series of full connection layers, perform a convolution to extract deeper features, and finally attach the feature matrix to a corresponding position in an original image to obtain a result map of example segmentation, the target detection and identification is to compare a segmented population individual with an existing individual image in a cognitive map, calculate a similarity probability through a similarity degree calculation method, determine the same individual if the similarity probability exceeds a preset threshold, and compare the result with the existing image in the cognitive map if the similarity probability does not exceed the preset threshold, and then perform image comparison through searching, the data word segmentation processing comprises a dictionary-based method (a forward maximum matching algorithm, a reverse maximum matching algorithm and a bidirectional maximum matching method) and a statistic-based method, wherein keyword labeling adopts a hidden Markov model, a perceptron and a conditional random field method, population individual identification is to firstly combine a population attribute library of an existing cognitive map to assign weights to each rule, then judge types according to the conformity degree of individuals and the rules, and then label a locked individual identification task as a sequence by using sentences which are pre-labeled based on the hidden Markov model, the maximum entropy model and the conditional random field, and a target population mainly extracts atomic information elements in population multimodal data, wherein the method based on the knowledge base and the dictionary mainly comprises matching patterns and character strings by means of a knowledge base and a dictionary which are established by means of the existing cognitive map, and the statistic-based method is based on the hidden Markov model, the conditional random field and the statistical method based on a machine learning method, the event extraction comprises open domain or limited domain resident event extraction and resident reason extraction, which are divided into meta-event extraction and subject event extraction, wherein the meta-event represents the occurrence of resident action of population individuals or the change of resident state, and is driven by verbs and can also be triggered by nouns capable of representing actions, including the resident location, time and associated individuals or groups of individuals participating in the resident action behavior.

According to the embodiment of the invention, the population attribute linking and cognition fusion are performed on the extracted population data in a multi-mode manner to obtain the population data multi-mode cognition, and the method specifically comprises the following steps:

merging the multi-modal cognitive maps of the regional intelligent agents into the cognitive library according to the constructed multi-modal cognitive maps of the regional intelligent agents to complete multi-modal cognitive combination, wherein the merging comprises merging of a data layer and a mode layer;

It should be noted that from two aspects of a population attribute layer and a population individual layer, population attributes, population individuals and resident events in a plurality of cognitive maps or information sources are linked through alignment, association, combination and other modes of a cognitive library to form a more uniform and dense intelligent multi-modal cognitive map, which is an important method for realizing cognitive sharing and reasoning.

the result evaluation includes accuracy and coverage evaluation.

It should be noted that the population parallel relationship similarity calculation is suitable for examining the index measure of how much any given two population individuals belong to the same attribute classification, and the higher the similarity is, the more likely the two population individuals belong to the same classification, so that the parallel relationship is relative to the longitudinal concept membership, and there are two methods for calculating the population parallel relationship similarity: the method comprises a mode matching method and a distribution similarity, wherein the mode matching method adopts a method of predefining individual pair modes, the frequency of common occurrence of given keyword combinations in the same semantic unit is obtained through mode matching, the similarity between individuals is calculated according to the frequency, the distribution similarity method is based on the premise that semantic similarity exists between the individuals frequently appearing in similar context pipe diameters, population upper and lower relation extraction is used for determining membership relation between concepts, a main method is to extract individual pairs based on grammar modes or judge individual relation and distinguish upper and lower terms by using a probability model, and help to train a model by concept classification knowledge to improve algorithm precision, a main task of generating a body is to cluster the concepts obtained by each level of the human mouth and carry out semantic class calibration on the concepts, one or more common upper terms are assigned to the individuals in the class, result evaluation is the final checking link of cognitive processing, and reasonability of an intelligent body multi-mode cognitive map is ensured, wherein the accuracy rate refers to the degree that the individual and the relation correctly represent phenomena in life, and the accuracy rate can be further subdivided into three dimensions: syntactic accuracy, semantic accuracy and timeliness, and coverage means avoiding missing elements related to a domain or a model which may produce incomplete query results or derived results or deviations.

According to the embodiment of the invention, the cognitive inference enriches the multi-modal cognitive maps of the regional intelligent agents by acquiring new associations between population individuals and new associations between individual regions through acquiring relationships between population regions and relationships between individual region parking events based on the multi-modal cognitive maps of the regional intelligent agents according to a logical inference rule, and specifically comprises the following steps:

A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a multi-modal cognitive map-based population occupancy prediction method program, and when the multi-modal cognitive map-based population occupancy prediction method program is executed by a processor, the method implements the steps of the multi-modal cognitive map-based population occupancy prediction method described in any one of the above.

