Cooperative capability assessment method and device, and storage medium
By collecting various types of data to construct a collaborative ability assessment model, extracting features and evaluating communication effectiveness, teamwork, leadership and followership, emotional intelligence, etc., this solves the problems of subjectivity and insufficient capture of non-verbal signals in existing collaborative ability assessments, and achieves more accurate and standardized assessment results.
Patent Information
- Application Number
- CN202511131995.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-13
- Publication Date
- 2025-12-16
AI Technical Summary
Existing technologies suffer from strong subjectivity, lack of standardization, insufficient objectivity, and low ecological validity when assessing the interpersonal interaction and collaboration abilities of individuals or teams. They are difficult to make horizontal comparisons across scenarios and teams, and traditional methods are unable to capture non-verbal signals and emotional dynamics in the collaboration process in real time.
By collecting facial, voice, body movement, physiological signal, and eye movement data of test subjects in collaborative scenarios, a collaborative ability assessment model is constructed. Features such as communication effectiveness, teamwork, leadership and followership, and emotional intelligence are extracted to achieve an objective and standardized assessment of collaborative ability.
It enables a comprehensive and accurate assessment of collaborative capabilities, improves the objectivity and standardization of the assessment, reduces the bias of the assessment results, and enhances the repeatability and fairness of the assessment.
Smart Images
Figure CN121146584A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] Embodiments of the present disclosure relate to the technical field of psychological measurement and organizational behavior assessment, and more particularly, to a collaboration ability assessment method, an electronic device and a storage medium. BACKGROUND
[0002] At present, the quantitative assessment of interpersonal interaction and collaboration ability of individuals or teams mainly relies on traditional means such as questionnaire survey, structured interview and on-site behavior observation. These methods have exposed the following common problems in actual application:
[0003] 1. Strong subjectivity. The evaluation results are highly dependent on the personal experience, cultural background and immediate mood of the evaluator, resulting in significant differences in conclusions about the same test subject by different evaluators;
[0004] 2. Lack of standardization. The index system, scoring rules and operation process adopted by each institution or evaluator are not uniform, making it difficult to realize horizontal comparison across scenes and teams, affecting the repeatability and fairness of the evaluation;
[0005] 3. Insufficient objectivity. Traditional methods are difficult to capture non-verbal signals, emotional dynamics and subtle patterns of team interaction in real time during the collaboration process, and key behavioral characteristics are easily missed;
[0006] 4. Low ecological validity. Questionnaires and interviews cannot restore the complex and dynamic real collaboration environment, resulting in a deviation between the evaluation results and the real task performance. SUMMARY
[0007] An object of embodiments of the present disclosure is to provide a new technical solution for collaboration ability assessment to solve at least one of the deficiencies of the existing multi-person collaboration ability assessment described in the background.
[0008] According to a first aspect of the present disclosure, a collaboration ability assessment method is provided, which comprises:
[0009] Obtaining collaboration performance data of different test objects in a collaboration scenario; wherein the collaboration performance data of the test objects comprises facial data, voice data, body movement data, physiological signal data, eye movement data of the test objects;
[0010] For each of the different test objects, determining collaboration ability related features of the test object according to the collaboration performance data of the test object; wherein the collaboration ability related features are features for characterizing the collaboration ability of the test object;
[0011] Determining the collaboration ability assessment result of the test object according to the collaboration ability related features and a collaboration ability assessment model;
[0012] output the collaboration ability evaluation result of the testee.
[0013] Optionally, the collaboration ability related features include a communication effectiveness feature, a team collaboration feature, a leadership and following feature, and an emotional intelligence feature, and the determining of the collaboration ability evaluation result of the testee according to the collaboration ability related features and a collaboration ability evaluation model comprises:
[0014] inputting the communication effectiveness feature into the collaboration ability evaluation model to obtain a communication ability score of the testee;
[0015] inputting the team collaboration feature and the leadership and following feature into the collaboration ability evaluation model to obtain a collaboration ability score of the testee;
[0016] inputting the emotional intelligence feature into the collaboration ability evaluation model to obtain an emotional intelligence score of the testee.
[0017] Optionally, the communication effectiveness feature includes at least one of an interaction frequency feature, a response speed feature, and a language clarity feature, the team collaboration feature includes at least one of a role allocation feature, a coordination degree feature, and a conflict resolution ability feature, the leadership and following feature includes at least one of an initiative feature, a guidance ability feature, and a support feature, and the emotional intelligence feature includes at least one of an empathy ability feature, an emotion regulation feature, and a social awareness feature.
[0018] Optionally, the determining of the collaboration ability evaluation result of the testee according to the collaboration ability related features and a collaboration ability evaluation model comprises:
[0019] inputting the collaboration ability related features into the collaboration ability evaluation model to obtain a first evaluation result of the testee;
[0020] determining a second evaluation result of the testee as the collaboration ability evaluation result of the testee according to a relative position ranking of the first evaluation result in a reference evaluation result set and an evaluation result conversion relationship.
