User psychology analysis system, method, device and storage medium

Abstract

The present disclosure relates to a user psychological analysis system, method, device and storage medium. In the embodiment of the present disclosure, multiple psychological research devices are set up, such as a first psychological research device and a second psychological research device to perform preliminary acquisition of image data, behavior data, emotion data and relationship data, and then upload these data to a data analysis device for analysis to obtain further psychological characteristic data. In this way, further psychological characteristic data can be centrally processed by a data analysis device with more advanced software and hardware configurations. The software and hardware configurations of multiple psychological research devices do not need to be too advanced to achieve the research purpose, and the economic cost of the psychological data research device is also controlled to a certain extent. In addition, data distribution through a unified data analysis device can save communication resources on the premise of achieving interaction between different psychological research devices.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a user psychology analysis system, method, device and storage medium. Background Art

[0002] With the development of science and technology and the increasing maturity of human civilization, the research on user psychology is extremely important in the entire experience development system. The biggest difficulty in user psychology research lies in the number, stability and time continuity of samples. These factors have always troubled psychologists in the accuracy of statistical data.

[0003] For example, in the sample collection of behavioral psychology, the psychology research center usually collects and analyzes samples from a small group. During this research process, some samples may lose a lot of data due to identity influence and spatial mobility factors, and the data may be inaccurate. How to improve the adverse effects of these external factors on subsequent analysis has become a problem that needs to be solved. Summary of the invention

[0004] The present disclosure proposes a user psychology analysis technical solution. The present disclosure provides a user psychology analysis system, which includes: a first psychology research device, a data analysis device, and a second psychology research device;

[0005] The first psychological research device is used to obtain first image data of a first sample person, and process the first image data to obtain first data to be analyzed of the first sample person; the first data to be analyzed includes first image data, first behavior data, first emotion data, and first relationship data;

[0006] The second psychological research device is used to obtain second image data of a second sample person, and process the second image data to obtain second data to be analyzed of the second sample person; the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data;

[0007] The data analysis device is used to analyze the received first data to be analyzed and the second data to be analyzed respectively to obtain the first psychological characteristic data of the first sample person and the second psychological characteristic data of the second sample person;

[0008] The first psychological research device and the second psychological research device obtain the first psychological characteristic data and / or the second psychological characteristic data from the data analysis device based on the first research requirement and the second research requirement, respectively, and determine the psychological analysis results according to the first psychological characteristic data and / or the second psychological characteristic data, respectively.

[0009] In some possible implementations, the first psychological research device includes an image acquisition device set, an image recognition processing device, and a psychological analysis device;

[0010] The image acquisition device in the image acquisition device set is arranged in the activity area where the first sample person is located, and is used to acquire the first image data of the first sample person;

[0011] The image recognition processing device is used to perform recognition processing on the first image data to determine the first behavior data of the first sample person;

[0012] The psychological analysis device is used to determine the first emotion data and the first relationship data of the first sample person based on the first image data and the first behavior data; and package the first image data, the first behavior data, the first emotion data and the first relationship data to obtain the first data to be analyzed.

[0013] In some possible implementations, the activity area includes a plurality of the first sample persons;

[0014] The image recognition processing device includes a face recognition module, which is used to perform face recognition on the first image data to determine the face data corresponding to each of the first sample persons.

[0015] In some possible implementations, the first behavior data of the first sample person includes gesture action data;

[0016] The image recognition processing device includes a gesture and action recognition module, which is used to perform gesture and action recognition on the first image data corresponding to each of the first sample persons to determine the gesture and action data of each of the first sample persons.

[0017] In some possible implementations, the first behavior data of the first sample persons includes behavior trajectory data; the first image data corresponding to each of the first sample persons carries shooting location information;

[0018] The image recognition processing device includes a trajectory integration module, which is used to perform trajectory integration on the first image data corresponding to each of the first sample persons and the location shooting information to determine the behavior trajectory data of each of the first sample persons.

[0019] In some possible implementations, the data analysis device includes a distribution storage device and a data analysis network;

[0020] The distribution storage device is used to receive the first data to be analyzed from the first psychological research device; and send the first data to be analyzed to the data analysis network;

[0021] The data analysis network is used to analyze the first image data, the first behavior data, the first emotion data and the first relationship data contained in the first data to be analyzed to obtain the first psychological characteristic data of the first sample person; and upload the first psychological characteristic data to the distribution storage device;

[0022] The distribution storage device is used to feed back the first psychological characteristic data and / or the second psychological characteristic data according to the second research requirement sent by the second psychological research device; or; is used to feed back the first psychological characteristic data and / or the second psychological characteristic data according to the first research requirement sent by the first psychological research device; wherein the first psychological characteristic data carries the first image data, and the second psychological characteristic data carries the second image data.

[0023] In some possible implementations, the distribution storage device includes an alliance chain network, and the data analysis network is located in the alliance chain network; the alliance chain network is jointly constructed by multiple members as nodes;

[0024] The alliance chain network includes a master node and a weak node; each of the master node and the weak node corresponds to a member; the master node includes a decision-making mechanism, and each node is provided with a storage area.

[0025] In some embodiments, the present disclosure further provides a user psychology analysis method, including:

[0026] Receiving a first data analysis request sent by a first psychological research device, parsing the first data to be analyzed from the first data analysis request; wherein the first data to be analyzed includes first image data, first behavior data, second emotion data, and first relationship data corresponding to a first sample person;

[0027] Receiving a second data analysis request sent by a second psychological research device, parsing the second data to be analyzed from the second data analysis request; wherein the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data corresponding to the second sample person;

[0028] Analyze the first data to be analyzed and the second data to be analyzed to obtain first psychological characteristic data of the first sample person and second psychological characteristic data of the second sample person;

[0029] The first psychological characteristic data and / or the second psychological characteristic data are sent to the first psychological research device and the second psychological research device respectively according to the received first research requirement and the second research requirement, and the first psychological characteristic data and / or the second psychological characteristic data are used to determine the psychological analysis results according to the first research requirement and the second research requirement.