The invention discloses a population residence prediction method, a system and a medium based on a multi-modal cognitive map, wherein the multi-modal cognitive map of a region agent is constructed according to region data, a preliminary region appearance cognitive system is established, the multi-modal cognitive map of the region agent is used for carrying out multi-modal recognition on the population multi-modal data established and population attributes and region parking event relation extraction, population attribute linkage and cognitive fusion are carried out on the extracted population data multi-modal to obtain population data multi-modal cognition, and cognitive processing is carried out according to the cognitive map and a preset logic reasoning rule to predict the population of the region residence; therefore, the multi-modal cognition map of the regional intelligent agent is constructed to identify and extract the attributes and event relations of the multi-modal population data, the attribute linkage and cognition fusion are carried out on the extracted multi-modal population to obtain the multi-modal cognition of the population data, and the cognition processing is carried out according to the cognition map and the rule to predict the residential population in the region.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described device embodiments are merely illustrative, for example, the division of the unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or in other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit may be implemented in the form of hardware, or in the form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a removable memory device, a read-only memory, a random access memory, a magnetic or optical disk, or any other medium that can store program code.

Alternatively, the integrated unit of the present invention may be stored in a readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

Claims

1. The population occupancy prediction method based on the multi-modal cognitive map is characterized by comprising the following steps of:

and performing cognitive processing according to the cognitive map and a preset logical reasoning rule to predict regional resident population.

2. The population residence prediction method based on the multi-modal cognition graph according to claim 1, wherein the building of the multi-modal cognition graph of the geographical intelligent object according to the geographical data and the building of the preliminary geographical appearance cognition system comprises:

extracting color information of a region model scenery model and carrying out rasterization processing by combining the digital descriptor to construct a virtual reality scene of the target region;

3. The population residence prediction method based on the multi-modal cognitive map as claimed in claim 2, wherein the acquiring population circulation data of the region and establishing population multi-modal data, performing population data multi-modal recognition and population attribute and region parking event relationship extraction on the population multi-modal data based on the region agent multi-modal cognitive map comprises:

4. The population occupancy prediction method based on the multi-modal cognitive atlas of claim 3, wherein the population multi-modal data based population image recognition, population data recognition, target population extraction, population relationship extraction, population attribute extraction and parking event extraction comprises:

5. The population occupancy prediction method based on the multi-modal cognition graph according to claim 4, wherein the performing population attribute linking and cognition fusion on the extracted population data in a multi-modal mode to obtain the population data in a multi-modal cognition, specifically comprises:

6. The population occupancy prediction method based on the multi-modal cognitive map as claimed in claim 5, wherein the cognitive processing performed according to the cognitive map and the preset logical inference rule to predict the population of the regional occupancy comprises:

the result evaluation includes accuracy and coverage evaluation.

7. The population occupancy prediction method based on the multi-modal cognitive map as claimed in claim 6, wherein the cognitive inference enriches the multi-modal cognitive map of the domain agent by obtaining new associations between population individuals and new associations between individual domains based on the multi-modal cognitive map of the domain agent according to logical inference rules, and by obtaining new associations between population domains and new associations between individual domains based on the inter-domain relationships and the inter-individual parking events of the population, the method comprises:

8. A system for predicting population occupancy based on a multi-modal cognitive profile, the system comprising: a memory and a processor, wherein the memory includes a multi-modal cognitive map-based population occupancy prediction method program, and the multi-modal cognitive map-based population occupancy prediction method program when executed by the processor implements the following steps:

establishing a multi-mode cognitive map of a region intelligent agent according to region data, and establishing a primary region appearance cognitive system;

9. The system for predicting population occupancy according to claim 8, wherein the system for building a multi-modal cognitive map of a geographic intelligence entity according to geographic data and building a preliminary geographic facies awareness system comprises:

10. Computer-readable storage medium, characterized in that the computer-readable storage medium comprises a multi-modal cognitive spectrum-based population occupancy prediction method program, which when executed by a processor, implements the steps of the multi-modal cognitive spectrum-based population occupancy prediction method according to any one of claims 1 to 7.