[0021] Optionally, the obtaining of the collaboration performance data of different testees in a collaboration scenario comprises:
[0022] receiving task configuration parameters for constructing a virtual collaboration scenario, wherein the task configuration parameters include at least one of a virtual task type, a testee quantity, and a testee role;
[0023] constructing a corresponding virtual collaboration scenario according to the task configuration parameters, and respectively displaying the virtual collaboration scenario to different testees through a head-mounted display device.
[0024] In a case that the virtual collaboration scene is respectively displayed to different test objects through the head-mounted display device, collaboration performance data of the different test objects is acquired.
[0025] Optionally, after the collaboration ability evaluation result of the test object is determined, the method further comprises:
[0026] According to the collaboration ability evaluation result of the test object, corresponding improvement prompt information is output through the head-mounted display device of the test object.
[0027] Optionally, the collaboration ability evaluation model is obtained by training through the following steps:
[0028] A training sample set is acquired; wherein each training sample in the training sample set comprises sample collaboration ability related features and a target collaboration score;
[0029] The collaboration ability evaluation model is trained through the training sample set, to obtain a trained collaboration ability evaluation model.
[0030] Optionally, the collaboration ability related features of the test object are determined according to the collaboration performance data, comprising:
[0031] The collaboration performance data is preprocessed to obtain processed collaboration performance data;
[0032] The collaboration ability related features of the test object are determined according to the processed collaboration performance data.
[0033] According to a second aspect of the present disclosure, an electronic device is further provided, comprising a memory and a processor, the memory is configured to store a computer program, and the processor is configured to execute the computer program to implement the method according to the first aspect of the present disclosure.
[0034] According to a third aspect of the present disclosure, a computer readable storage medium is further provided, the computer readable storage medium stores a computer program, and the computer program implements the method according to the first aspect of the present disclosure when executed by a processor.
[0035] An advantage of the embodiments of the present disclosure is that by collecting facial data, voice data, body movement data, physiological signal data and eye movement data of the test object in the collaboration scene, the behavior characteristics of the test object can be recorded comprehensively. Moreover, by extracting collaboration ability related features of different test objects in the same collaboration scene, and evaluating the collaboration ability of any test object based on the collaboration ability related features of the test object, the objectivity and standardization of the collaboration ability evaluation can be realized, so that the accuracy of the collaboration ability evaluation of multiple test objects can be improved.
[0036] Other features of the present embodiments, and their potential advantages, will be apparent from the following detailed description of the exemplary embodiments of the present disclosure, and from the claims. BRIEF DESCRIPTION OF DRAWINGS
[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present embodiments.
[0038] Figure 1 is a hardware configuration block diagram of a collaboration capability assessment system that can be used to implement the present embodiments;
[0039] Figure 2 is a flowchart of a collaboration capability assessment method according to one embodiment;
[0040] Figure 3 is a structural diagram of an electronic device according to one embodiment. DETAILED DESCRIPTION
[0041] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps set forth in the embodiments, numerical expressions, and numerical values are not limiting to the scope of the present disclosure unless specifically stated otherwise.
[0042] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting to the scope of the present disclosure and its applications or uses.
[0043] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, the techniques, methods, and devices should be considered part of the specification, if appropriate.
[0044] In all of the examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation. Thus, other examples of the exemplary embodiments can have different values.
[0045] It should be noted that like numbers and letters refer to like items throughout the drawings, and that, once an item is defined in one drawing, it should not require further discussion in subsequent drawings.
[0046] <Hardware Configuration>
[0047] Figure 1 is a hardware configuration block diagram of a collaboration capability assessment system that can be used to implement the present embodiments.
[0048] As Figure 1As shown, the collaboration ability evaluation system 100 includes an electronic device 1000, a head-mounted display device 2000, a physiological signal acquisition apparatus 3000, a camera 4000, and a motion capture system 5000.
[0049] The head-mounted display device 2000 can be a head-mounted AR glasses or the like, which is not limited herein.
[0050] The head-mounted display device 2000 can display a picture of a virtual collaboration scene to immerse the user in the virtual collaboration scene.
[0051] The head-mounted display device 2000 further integrates an eye movement tracking device and a microphone. The eye movement tracking device is configured to acquire eye movement data of the test subject. The microphone is configured to acquire voice data of the test subject.
[0052] The physiological signal acquisition apparatus 3000 is configured to acquire physiological signal data of the test subject. The physiological signal data can be at least one of heart rate signal data and skin conductance data.
[0053] The physiological signal acquisition apparatus 3000 can be a non-contact sensor (e.g., a heart rate monitor, a skin conductance sensor) or a contact sensor to monitor heart rate, skin conductance, and brain activity, and evaluate stress level, cognitive load, and emotional state.
[0054] The camera 4000 is configured to acquire facial data of the test subject.
[0055] The motion capture system 5000 is configured to acquire limb movement data of the test subject.
[0056] In one example, one test subject corresponds to one head-mounted display device 2000, one physiological signal acquisition apparatus 3000, one camera 4000, and one motion capture system 5000.