[0030] In some embodiments, the present disclosure further provides a user psychology analysis device, comprising:

[0031] A receiving module, configured to receive a first data analysis request sent by a first psychological research device, and parse out first data to be analyzed from the first data analysis request; wherein the first data to be analyzed includes first image data, first behavior data, second emotion data, and first relationship data corresponding to a first sample person; receive a second data analysis request sent by a second psychological research device, and parse out second data to be analyzed from the second data analysis request; wherein the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data corresponding to a second sample person;

[0032] An analysis module, used for analyzing the first data to be analyzed and the second data to be analyzed to obtain first psychological characteristic data of the first sample person and second psychological characteristic data of the second sample person;

[0033] A sending module is used to send the first psychological characteristic data and / or the second psychological characteristic data to the first psychological research device and the second psychological research device respectively according to the received first research requirement and the second research requirement, and the first psychological characteristic data and / or the second psychological characteristic data are used to determine the psychological analysis results of the first research requirement and the second research requirement.

[0034] In some embodiments, the present disclosure also provides an electronic device, comprising at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the user psychological analysis method as in the second aspect by executing the instructions stored in the memory.

[0035] In some embodiments, the present disclosure also provides a computer-readable storage medium, which stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the user psychological analysis method of the second aspect of the present disclosure.

[0036] In some embodiments, the present disclosure further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute to implement the user psychological analysis method of the second aspect of the present disclosure.

[0037] In the disclosed embodiment, multiple psychological research devices are set up, such as a first psychological research device and a second psychological research device to perform preliminary acquisition of image data, behavior data, emotion data, and relationship data, and then upload these data to a data analysis device for analysis to obtain further psychological characteristic data. In this way, further psychological characteristic data can be centrally processed by a data analysis device with more advanced hardware and software configurations. The hardware and software configurations of multiple psychological research devices do not need to be too advanced to achieve the research purpose, and the economic cost of the psychological data research device is also controlled to a certain extent. In addition, data distribution through a unified data analysis device can save communication resources on the premise of achieving interaction between different psychological research devices.

[0038] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0039] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages in the embodiments of this specification or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A structural diagram of a user psychology analysis system according to an embodiment of the present disclosure is shown;

[0042] Figure 2 A structural diagram showing a first psychological research device according to an embodiment of the present disclosure;

[0043] Figure 3 A structural diagram showing a first image recognition processing device according to an embodiment of the present disclosure;

[0044] Figure 4 A structural diagram of a data analysis device according to an embodiment of the present disclosure is shown;

[0045] Figure 5 A structural diagram showing a second psychological research device according to an embodiment of the present disclosure;

[0046] Figure 6 A structural diagram showing a second image recognition processing device according to an embodiment of the present disclosure;

[0047] Figure 7 A flowchart showing a user psychology analysis method according to an embodiment of the present disclosure is shown;

[0048] Figure 8 A block diagram showing a user psychology analysis device according to an embodiment of the present disclosure;

[0049] Fig. 9 A block diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0050] Fig.10 A block diagram of another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0053] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0054] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0055] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0056] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0057] See also Figure 1 , Figure 1 A user psychology analysis system according to an embodiment of the present disclosure is shown. Figure 1 As shown, it includes a first psychological research device 1, a data analysis device 2 and a second psychological research device 3.

[0058] In an optional embodiment, the first psychological research device 1 and the second psychological research device 3 are respectively connected to the data analysis device 2, wherein the connection mode may include wireless connection and wired connection.

[0059] Optionally, the first psychological research device 1 is used to obtain first image data of a first sample person, process the first image data, and obtain first data to be analyzed of the first sample person; wherein the first data to be analyzed includes first image data, first behavior data, first emotion data, and first relationship data.

[0060] The second psychological research device 3 is used to obtain second image data of the second sample person, and process the second image data to obtain second data to be analyzed of the second sample person; wherein the second data to be analyzed includes second image data, second behavior data, second emotion data and second relationship data.

[0061] The data analysis device 2 is used to analyze the received first data to be analyzed and the second data to be analyzed respectively, to obtain the first psychological characteristic data of the first sample person and the second psychological characteristic data of the second sample person. Specifically, the first psychological research device 1 transmits the first data to be analyzed to the data analysis device 2, and the second psychological research device 3 transmits the second data to be analyzed to the data analysis device 2.

[0062] The first psychological research device 1 and the second psychological research device 3 respectively obtain the first psychological characteristic data and / or the second psychological characteristic data from the data analysis device 2 based on the first research requirement and the second research requirement, and determine the psychological analysis result according to the first psychological characteristic data and / or the second psychological characteristic data. In an optional implementation, the first research requirement is sent by the first psychological research device 1, and the second research requirement is sent by the second psychological research device 2.

[0063] In the disclosed embodiment, the first psychological research device 1 includes an image acquisition device set, an image recognition processing device, and a psychological analysis device, wherein the image acquisition device in the image acquisition device set is arranged in the activity area where the first sample person is located, and is used to acquire the first image data of the first sample person. The image recognition processing device is used to perform recognition processing on the first image data to determine the first behavior data of the first sample person. The psychological analysis device is used to determine the first emotion data and the first relationship data of the first sample person based on the first image data and the first behavior data; and the first image data, the first behavior data, the first emotion data, and the first relationship data are packaged to obtain the first data to be analyzed.

[0064] In order to distinguish it from the equipment in the second psychological research equipment, such as Figure 2 As shown, the devices included in the first psychological research device 1 are called a first image acquisition device set 11, a first image recognition processing device 12 and a first psychological analysis device 13.

[0065] The image acquisition devices in the first image acquisition device set 11 are arranged in the activity area where the first sample person is located, and are used to acquire the first image data of the first sample person. In a possible embodiment, there may be multiple first image acquisition devices in the first image acquisition device set 11, and the relationship between the first image acquisition devices and the first image recognition processing device 12 may be as follows: Figure 2 As shown, there are multiple separate and independent devices, and a link for transmitting the first image data is respectively set between each first image acquisition device and the first image recognition and processing device 12.

[0066] In other embodiments, there may be multiple first image acquisition devices in the first image acquisition device set 11. Each first image acquisition device has its own first image recognition processing device 12, and each first image acquisition device and its corresponding first image recognition processing device 12 are integrated together. Optionally, the first image acquisition device set 11 and the first image recognition processing device 12 may be connected via a wireless link for transmitting the first image data, or may be connected via a wired link for transmitting the first image data.

[0067] In some embodiments, the first image acquisition device in the first image acquisition device set 11 is set in the activity area where the first sample person is located, and is used to collect image data of the first sample person. The above-mentioned activity area can be any area that the psychologist wants to study, including but not limited to schools, scientific research institutions, companies, offices, classrooms, playgrounds, etc.

[0068] In some embodiments, the attributes of the first sample persons to be studied can be determined first, and then the activity area for setting the image acquisition device can be determined based on the attributes of the first sample persons. For example, if it is determined that the first sample persons to be studied are students and teachers, then the activity area for setting the first image acquisition device can be determined to be a school or classroom.

[0069] In other embodiments, the activity area to be studied can be determined first, and after the first image acquisition device is set up in the activity area, the first sample personnel can be determined directly based on the acquired image data. For example, if the location of a company is determined as the activity area, the first sample personnel can be the company's human resources, engineers, finance, cleaning staff, etc.

[0070] In some embodiments, the first image acquisition devices can be evenly arranged in the activity area based on the area of ​​the activity area and the number of sample personnel. Optionally, the area that each first image acquisition device is responsible for is not repeated. Alternatively, more first image acquisition devices can be arranged in areas where the first sample personnel appear more frequently, and fewer first image acquisition devices can be arranged in areas where the first sample personnel appear less frequently, so that each first image acquisition device can be fully utilized.

[0071] Optionally, the first image acquisition device in the first image acquisition device set 11 above may include but is not limited to any device capable of acquiring the first image data, such as a camera, a still camera, a video recorder, an infrared radar, etc. For example, during the day, the camera may be activated to acquire the first image data, and during the night, the camera and the infrared radar may be used to acquire the first image data simultaneously.

[0072] In some embodiments, the first image recognition processing device 12 is used to perform recognition processing on the first image data to determine the behavior data of the first sample person. The first image recognition processing device 12 includes a face recognition module, a gesture and action recognition module, and a trajectory integration module. In order to distinguish it from the second psychological research device, the name of the included device can be used to distinguish it. Please refer to Figure 3 , Figure 3 A structural diagram of a first image recognition processing device according to an embodiment of the present disclosure is shown as follows: Figure 3As shown, the first image recognition processing device 12 includes a first face recognition module 121 , a first gesture and action recognition module 122 and a first trajectory integration module 123 .

[0073] In some embodiments, during a certain period of time, it may be necessary to sample the first image data of only one first sample person. Therefore, when identifying the first image data of the first sample person, it is necessary to determine the first image data of the first sample person from the numerous first image data collected by the first image collection device set 11. In this case, the first image recognition processing device 12 may include a first face recognition module 121, which is used to determine the first image data related to the first sample person from the numerous first image data based on the face data. For example, if 10,000 images are collected, 100 images related to the first sample person can be determined by the first face recognition module 121. Optionally, a reference image of the first sample person can be obtained, and the first eigenvalue of the person in the reference image can be calculated by the first face recognition module 121. Subsequently, the second eigenvalue of the person in the 10,000 images can be calculated by the first face recognition module 121. The similarity value of each image relative to the reference image is calculated by each second eigenvalue and the first eigenvalue. Based on the 10,000 similarity values ​​obtained above, 100 images related to the sample person are determined from all the images.

[0074] In some embodiments, there may be multiple first sample persons in the activity area, and the first image recognition processing device 12 may include a first face recognition module 121, which is used to perform face recognition on the first image data to determine the face data corresponding to each first sample person. The specific recognition process can refer to the recognition process in the previous paragraph, which will not be repeated here.

[0075] Optionally, the first behavior data of the first sample person mentioned above may include gesture data, and the first image recognition processing device 12 includes a first gesture recognition module 122, which is used to perform gesture recognition on the first image data corresponding to each first sample person to determine the gesture data of each first sample person. The gesture data includes the first sample person's walking, sitting, running, jumping, chatting and other actions, and also includes the manner, speed, posture, etc. when performing the above actions.

[0076] Optionally, the first behavior data of the first sample person mentioned above also includes behavior trajectory data, and the first image data corresponding to each first sample person may carry shooting location information. Based on the above situation, the first image recognition processing device 12 includes a first trajectory integration module 123, which is used to perform trajectory integration on the first image data and location shooting information corresponding to each first sample person to determine the behavior trajectory data of the first sample person. For example, it can be determined which position in the activity area each first sample person appears more frequently. In this way, the first image recognition processing device 12 comprehensively identifies the posture motion data and behavior trajectory data of the first sample person on the basis of identifying the first sample person.

[0077] Optionally, the first image data corresponding to each first sample person also carries shooting time information. Based on the shooting time information, the first image recognition processing device 12 can integrate the behavior trajectory data and posture action data of each first sample person to determine the behavior trajectory data and posture action data that change with time, that is, which positions in the activity area the first sample person passed through along the timeline, what they did at these positions, who they met, chatted with, exercised with, etc.

[0078] Optionally, the first face recognition module mentioned above can also be constructed based on a face recognition network, and the first gesture and action recognition module 122 mentioned above can be determined based on a gesture and action recognition network, and the face recognition network or the gesture and action recognition network includes but is not limited to deep learning networks such as convolutional neural networks, recurrent neural networks or recursive neural networks. Taking the convolutional neural network and the face recognition network as examples, a large number of training data sets can be obtained, each of which includes an image data set, and annotated faces and labels corresponding to faces on each image in the image data set. Then, face recognition training is performed on the convolutional neural network based on a large number of training data sets. During the training, the parameters of the convolutional neural network are adjusted until the labels of the faces output by the convolutional neural network match the labels of the annotated faces, that is, different image data can be accurately classified to obtain the face recognition network.