[0057] The electronic device 1000 can be communicatively connected to at least one head-mounted display device 2000, at least one physiological signal acquisition apparatus 3000, at least one camera 4000, and at least one motion capture system 5000, respectively, to receive facial data, voice data, limb movement data, physiological signal data, and eye movement data of at least one test subject, and evaluate collaboration ability of the at least one test subject based on the facial data, the voice data, the limb movement data, the physiological signal data, and the eye movement data of the at least one test subject.
[0058] The electronic device 1000 can include a processor 1100, a memory 1200, an interface apparatus 1300, a communication apparatus 1400, a display apparatus 1500, an input apparatus 1600, a loudspeaker 1700, a microphone 1800, and the like.
[0059] The processor 1100 is configured to execute a computer program, which can be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, a ROM (Read-Only Memory), a RAM (Random Access Memory), a nonvolatile memory such as a hard disk, etc. The interface device 1300 includes, for example, a USB interface, a headphone interface, etc. The communication device 1400 is capable of wired or wireless communication, for example, and can include at least one short-range communication module, such as any module for short-range wireless communication based on a Hilink protocol, a WiFi (IEEE 802.11 protocol), a Mesh, a Bluetooth, a ZigBee, a Thread, a Z-Wave, an NFC, a UWB, a LiFi, etc., and can also include a long-range communication module, such as any module for WLAN, GPRS, 2G / 3G / 4G / 5G long-range communication. The display device 1500 is, for example, a liquid crystal display screen, a touch display screen, etc. The input device 1600 can include, for example, a touch screen, a keyboard, etc. The loudspeaker 1700 is configured to output an audio signal. The microphone 1800 is configured to acquire an audio signal.
[0060] The electronic device 1000 can be any type of electronic device having computing processing capability, which is not limited herein. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, an ultra-mobile personal computer (UMPC), etc., and can also be a server, a Network Attached Storage (NAS), a personal computer (PC), etc.
[0061] Those skilled in the art should understand that, although a plurality of devices of the electronic device 1000 are shown in Figure 1 , the electronic device 1000 of the embodiments of the present disclosure can only involve part of the devices, for example, only the processor 1100 and the memory 1200. This is well known in the art, and will not be described in detail herein.
[0062] In the present embodiment, the memory 1200 is configured to store computer program instructions for controlling the processor 1100 to perform operations to implement the cooperation capability evaluation method according to any embodiment of the present disclosure. Those skilled in the art can design the instructions according to the disclosed solutions. How the instructions control the processor 1100 to perform operations is well known in the art, and will not be described in detail herein.
[0063] <Method Embodiment>
[0064] Figure 2is a flowchart of a collaboration capability evaluation method according to an embodiment, which can be executed by Figure 1 the electronic device 1000 in the embodiment.
[0065] As shown in Figure 2 , the collaboration capability evaluation method of the embodiment can include the following steps S210-S240:
[0066] Step S210, obtain collaboration performance data of different test objects in a collaboration scenario.
[0067] In the embodiment, the collaboration scenario can be, for example, a conference room, an escape room, a project collaboration scenario, etc., which is not limited here.
[0068] The collaboration scenario can be a real collaboration scenario or a virtual collaboration scenario, which is not limited here.
[0069] In the example where the collaboration scenario is a virtual collaboration scenario, the test object can enter the virtual collaboration scenario through an AR headset and interact with other test objects or digital people.
[0070] There are multiple test objects in the collaboration scenario, i.e., multiple test objects participate in the collaboration capability evaluation. During the collaboration capability evaluation, the collaboration performance data of each test object in the collaboration scenario can be collected respectively, so that the collaboration capability of any test object can be evaluated based on the collaboration performance data of the test object.
[0071] The collaboration performance data of the test object can reflect the collaboration performance of the test object in the entire collaboration scenario. The collaboration performance data of the test object includes facial data, voice data, body movement data, physiological signal data, and eye movement data of the test object.
[0072] In the embodiment where the collaboration scenario is a virtual collaboration scenario, the collaboration capability evaluation system includes an electronic device, a head-mounted display device, a physiological signal acquisition device, a camera, and a motion capture system. The head-mounted display device worn by the test object can display a picture corresponding to the virtual collaboration scenario to make the test object immersed in the virtual collaboration scenario. The head-mounted display device is also integrated with an eye tracking device and a microphone. The eye tracking device is used to collect eye movement data of the test object. The microphone is used to collect voice data of the test object. The physiological signal acquisition device is used to collect physiological signal data of the test object. The physiological signal data can be at least one of heart rate signal data and skin conductance data. The physiological signal acquisition device can be a non-contact sensor (such as a heart rate monitor, a skin conductance sensor) or a contact sensor to monitor heart rate, skin conductance, and brain activity, and to evaluate stress level, cognitive load, and emotional state.
[0073] The camera is used to collect facial data of the test object. The motion capture system is used to collect limb motion data of the test object.
[0074] In one embodiment, the collaborative scenario is a virtual collaborative scenario.
[0075] Based on this, the step S210 of acquiring the collaborative performance data of different test objects in the collaborative scenario includes steps S2101-S2103.
[0076] Step S2101, receiving a task configuration parameter for constructing a virtual collaborative scenario.