[0079] Among them, the training data in the training data set in the embodiment of the present application can be stored in a storage area, and the storage area can be a blockchain. Among them, blockchain is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods, each of which contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. The blockchain can include the blockchain underlying platform, the platform product service layer, and the application service layer.

[0080] After the first image recognition processing device 12 determines the first behavior data based on the first image data, it can send the first image data and the first behavior data to the first psychological analysis device 13, and the first psychological analysis device 13 can determine the first emotion data and the first relationship data of the first sample person based on the received first image data and the first behavior data.

[0081] In some embodiments, the first psychological analysis device 13 may also receive label data obtained based on the first image data and the first behavior data from other devices, and the label data may be obtained by uploading the label data analyzed by the first researcher. Subsequently, the first psychological analysis device 13 may obtain the first emotion data and the first relationship data of the sample person based on the first image data, the first behavior data and the label data.

[0082] In other embodiments, the first psychological analysis device 13 can determine the first emotion data and the first relationship data of the first sample person through an emotion relationship analysis network built in the first psychological analysis device 13. The emotion relationship analysis network or gesture action recognition network mentioned above includes but is not limited to deep learning networks such as convolutional neural networks, recurrent neural networks or recursive neural networks.

[0083] Optionally, the first emotion data mentioned above may be that each first image data (each image) carries an emotion data expression (such as cheerful, angry, happy, depressed or sad, etc.), or it may be a series of first emotion data that changes over time to describe the first sample person (such as happy-depressed-lively-depressed).

[0084] Optionally, the first relationship data mentioned above can be used to describe a person alone or with multiple people. When there are multiple people together, the first relationship data can be used to describe the relationship between the first sample person and other people (such as lovers, friends, teachers and students, acquaintances, strangers, etc.). Each first image data can be accompanied by a first relationship data, and the first sample person can also be described by a series of first relationship data that change over time.

[0085] The data analysis device 2 receives the first image data, the first behavior data, the first emotion data and the first relationship data sent from the first psychological research device 1, and analyzes the first image data, the first behavior data, the first emotion data and the first relationship data to obtain the first psychological characteristic data of the first sample person. The first psychological characteristic data can be used to describe the psychological characteristics that may exist in the first sample person during the period of research and the possibility of the existence of such psychological characteristics, such as depression-probability 75%, anxiety-probability 15%, normal-probability 10%.

[0086] In some embodiments, Figure 4 A structural diagram of a data analysis device 2 according to an embodiment of the present disclosure is shown. The data analysis device 2 includes a distribution storage device 21 and a data analysis network 22 .

[0087] The distribution storage device 21 is used to receive and store the first data to be analyzed from the first psychological research device 1, including the first image data, the first behavior data, the first emotion data and the first relationship data. Subsequently, the first image data, the first behavior data, the first emotion data and the first relationship data can be sent to the data analysis network 22. The data analysis network 22 is used to analyze the first image data, the first behavior data, the first emotion data and the first relationship data to obtain the first psychological characteristic data of the first sample person, and upload the first psychological characteristic data to the distribution storage device 21.

[0088] In some embodiments, the distribution storage device 21 may be public, and anyone may download the first image data and the first psychological characteristic data from it for research.

[0089] In other embodiments, the distribution storage device 21 includes an alliance chain network, which is jointly constructed by multiple members as nodes and is used to transmit the first image data and the first psychological characteristic data only between the members belonging to the alliance chain network to ensure the security of the research data. The alliance chain network is used to receive the first image data, the first behavior data, the first emotion data and the first relationship data from the first psychological research device 1, and send the first image data, the first behavior data, the first emotion data and the first relationship data to the data analysis network 22.

[0090] Optionally, the alliance chain network includes a master node and a weak node, each of which corresponds to a member. The member can be a psychological research device set up in different research institutes, such as a first psychological research device and a second psychological research device, or a psychological research device set up in a social company. The master node includes a decision-making mechanism for deciding whether to allow psychological research devices in other locations to join the alliance chain network, and whether to add a node for the newly added psychological research device, or only give the psychological research device a data interface for downloading data on the alliance chain network. Optionally, each node is provided with a storage area, which can be used to store the first image data, the first behavior data, the first emotion data, the first relationship data, and the first psychological characteristic data.

[0091] The second psychological research device 3 is explained below. With reference to the description of the first psychological research device 3, in the embodiment of the present disclosure, the second psychological research device 3 may also include an image acquisition device set, an image recognition processing device, and a psychological analysis device, wherein the image acquisition device in the image acquisition device set is arranged in the activity area where the second sample person is located, and is used to collect the second image data of the second sample person. The image recognition processing device is used to perform recognition processing on the second image data to determine the second behavior data of the second sample person. The psychological analysis device is used to determine the second emotion data and the second relationship data of the second sample person based on the second image data and the second behavior data; and the second image data, the second behavior data, the second emotion data, and the second relationship data are packaged to obtain the second data to be analyzed.

[0092] In order to distinguish it from the equipment in the first psychological research equipment, such as Figure 5 As shown, the devices included in the second psychological research device 3 are called a second image acquisition device set 31, a second image recognition processing device 32 and a second psychological analysis device 33.

[0093] The image acquisition devices in the second image acquisition device set 31 are set in the activity area where the second sample person is located, and are used to acquire the second image data of the second sample person. In some embodiments, there may be multiple second image acquisition devices in the second image acquisition device set 31, and the relationship between the second image acquisition device and the second image recognition processing device 32 may be as follows: Figure 5 As shown, there are multiple separate and independent devices, and a link for transmitting the second image data is respectively set between each second image acquisition device and the second image recognition and processing device 32.

[0094] In other embodiments, there may be multiple second image acquisition devices in the second image acquisition device set 31. Each second image acquisition device has its own second image recognition processing device 32, and each second image acquisition device and its corresponding second image recognition processing device 32 are integrated together. Optionally, the second image acquisition device set 31 and the second image recognition processing device 32 may be connected via a wireless link for transmitting the second image data, or may be connected via a wired link for transmitting the second image data.

[0095] In some embodiments, the second image acquisition device in the second image acquisition device set 31 is set in the activity area where the second sample person is located, and is used to collect image data of the second sample person. The above-mentioned activity area can be any area that the psychologist wants to study, including but not limited to schools, scientific research institutions, companies, offices, classrooms, playgrounds, etc.

[0096] In some embodiments, the attributes of the second sample persons to be studied can be determined first, and then the activity area where the image acquisition device is to be set can be determined based on the attributes of the second sample persons. For example, if it is determined that the second sample persons to be studied are students and teachers, then the activity area for setting the second image acquisition device can be determined to be a school or classroom.

[0097] In other embodiments, the activity area to be studied can be determined first, and after the second image acquisition device is set up in the activity area, the second sample personnel can be determined directly based on the acquired image data. For example, if the location of a company is determined as the activity area, the second sample personnel can be the company's human resources, engineers, finance, cleaning staff, etc.

[0098] In an optional embodiment of setting the second image acquisition device, the second image acquisition device can be evenly set in the activity area based on the area of ​​the activity area and the number of sample personnel. Optionally, the area that each second image acquisition device is responsible for is not repeated. Alternatively, more second image acquisition devices can be set in areas where the second sample personnel appear more frequently, and fewer second image acquisition devices can be set in areas where the second sample personnel appear less frequently, so that each second image acquisition device can be fully utilized.

[0099] Optionally, the second image acquisition device in the second image acquisition device set 31 above may include but is not limited to any device capable of acquiring the second image data, such as a camera, a still camera, a video recorder, an infrared radar, etc. For example, during the day, the camera may be activated to acquire the second image data, and during the night, the camera and the infrared radar may be used to acquire the second image data simultaneously.

[0100] In some embodiments, the second image recognition processing device 32 is used to recognize and process the second image data to determine the behavior data of the second sample person. The second image recognition processing device 32 includes a face recognition module, a gesture and action recognition module, and a trajectory integration module. In order to distinguish it from the second psychological research device, the name of the included device can be used to distinguish it. Please refer to Figure 6 , Figure 6 A structural diagram of a second image recognition processing device according to an embodiment of the present disclosure is shown as follows: Figure 6 As shown, the second image recognition processing device 32 includes a second face recognition module 321 , a second gesture and action recognition module 322 and a second trajectory integration module 323 .

[0101] In some embodiments, in a certain period of time, it may be necessary to sample the second image data of only one second sample person. Therefore, when identifying the second image data of the second sample person, it is necessary to determine the second image data of the second sample person from the plurality of second image data collected by the second image collection device set 31. In this case, the second image recognition processing device 32 may include a second face recognition module 321, which is used to determine the second image data related to the second sample person from the plurality of second image data based on the face data. For example, if 10,000 images are collected, 100 images related to the second sample person can be determined from them by the second face recognition module 321. Optionally, a reference image of the second sample person can be obtained, and the second eigenvalue of the person in the reference image can be calculated by the second face recognition module 321. Subsequently, the second eigenvalue of the person in the 10,000 images can be calculated by the second face recognition module 321. The similarity value of each image relative to the reference image is calculated by each second eigenvalue and the second eigenvalue. According to the 10,000 similarity values ​​obtained above, 100 images related to the sample person are determined from all the images.

[0102] In some embodiments, there may be multiple second sample persons in the activity area, and the second image recognition processing device 32 may include a second face recognition module 321, which is used to perform face recognition on the second image data to determine the face data corresponding to each second sample person. The specific recognition process can refer to the recognition process in the previous paragraph, which will not be repeated here.

[0103] Optionally, the second behavior data of the second sample person mentioned above may include gesture data, and the second image recognition processing device 32 includes a second gesture recognition module 322, which is used to perform gesture recognition on the second image data corresponding to each second sample person to determine the gesture data of each second sample person. The gesture data includes the actions of the second sample person such as walking, sitting, running, jumping, chatting, etc., and also includes the manner, speed, gesture, etc. when performing the above actions.

[0104] Optionally, the second behavior data of the second sample person mentioned above also includes behavior trajectory data, and the second image data corresponding to each second sample person may carry shooting location information. Based on the above situation, the second image recognition processing device 32 includes a second trajectory integration module 323, which is used to perform trajectory integration on the second image data and location shooting information corresponding to each second sample person to determine the behavior trajectory data of the second sample person. For example, it can be determined which position in the activity area each second sample person appears more frequently. In this way, the second image recognition processing device 32 comprehensively identifies the posture motion data and behavior trajectory data of the second sample person on the basis of identifying the second sample person.

[0105] Optionally, the second image data corresponding to each second sample person also carries shooting time information. Based on the shooting time information, the second image recognition processing device 32 can integrate the behavior trajectory data and posture action data of each second sample person to determine the behavior trajectory data and posture action data that change with time, that is, which positions in the activity area the second sample persons passed through along the timeline, what they did at these positions, who they met, chatted with, exercised with, etc.

[0106] Optionally, the second face recognition module mentioned above can also be constructed based on a face recognition network, and the second gesture action recognition module 322 mentioned above can be determined based on a gesture action recognition network, and the face recognition network or gesture action recognition network includes but is not limited to deep learning networks such as convolutional neural networks, recurrent neural networks or recursive neural networks. Taking the convolutional neural network and the face recognition network as examples, a large number of training data sets can be obtained, each of which includes an image data set, and annotated faces and labels corresponding to faces on each image in the image data set. Then, face recognition training is performed on the convolutional neural network based on a large number of training data sets. During the training, the parameters of the convolutional neural network are adjusted until the labels of the faces output by the convolutional neural network match the labels of the annotated faces, that is, different image data can be accurately classified to obtain the face recognition network.

[0107] Among them, the training data in the training data set in the embodiment of the present application can be stored in a storage area, and the storage area can be a blockchain. Among them, blockchain is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods, each of which contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. The blockchain can include the blockchain underlying platform, the platform product service layer, and the application service layer.

[0108] After the second image recognition processing device 32 determines the second behavior data based on the second image data, the second image data and the second behavior data can be sent to the second psychological analysis device 33, and the second psychological analysis device 33 can determine the second emotion data and second relationship data of the second sample person based on the received second image data and second behavior data.