[0077] The task configuration parameter includes at least one of a virtual task type, a number of test objects, and a role of a test object.
[0078] In this embodiment, the virtual task type can include a virtual conference room, a virtual escape room, a virtual project collaboration, etc., which is not limited here.
[0079] The number of test objects is the number of subjects participating in the virtual collaborative scenario.
[0080] The role of the test object is the role played by the test object in the virtual collaborative scenario.
[0081] Step S2102, according to the task configuration parameter, constructing a corresponding virtual collaborative scenario, and displaying the virtual collaborative scenario to different test objects through a head-mounted display device.
[0082] For example, the virtual task type is a virtual escape room task, the number of test objects is 5, and the roles of different test objects are different. Then, the virtual collaborative scenario is that 5 test objects participate in a virtual escape room task, and the task requires the team to solve a series of puzzles through communication and cooperation within 30 minutes. Each test object plays a different role (such as "team leader" and "technical expert"). The 5 test objects enter the virtual escape room task through the AR head-mounted display. The digital person serves as an additional team member or challenger, providing clues or asking interfering questions.
[0083] Step S2103, acquiring the collaborative performance data of the different test objects while displaying the virtual collaborative scenario to different test objects through a head-mounted display device.
[0084] In this embodiment, one test object corresponds to one head-mounted display device, one physiological signal acquisition device, one camera, and one motion capture system to collect the collaborative performance data of the test object.
[0085] By constructing a virtual collaboration scene, the problem that the evaluation result deviates from the real task performance due to the fact that the existing questionnaire and interview cannot restore the complex and dynamic real collaboration environment can be solved, and the accuracy of the collaboration ability evaluation can be improved.
[0086] In step S220, for each of the different test objects, a collaboration ability related feature of the test object is determined according to the collaboration performance data of the test object.
[0087] The collaboration ability related feature is a feature for representing the collaboration ability of the test object.
[0088] In one embodiment, the collaboration ability related feature includes a communication effectiveness feature, a team collaboration feature, a leadership and following feature, and an emotional intelligence feature.
[0089] In this embodiment, the communication effectiveness feature is used to represent the feature of the quality of information transmission of the test object in the collaboration scene. The team collaboration feature is used to represent the feature of the collective synergy efficiency of the test object in the collaboration scene. The leadership and following feature is used to represent the feature of the role driving and response ability of the test object in the collaboration scene. The emotional intelligence feature is used to represent the feature of the emotional interaction adaptability of the test object in the collaboration scene.
[0090] In one embodiment, the communication effectiveness feature includes at least one of an interaction frequency feature, a response speed feature, and a language clarity feature.
[0091] In one example, the communication effectiveness feature includes the interaction frequency feature, and in this example, step S220 determines the collaboration ability related feature of the test object according to the collaboration performance data of the test object, including:
[0092] According to the voice data of the test object, the interaction frequency feature of the test object is determined.
[0093] In this embodiment, the interaction frequency feature is the number of times of initiating or participating in a conversation by the test object per unit time. The voice data is recognized by the speaker log technology to count the number of speeches of the test object per minute. The interaction frequency feature can be used to represent the initiative feature and the participation degree of communication.
[0094] In one example, the communication effectiveness feature includes the response speed feature, and in this example, step S220 determines the collaboration ability related feature of the test object according to the collaboration performance data of the test object, including:
[0095] According to the voice data of the test object, the response speed feature of the test object is determined.
[0096] In this embodiment, the response speed feature is the average time from the end of the speech of other test subjects to the start of the response of the test subject itself. The response speed feature is determined by measuring the time interval between consecutive speech segments in the speech data. A shorter time interval indicates higher concentration and interaction efficiency.
[0097] In one example, the team collaboration feature includes a role assignment feature, in which case step S220 determines the collaboration ability related features of the test subject based on the collaboration performance data of the test subject, including:
[0098] The language clarity feature of the test subject is determined based on the speech data of the test subject.
[0099] In this embodiment, the language clarity feature is the structure, logic and intelligibility of language expression.
[0100] The speech clarity is obtained by evaluating the perplexity of the text corresponding to the speech data through a natural language model, combined with acoustic analysis to detect whether the speech speed is too fast / slow, whether the pauses are appropriate, etc. The speech clarity can represent the quality of information transmission.
[0101] In one embodiment, the team collaboration feature includes at least one of a role assignment feature, a coordination degree feature, and a conflict resolution capability feature.
[0102] In one example, the team collaboration feature includes a role assignment feature, in which case step S220 determines the collaboration ability related features of the test subject based on the collaboration performance data of the test subject, including:
[0103] The role assignment feature of the test subject is determined based on the speech data, body movement data and eye movement data of the test subject.
[0104] In this example, the role assignment feature is whether the test subject exhibits a stable and identifiable behavior pattern (such as an information collector, an executor, a coordinator) in the collaboration scenario. The role assignment feature of the test subject is determined by clustering the speech data, body movement data and eye movement data of the test subject using an unsupervised clustering algorithm (K-Means). The role assignment feature is used to represent the contribution of the test subject to the team.