[0109] In some embodiments, the second psychological analysis device 33 may also receive label data obtained based on the second image data and the second behavior data from other devices, and the label data may be obtained by uploading the label data analyzed by the second researcher. Subsequently, the second psychological analysis device 33 may obtain the second emotion data and the second relationship data of the sample person based on the second image data, the second behavior data and the label data.

[0110] In other embodiments, the second psychological analysis device 33 can determine the second emotional data and the second relationship data of the second sample person through an emotional relationship analysis network built into the second psychological analysis device 33. The emotional relationship analysis network or gesture recognition network mentioned above includes but is not limited to deep learning networks such as convolutional neural networks, recurrent neural networks or recursive neural networks.

[0111] Optionally, the second emotion data mentioned above may be that each second image data (each image) carries an emotion data expression (such as cheerful, angry, happy, depressed or sad, etc.), or it may be a series of second emotion data changing over time to describe the second sample person (such as happy-depressed-lively-depressed).

[0112] Optionally, the second relationship data mentioned above can be used to describe a person alone or with multiple people. When there are multiple people together, the second relationship data can be used to describe the relationship between the second sample person and other people (such as lovers, friends, teachers and students, acquaintances, strangers, etc.). Each second image data can be accompanied by a second relationship data, and the second sample person can also be described by a series of second relationship data that change over time.

[0113] The data analysis device 2 receives the second data to be analyzed including the second image data, the second behavior data, the second emotion data and the second relationship data sent by the second psychological research device 3, and analyzes the second image data, the second behavior data, the second emotion data and the second relationship data to obtain the second psychological characteristic data of the second sample person. The second psychological characteristic data can be used to describe the psychological characteristics that may exist in the second sample person during the period of research and the possibility of the existence of such psychological characteristics, such as depression-likelihood 45%, anxiety-likelihood 25%, and normal-likelihood 30%.

[0114] In some embodiments, Figure 6 The distribution storage device 21 shown in the figure is also used to receive the second data to be analyzed from the second psychological research device 3, including the second image data, the second behavior data, the second emotion data and the second relationship data, and store them. Subsequently, the second image data, the second behavior data, the second emotion data and the second relationship data can be sent to the data analysis network 22. The data analysis network 22 is used to analyze the second image data, the second behavior data, the second emotion data and the second relationship data to obtain the second psychological characteristic data of the second sample person, and upload the second psychological characteristic data to the distribution storage device 21.

[0115] In the embodiment of the present disclosure, after the data analysis network 22 transmits the first psychological characteristic data and the second psychological characteristic data to the distribution storage device 21, the first psychological characteristic data and / or the second psychological characteristic data can be obtained from the data analysis device 2 based on the first research requirement and the second research requirement, and the psychological analysis results can be determined according to the first psychological characteristic data and / or the second psychological characteristic data. Specifically, it can include at least the following centralized situations:

[0116] Optionally, the data analysis device 2 or the distribution storage device 21 receives the second research requirement sent by the second psychological research device 3, and feeds back the first psychological characteristic data to the second psychological research device 3 according to the second research requirement. Correspondingly, the second psychological research device 3 receives the first psychological characteristic data, and determines the first psychological analysis result according to the first psychological characteristic data. The first psychological characteristic data also carries the first image data.

[0117] Optionally, the data analysis device 2 or the distribution storage device 21 receives the second research requirement sent by the second psychological research device 3, and feeds back the first psychological characteristic data and the second psychological characteristic data to the second psychological research device 3 according to the second research requirement. Correspondingly, the second psychological research device 3 receives the first psychological characteristic data and the second psychological characteristic data, and determines the second psychological analysis result according to the first psychological characteristic data and the second psychological characteristic data. The first psychological characteristic data also carries the first image data, and the second psychological characteristic data also carries the second image data.

[0118] Optionally, the data analysis device 2 or the distribution storage device 21 receives the second research requirement sent by the second psychological research device 3, and feeds back the second psychological characteristic data to the second psychological research device 3 according to the second research requirement. Correspondingly, the second psychological research device 3 receives the second psychological characteristic data, and determines the third psychological analysis result according to the second psychological characteristic data. The second psychological characteristic data also carries the second image data.

[0119] Optionally, the data analysis device 2 or the distribution storage device 21 receives the first research requirement sent by the first psychological research device 1, and feeds back the first psychological characteristic data to the first psychological research device 1 according to the first research requirement. Correspondingly, the first psychological research device 1 receives the first psychological characteristic data, and determines the fourth psychological analysis result according to the first psychological characteristic data. The first psychological characteristic data also carries the first image data.

[0120] Optionally, the data analysis device 2 or the distribution storage device 21 receives the first research requirement sent by the first psychological research device 1, and feeds back the first psychological characteristic data and the second psychological characteristic data to the first psychological research device 1 according to the first research requirement. Correspondingly, the first psychological research device 1 receives the first psychological characteristic data and the second psychological characteristic data, and determines the fifth psychological analysis result according to the first psychological characteristic data and the second psychological characteristic data. The first psychological characteristic data also carries the first image data, and the second psychological characteristic data also carries the second image data.

[0121] Optionally, the data analysis device 2 or the distribution storage device 21 receives the first research requirement sent by the first psychological research device 1, and feeds back the second psychological characteristic data to the first psychological research device 1 according to the first research requirement. Correspondingly, the first psychological research device 1 receives the second psychological characteristic data, and determines the sixth psychological analysis result according to the second psychological characteristic data. The second psychological characteristic data also carries the second image data.

[0122] In the above embodiment, although the psychological characteristic data obtained by different psychological research devices are consistent, the psychological analysis results obtained may be different. This is because different psychological research devices have different research directions based on the same psychological characteristic data, which leads to differences in the psychological analysis results obtained.

[0123] In summary, the disclosed embodiments can achieve the research purpose by further processing psychological characteristic data by a data analysis device with more advanced software and hardware configuration, and the software and hardware configuration of multiple psychological research devices does not need to be too advanced. At the same time, the economic cost of psychological data research equipment is also controlled to a certain extent. In addition, data distribution through a unified data analysis device can save communication resources on the premise of achieving interaction between different psychological research devices and solving the problem of incomplete research data caused by the flow of sample personnel.