[0105] In one example, the team collaboration feature includes a coordination degree feature, in which case step S220 determines the collaboration ability related features of the test subject based on the collaboration performance data of the test subject, including:
[0106] The coordination degree feature of the test subject is determined based on the speech data, body movement data and eye movement data of the test subject.
[0107] In this example, the coordination degree feature is the synchronization and complementarity of the language, actions of the test subject with other test subjects. The turn-taking pattern of the speech data and the action mimicry of the body movement data are analyzed, and the coordination degree feature is determined from the eye movement data. The coordination degree feature is used to represent the tacit understanding and fluidity of the team.
[0108] In one example, the team collaboration feature includes a conflict resolution capability feature, in which case step S220 determines, from the collaboration performance data of the test subject, a collaboration capability related feature of the test subject, including:
[0109] The conflict resolution capability feature of the test subject is determined from the speech data and facial data of the test subject.
[0110] In this example, the conflict resolution capability feature is the behavioral tendency (such as compromise, cooperation, avoidance) exhibited by the test subject when a disagreement arises. The negation, questioning vocabulary in the speech data is evaluated using a natural language model, and negative emotions (such as anger, contempt) are identified from the facial data; the speech data is acoustically analyzed to detect argumentative tone, and finally the subsequent behavioral trajectory of these signals is analyzed by a temporal model to determine the conflict resolution capability feature. The conflict resolution capability feature is used to represent the constructive ability of the test subject to handle team friction.
[0111] In one embodiment, the leadership and followership feature includes at least one of initiative feature, guidance capability feature, support feature.
[0112] In one example, the leadership and followership feature includes an initiative feature, in which case step S220 determines, from the collaboration performance data of the test subject, a collaboration capability related feature of the test subject, including:
[0113] The initiative feature of the test subject is determined from the speech data and body movement data of the test subject.
[0114] In this example, the initiative feature is the behavior of actively initiating new topics, proposing new solutions or taking on new tasks. The initiative feature is determined by detecting suggestive, guiding speech (such as "Let's try... ", "I think...") in the speech data using a natural language model, and identifying pointing, guiding gestures in the body movement data. The initiative feature is used to represent the willingness of the test subject to drive the team forward.
[0115] In one example, the leadership and followership feature includes a guidance capability feature, in which case step S220 determines, from the collaboration performance data of the test subject, a collaboration capability related feature of the test subject, including:
[0116] According to the voice data and the eye movement data of the test subject, a guiding ability feature of the test subject is determined.
[0117] In this example, the guiding ability feature is the ability to provide clear instructions, explanations or feedback to others. The frequency of imperative sentences and explanatory utterances in the voice data is analyzed using a natural language model, and whether the test subject gazes at the other person or shares information while explaining is analyzed through eye movement data, to determine the guiding ability feature. The guiding ability feature is used to represent the ability of the test subject to empower other test subjects.
[0118] In one example, the leadership and following features include a supportive feature, and in this example, step S220 determines, according to the collaboration performance data of the test subject, a collaboration ability-related feature of the test subject, including:
[0119] According to the voice data and the facial data of the test subject, a supportive feature of the test subject is determined.
[0120] In this example, the supportive feature is the ability to express affirmation, encouragement or provide help to others. Positive feedback such as smiles, nods, etc. in the facial data are identified, and approval, encouraging vocabulary (such as “good idea”, “I agree”) in the voice data are detected through a natural language model, to determine the supportive feature. The supportive feature is used to represent the positive contribution and team cohesion of the test subject as a follower.
[0121] In one embodiment, the emotional intelligence feature includes at least one of an empathy ability feature, an emotion regulation feature, and a social awareness feature.
[0122] In one example, the emotional intelligence feature includes an empathy ability feature, and in this example, step S220 determines, according to the collaboration performance data of the test subject, a collaboration ability-related feature of the test subject, including:
[0123] According to the voice data and the facial data of the test subject, an empathy ability feature of the test subject is determined.
[0124] In this example, the empathy ability feature is the ability to recognize and respond appropriately to the emotions of others.
[0125] Through the voice data and the facial data, the reaction of the test subject in terms of emotion or language after the other person shows a clear emotion (such as confusion, happiness) is analyzed to determine the empathy ability feature.
[0126] For example, the model detects whether test subject B will explain in simpler language after test subject A shows confusion.
[0127] The empathy ability feature is used to represent the sensitivity and understanding of the test subject to the emotions of others.
[0128] In one example, the emotional intelligence feature includes an emotion regulation feature, in which case, step S220 determines the collaboration ability related feature of the test subject according to the collaboration performance data of the test subject, including:
[0129] According to the facial data and the physiological signal data of the test subject, the emotion regulation feature of the test subject is determined.
[0130] In this example, the emotion regulation feature is the ability of the test subject to maintain emotional stability under stress or conflict.
[0131] In the high cognitive load or conflict segment, the duration of the facial negative emotion of the test subject and the stability of the physiological signal data (such as heart rate variability HRV) are analyzed to determine the emotion regulation feature of the test subject. The emotion regulation feature is used to represent the psychological resilience of the test subject.
[0132] In one example, the emotional intelligence feature includes a social awareness feature, in which case, step S220 determines the collaboration ability related feature of the test subject according to the collaboration performance data of the test subject, including:
[0133] According to the facial data, the body movement data and the eye movement data of the test subject, the social awareness feature of the test subject is determined.
[0134] In this example, the social awareness feature is the ability to perceive the overall emotional atmosphere of the team and the interpersonal dynamics.
[0135] The social awareness feature of an individual is determined by analyzing the eye movement data to determine whether the individual's line of sight is evenly distributed among team members, by analyzing the body movement data to determine whether the individual's body is oriented towards the communication center, and by analyzing the facial data to determine whether the individual's facial expression is consistent with the mainstream emotion of the team. The social awareness feature is used to represent the degree to which the individual is integrated into the team atmosphere.
[0136] In one embodiment, step S220 determines the collaboration ability related feature of the test subject according to the collaboration performance data, including steps S2201-S2202.
[0137] Step S2201, data preprocessing is performed on the collaboration performance data to obtain processed collaboration performance data.
[0138] In this embodiment, data preprocessing can include time synchronization processing, noise reduction (such as filtering), normalization processing, etc., which are not limited here.
[0139] Data preprocessing on the collaboration performance data can eliminate individual differences and environmental interference, and prepare for subsequent feature extraction.
[0140] Step S2202, determining the collaboration ability related features of the testee according to the processed collaboration performance data.
[0141] The way of extracting the collaboration ability related features in this step is basically the same as that described in step S220, which is not described here.
[0142] Step S230, determining the collaboration ability evaluation result of the testee according to the collaboration ability related features and the collaboration ability evaluation model.
[0143] In this embodiment, the collaboration ability evaluation model can be used to evaluate the collaboration ability of the testee to obtain the collaboration ability evaluation result.
[0144] In one embodiment, the collaboration ability related features include communication effectiveness features, team collaboration features, leadership and following features, and emotional intelligence features.
[0145] In this embodiment, determining the collaboration ability evaluation result of the testee according to the collaboration ability related features and the collaboration ability evaluation model in step S230 includes steps S2301-S2303.
[0146] Step S2301, inputting the communication effectiveness features into the collaboration ability evaluation model to obtain the communication ability score of the testee.
[0147] In this embodiment, the collaboration ability evaluation model can evaluate the communication ability of the testee according to the communication effectiveness features and output the communication ability score.
[0148] Step S2302, inputting the team collaboration features and the leadership and following features into the collaboration ability evaluation model to obtain the collaboration ability score of the testee.
[0149] In this embodiment, the collaboration ability evaluation model can evaluate the collaboration ability of the testee according to the team collaboration features and the leadership and following features and output the collaboration ability score.
[0150] Step S2303, inputting the emotional intelligence features into the collaboration ability evaluation model to obtain the emotional intelligence score of the testee.
[0151] In this embodiment, the collaboration ability evaluation model can evaluate the emotional intelligence of the testee according to the emotional intelligence features and output the emotional intelligence score.
[0152] That is, in this embodiment, the collaboration ability evaluation result of any testee includes the communication ability score, the collaboration ability score, and the emotional intelligence score.
[0153] In an embodiment, determining the collaboration ability evaluation result of the testee according to the collaboration ability related features and the collaboration ability evaluation model in step S230 includes steps S2311-S2312.
[0154] In step S2311, the collaboration ability related features are input into the collaboration ability evaluation model to obtain a first evaluation result of the testee.
[0155] In this embodiment, the first evaluation result is the collaboration ability evaluation result output by the collaboration ability evaluation model.
[0156] In embodiments in which the collaboration ability related features include the communication effectiveness features, the team collaboration features, the leadership and following features, and the emotional intelligence features, the first evaluation result is the communication ability score, the collaboration ability score, and the emotional intelligence score in steps S2301-S2303, which are absolute scores predicted by the model.
[0157] For the sake of distinction, the communication ability score, the collaboration ability score, and the emotional intelligence score in steps S2301-S2303 are referred to as the first communication ability score, the first collaboration ability score, and the first emotional intelligence score, respectively.
[0158] In step S2312, the second evaluation result of the testee is determined according to the relative position ranking of the first evaluation result in the reference evaluation result set and the evaluation result conversion relationship, and is taken as the collaboration ability evaluation result of the testee.
[0159] In this embodiment, the reference evaluation result set can be the evaluation result set obtained by inputting the training sample set into the collaboration ability evaluation model, can be the evaluation result set obtained by inputting the training sample set and the test sample set into the collaboration ability evaluation model, or can be the evaluation result set obtained based on the collaboration ability evaluation results of multiple testees participating in the collaboration ability evaluation this time, and is not limited herein.
[0160] The reference evaluation result set includes a reference communication ability score set, a reference collaboration ability score set, and a reference emotional intelligence score set. In this way, the second communication ability score can be determined according to the relative position ranking of the first communication ability score in the reference communication ability score set and the evaluation result conversion relationship. The second collaboration ability score can be determined according to the relative position ranking of the first collaboration ability score in the reference collaboration ability score set and the evaluation result conversion relationship. The second emotional intelligence score can be determined according to the relative position ranking of the first emotional intelligence score in the reference emotional intelligence score set and the evaluation result conversion relationship. The second communication ability score, the second collaboration ability score, and the second emotional intelligence score are relative scores.