[0124] The disclosed embodiment also acquires image data of sample personnel by means of multiple image acquisition devices arranged in the activity area, and directly processes the data through the image recognition processing device and then uploads the data to the psychological research device, thus breaking the problem of limitation on the number of sample collection due to human and material resources.

[0125] Figure 7 A flowchart of a user psychology analysis method according to an embodiment of the present disclosure is shown. Figure 7 As shown, the method includes:

[0126] S701: receiving a first data analysis request sent by a first psychological research device, and parsing first data to be analyzed from the first data analysis request; wherein the first data to be analyzed includes first image data, first behavior data, second emotion data, and first relationship data corresponding to a first sample person;

[0127] S702: receiving a second data analysis request sent by a second psychological research device, and parsing second data to be analyzed from the second data analysis request; wherein the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data corresponding to a second sample person;

[0128] S703: Analyze the first data to be analyzed and the second data to be analyzed to obtain first psychological characteristic data of the first sample person and second psychological characteristic data of the second sample person;

[0129] S704: Sending the first psychological characteristic data and / or the second psychological characteristic data to the first psychological research device and the second psychological research device respectively according to the received first research requirement and the second research requirement, wherein the first psychological characteristic data and / or the second psychological characteristic data are used to determine the psychological analysis results according to the first research requirement and the second research requirement.

[0130] The specific implementation steps of the above-mentioned user psychology analysis method can refer to the user psychology analysis system and the transmission processing solution between various devices in the system, which will not be repeated here.

[0131] Figure 8 A block diagram of a user psychology analysis device according to an embodiment of the present disclosure is shown as follows: Figure 8 As shown, the user psychology analysis device includes:

[0132] The receiving module 801 is used to receive a first data analysis request sent by a first psychological research device, and parse out first data to be analyzed from the first data analysis request; wherein the first data to be analyzed includes first image data, first behavior data, second emotion data, and first relationship data corresponding to a first sample person; receive a second data analysis request sent by a second psychological research device, and parse out second data to be analyzed from the second data analysis request; wherein the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data corresponding to a second sample person;

[0133] The analysis module 802 is used to analyze the first data to be analyzed and the second data to be analyzed to obtain the first psychological characteristic data of the first sample person and the second psychological characteristic data of the second sample person;

[0134] The sending module 803 is used to send the first psychological characteristic data and / or the second psychological characteristic data to the first psychological research device and the second psychological research device respectively according to the received first research requirement and the second research requirement. The first psychological characteristic data and / or the second psychological characteristic data are used to determine the psychological analysis results according to the first research requirement and the second research requirement.

[0135] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above embodiments, and for the sake of brevity, it will not be repeated here.

[0136] The present disclosure also provides a computer-readable storage medium, wherein at least one instruction or at least one program is stored in the computer-readable storage medium, and when the at least one instruction or at least one program is loaded and executed by a processor, the above method is implemented. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0137] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method.

[0138] The electronic device may be provided as a terminal, a server, or a device in other forms.

[0139] An embodiment of the present disclosure provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the user psychology analysis method of the present disclosure.

[0140] Fig. 9 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. For example, the electronic device 900 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.

[0141] Reference Fig. 9 , the electronic device 900 may include one or more of the following components: a processing component 902 , a memory 904 , a power component 906 , a multimedia component 908 , an audio component 910 , an input / output (I / O) interface 912 , a sensor component 914 , and a communication component 916 .

[0142] The processing component 902 generally controls the overall operation of the electronic device 900, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 902 may include one or more modules to facilitate the interaction between the processing component 902 and other components. For example, the processing component 902 may include a multimedia module to facilitate the interaction between the multimedia component 908 and the processing component 902.

[0143] The memory 904 is configured to store various types of data to support operations on the electronic device 900. Examples of such data include instructions for any application or method operating on the electronic device 900, contact data, phone book data, messages, pictures, videos, etc. The memory 904 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0144] The power supply component 906 provides power to the various components of the electronic device 900. The power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 900.

[0145] The multimedia component 908 includes a screen that provides an output interface between the electronic device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front camera and / or a rear camera. When the electronic device 900 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0146] The audio component 910 is configured to output and / or input audio signals. For example, the audio component 910 includes a microphone (MIC), and when the electronic device 900 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 904 or sent via the communication component 916. In some embodiments, the audio component 910 also includes a speaker for outputting audio signals.

[0147] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0148] The sensor assembly 914 includes one or more sensors for providing various aspects of status assessment for the electronic device 900. For example, the sensor assembly 914 can detect the open / closed state of the electronic device 900, the relative positioning of components, such as the display and keypad of the electronic device 900, and the sensor assembly 914 can also detect the position change of the electronic device 900 or a component of the electronic device 900, the presence or absence of user contact with the electronic device 900, the orientation or acceleration / deceleration of the electronic device 900, and the temperature change of the electronic device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0149] The communication component 916 is configured to facilitate wired or wireless communication between the electronic device 900 and other devices. The electronic device 900 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 916 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 916 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0150] In an exemplary embodiment, the electronic device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0151] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 904 including computer program instructions, which can be executed by a processor 920 of an electronic device 900 to perform the above method.

[0152] Fig.10 1 is a block diagram of another electronic device according to an embodiment of the present disclosure. For example, the electronic device 1000 may be provided as a server. Fig.10 , the electronic device 1000 includes a processing component 1022, which further includes one or more processors, and a memory resource represented by a memory 1032 for storing instructions, such as applications, that can be executed by the processing component 1022. The application stored in the memory 1032 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1022 is configured to execute instructions to perform the above method.

[0153] The electronic device 1000 may also include a power supply component 1026 configured to perform power management of the electronic device 1000, a wired or wireless network interface 1050 configured to connect the electronic device 1000 to a network, and an input / output (I / O) interface 1058. The electronic device 1000 may operate based on an operating system stored in the memory 1032, such as Windows Server™, MacOS X™, Unix™, Linux™, FreeBSD™ or the like.

[0154] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1032 including computer program instructions, which can be executed by the processing component 1022 of the electronic device 1000 to perform the above method.

[0155] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0156] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0157] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0158] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0159] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0160] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0161] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0162] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0163] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A user psychology analysis system, characterized in that: The user psychology analysis system includes a first psychology research device, a data analysis device, and a second psychology research device; The first psychological research device is used to obtain first image data of a first sample person, and process the first image data to obtain first data to be analyzed of the first sample person; the first data to be analyzed includes first image data, first behavior data, first emotion data, and first relationship data; The second psychological research device is used to obtain second image data of a second sample person, and process the second image data to obtain second data to be analyzed of the second sample person; the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data; The data analysis device is used to analyze the first data to be analyzed and the second data to be analyzed respectively, to obtain the first psychological characteristic data of the first sample person and the second psychological characteristic data of the second sample person; the data analysis device includes a distribution storage device and a data analysis network; the distribution storage device is used to receive the first data to be analyzed from the first psychological research device; and send the first data to be analyzed to the data analysis network; The data analysis network is used to analyze the first image data, the first behavior data, the first emotion data and the first relationship data contained in the first data to be analyzed to obtain the first psychological characteristic data of the first sample person; and upload the first psychological characteristic data to the distribution storage device; The first psychological research device and the second psychological research device obtain the first psychological characteristic data and / or the second psychological characteristic data from the data analysis device based on the first research demand and the second research demand, respectively, and determine the psychological analysis results according to the first psychological characteristic data and / or the second psychological characteristic data, respectively.

2. The system according to claim 1, characterized in that The first psychological research device includes an image acquisition device set, an image recognition processing device and a psychological analysis device; The image acquisition device in the image acquisition device set is arranged in the activity area where the first sample person is located, and is used to acquire the first image data of the first sample person; The image recognition processing device is used to perform recognition processing on the first image data to determine the first behavior data of the first sample person; The psychological analysis device is used to determine the first emotion data and the first relationship data of the first sample person according to the first image data and the first behavior data; The first image data, the first behavior data, the first emotion data and the first relationship data are packaged to obtain the first data to be analyzed.

3. The system according to claim 2, characterized in that The activity area includes a plurality of the first sample persons; The image recognition processing device includes a face recognition module, which is used to perform face recognition on the first image data to determine the face data corresponding to each of the first sample persons.

4. The system according to claim 3, characterized in that The first behavior data of the first sample person includes gesture action data; The image recognition processing device includes a gesture and action recognition module, which is used to perform gesture and action recognition on the first image data corresponding to each of the first sample persons to determine the gesture and action data of each of the first sample persons.

5. The system according to claim 4, characterized in that The first behavior data of the first sample persons includes behavior trajectory data; the first image data corresponding to each of the first sample persons carries shooting location information; The image recognition processing device includes a trajectory integration module, which is used to perform trajectory integration on the first image data corresponding to each of the first sample persons and the location shooting information to determine the behavior trajectory data of each of the first sample persons.

6. The system according to claim 1, characterized in that The distribution storage device is used to feed back the first psychological characteristic data and / or the second psychological characteristic data according to the second research requirement sent by the second psychological research device; or; is used to feed back the first psychological characteristic data and / or the second psychological characteristic data according to the first research requirement sent by the first psychological research device; wherein the first psychological characteristic data carries the first image data, and the second psychological characteristic data carries the second image data.

7. The system according to claim 6, characterized in that The distribution storage device includes an alliance chain network, and the data analysis network is located in the alliance chain network; the alliance chain network is jointly constructed by multiple members as nodes; The alliance chain network includes a master node and a weak node; each of the master node and the weak node corresponds to a member; the master node includes a decision-making mechanism, and each node is provided with a storage area.

8. A user psychology analysis method, characterized in that: Applied to a data analysis device, the data analysis device includes a distribution storage device and a data analysis network, the method includes: Receiving a first data analysis request sent by a first psychological research device through the distribution storage device, parsing the first data to be analyzed from the first data analysis request; wherein the first data to be analyzed includes first image data, first behavior data, second emotion data, and first relationship data corresponding to a first sample person; Receiving a second data analysis request sent by a second psychological research device through the distribution storage device, parsing the second data to be analyzed from the second data analysis request; wherein the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data corresponding to the second sample person; Analyze the first data to be analyzed and the second data to be analyzed through the data analysis network to obtain first psychological characteristic data of the first sample person and second psychological characteristic data of the second sample person; The first psychological characteristic data and / or the second psychological characteristic data are sent to the first psychological research device and the second psychological research device respectively according to the received first research requirement and the second research requirement through the distribution storage device, and the first psychological characteristic data and / or the second psychological characteristic data are used to determine the psychological analysis results of the first research requirement and the second research requirement.

9. A user psychology analysis device, characterized in that: Applied to a data analysis device, the data analysis device includes a distribution storage device and a data analysis network, and the device includes: A receiving module, configured to receive a first data analysis request sent by a first psychological research device through the distribution storage device, and parse out first data to be analyzed from the first data analysis request; wherein the first data to be analyzed includes first image data, first behavior data, second emotion data, and first relationship data corresponding to a first sample person; receive a second data analysis request sent by a second psychological research device, and parse out second data to be analyzed from the second data analysis request; wherein the second data to be analyzed includes second image data, second behavior data, second emotion data, and second relationship data corresponding to a second sample person; An analysis module, configured to analyze the first data to be analyzed and the second data to be analyzed through the data analysis network to obtain first psychological characteristic data of the first sample person and second psychological characteristic data of the second sample person; A sending module is used to send the first psychological characteristic data and / or the second psychological characteristic data to the first psychological research device and the second psychological research device respectively through the distribution storage device according to the received first research requirement and the second research requirement, and the first psychological characteristic data and / or the second psychological characteristic data are used to determine the psychological analysis results of the first research requirement and the second research requirement.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement a user psychological analysis method as claimed in claim 8.

11. An electronic device, characterized in that: It includes at least one processor and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the at least one processor implements a user psychological analysis method as described in claim 8 by executing the instructions stored in the memory.

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