[0161] The evaluation result conversion relationship can reflect a mapping relationship between a relative position ranking of the first evaluation result (i.e., an absolute evaluation result of the model) in the reference evaluation result set and the second evaluation result (i.e., a relative evaluation result).
[0162] For example, the first communication ability score is 60, the reference communication ability score set includes 100 communication ability scores, the first communication ability score has a relative position ranking of 30 in the reference communication ability score set, and the second communication ability score is determined to be 70 through the evaluation result conversion relationship. The first collaboration ability score is 50, the reference collaboration ability score set includes 100 collaboration ability scores, the first collaboration ability score has a relative position ranking of 40 in the reference collaboration ability score set, and the second collaboration ability score is determined to be 60 through the evaluation result conversion relationship. The first emotional intelligence score is 50, the reference emotional intelligence score set includes 100 emotional intelligence scores, the first emotional intelligence score has a relative position ranking of 50 in the reference emotional intelligence score set, and the second emotional intelligence score is determined to be 50 through the evaluation result conversion relationship.
[0163] By converting the absolute evaluation result output by the model into a relative evaluation result, a standardized evaluation result with relative comparison significance can be obtained.
[0164] In one embodiment, the collaboration ability evaluation model is obtained through the following steps: step S310 and step S320.
[0165] In step S310, a training sample set is obtained.
[0166] Each training sample in the training sample set includes sample collaboration ability related features and a target collaboration score.
[0167] In this embodiment, the target collaboration score of one training sample includes a target communication ability score, a target collaboration ability score, and a target emotional intelligence score.
[0168] In one embodiment, the sample collaboration ability related features include sample communication effectiveness features, sample team collaboration features, sample leadership and following features, and sample emotional intelligence features.
[0169] In this embodiment, obtaining the training sample set in step S310 includes steps S3101-S3102.
[0170] In step S3101, a sample data set is obtained.
[0171] In this embodiment, the sample data set includes a plurality of sample collaboration performance data.
[0172] In an embodiment, the sample collaborative performance data includes sample facial data, sample voice data, sample body movement data, sample physiological signal data, and sample eye movement data.
[0173] In step S3102, for any sample collaborative performance data in the sample data set, a sample collaboration ability related feature of the training sample is determined according to the sample collaborative performance data.
[0174] The method of extracting the collaboration ability related feature in this step is basically the same as that in step S220, which will not be repeated here.
[0175] In an embodiment, before extracting the sample collaboration ability related feature in step S3102, the sample collaborative performance data can also be preprocessed, and then the sample collaboration ability related feature of the training sample is determined according to the processed sample collaborative performance data.
[0176] In step S320, a collaboration ability evaluation model is trained through the training sample set to obtain a trained collaboration ability evaluation model.
[0177] In this embodiment, the action ability evaluation model can be a deep learning model based on attention mechanism (such as Transformer architecture). The model adopts a hybrid fusion strategy: at the feature level, different source feature vectors (i.e., sample communication effectiveness features, sample team collaboration features, sample leadership and following features, and sample emotional intelligence features) are weighted and fused through a cross-modal attention network; at the decision level, the fused features are mapped to three independent output heads (Output Head), respectively corresponding to the output predicted communication ability score, the output predicted emotional intelligence score, and the output predicted collaboration ability score.
[0178] When any training sample is input into the collaboration ability evaluation model, the model outputs the predicted communication ability score, the predicted emotional intelligence score, and the predicted collaboration ability score, and uses a loss function to calculate the gap between the predicted scores (i.e., the predicted communication ability score, the predicted emotional intelligence score, and the predicted collaboration ability score) and the target collaboration scores (i.e., the target communication ability score, the target emotional intelligence score, and the target collaboration ability score). The gap drives the collaboration ability evaluation model to update the internal parameters through a backpropagation algorithm to learn the complex mapping relationship from the input collaboration ability related features to the final three evaluation scores. Thus, the trained collaboration ability evaluation model is obtained. The performance of the trained collaboration ability evaluation model is evaluated on an independent test sample set, and the learning rate, the number of network layers, the number of attention heads, and other hyperparameters are systematically adjusted. The goal of optimization is to find a set of optimal hyperparameter combinations that can minimize the prediction error of the three ability scores of the model on the test sample set, so as to ensure the generalization ability of the model. Finally, the obtained model is the collaboration ability evaluation model in step S230.
[0179] Step S240, output the collaboration ability evaluation result of the testee.
[0180] In this embodiment, the collaboration ability evaluation result of the testee can be output through the head-mounted display device worn by the testee. Alternatively, the collaboration ability evaluation result of each testee in the current test can be output through the electronic device, which is not limited here.
[0181] In one example, the collaboration ability evaluation result includes a communication ability score, a collaboration ability score, and an emotional intelligence score.
[0182] In one embodiment, after determining the collaboration ability evaluation result of the testee in step S240, the method further includes step S410.
[0183] Step S410, according to the collaboration ability evaluation result of the testee, output corresponding improvement prompt information through the head-mounted display device of the testee.
[0184] In one example, the collaboration ability evaluation result includes a communication ability score, and step S410 of outputting corresponding improvement prompt information through the head-mounted display device of the testee according to the collaboration ability evaluation result of the testee includes steps S11-S13.
[0185] Step S11, in the case where the communication ability score is less than a first score threshold, output first improvement prompt information through the head-mounted display device of the testee.
[0186] Exemplarily, the first score threshold can be 50, and the first improvement prompt information can be “communication needs to be strengthened, please start with clear expression”.
[0187] Step S12, in the case where the communication ability score is greater than the first score threshold and less than a second score threshold, output second improvement prompt information through the head-mounted display device of the testee.
[0188] Exemplarily, the first score threshold can be 50, the second score threshold can be 80, and the second improvement prompt information can be “communication ability is acceptable, need to improve expression logic”.
[0189] Step S13, in the case where the communication ability score is greater than the second score threshold, output third improvement prompt information through the head-mounted display device of the testee.
[0190] Exemplarily, the second score threshold can be 80, and the third improvement prompt information can be “communication is accurate and efficient, and has great influence”.
[0191] In one example, the collaboration ability evaluation result includes a collaboration ability score, and the output of the corresponding improvement prompt information through the head-mounted display device of the testee according to the collaboration ability evaluation result of the testee in step S410 includes steps S21-S23.
[0192] In step S21, the fourth improvement prompt information is output through the head-mounted display device of the testee when the collaboration ability score is less than a first score threshold.
[0193] Exemplarily, the first score threshold can be 50, and the fourth improvement prompt information can be “Team collaboration awareness is not strong, please start from active participation”.
[0194] In step S22, the fifth improvement prompt information is output through the head-mounted display device of the testee when the collaboration ability score is greater than the first score threshold and less than a second score threshold.
[0195] Exemplarily, the first score threshold is 50, the second score threshold is 80, and the fifth improvement prompt information can be “Collaboration performance is qualified, and it is suggested to take more active team responsibility”.
[0196] In step S23, the sixth improvement prompt information is output through the head-mounted display device of the testee when the collaboration ability score is greater than the second score threshold.
[0197] Exemplarily, the second score threshold is 80, and the sixth improvement prompt information can be “Team spirit is excellent, and it is a model of active collaboration”.
[0198] In one example, the collaboration ability evaluation result includes an emotional intelligence score, and the output of the corresponding improvement prompt information through the head-mounted display device of the testee according to the collaboration ability evaluation result of the testee in step S410 includes steps S31-S33.
[0199] In step S31, the seventh improvement prompt information is output through the head-mounted display device of the testee when the emotional intelligence score is less than a first score threshold.
[0200] Exemplarily, the first score threshold can be 50, and the seventh improvement prompt information can be “Emotional awareness is insufficient, please start from cognitive self-emotion”.
[0201] In step S32, the eighth improvement prompt information is output through the head-mounted display device of the testee when the emotional intelligence score is greater than the first score threshold and less than a second score threshold.
[0202] Exemplarily, the first score threshold can be 50, the second score threshold can be 80, and the eighth improvement prompt information can be "emotional performance is stable, and can focus on improving the empathy ability feature".
[0203] Step S33, in the case where the emotional intelligence score is greater than the second score threshold, outputting ninth improvement prompt information through the head-mounted display device of the test object.
[0204] Exemplarily, the second score threshold can be 80, and the ninth improvement prompt information can be "emotional intelligence is outstanding, and can create a positive atmosphere".
[0205] By collecting facial data, voice data, body movement data, physiological signal data, and eye movement data of the test object in the cooperation scene, the behavior characteristics of the test object can be comprehensively recorded. Moreover, by extracting the respective cooperation ability related features of different test objects in the same cooperation scene, and based on the cooperation ability related features of any test object, the cooperation ability of the test object can be evaluated, the objectivity and standardization of the cooperation ability evaluation can be realized, and thus the accuracy of the cooperation ability evaluation of multiple test objects can be improved.
[0206] <Example>
[0207] The following is a cooperation ability evaluation method according to another embodiment, which includes steps s1-S11.
[0208] Step S1, receiving a task configuration parameter for constructing a virtual cooperation scene.
[0209] In this example, the task configuration parameter includes: the virtual task type is a virtual escape room task, the number of test objects is 5, and the roles of the test objects (such as "team leader" and "technical expert").
[0210] Step S2, constructing a corresponding virtual cooperation scene according to the task configuration parameter, and respectively displaying the virtual cooperation scene to different test objects through a head-mounted display device.
[0211] In this example, the virtual escape room scene is displayed to the five test objects through the respective head-mounted display devices of the test objects. Digital people serve as additional team members or challengers, and provide clues or ask interfering questions.
[0212] Step S3, in the case where the virtual cooperation scene is respectively displayed to different test objects through a head-mounted display device, obtaining cooperation performance data of different test objects.
[0213] In this example, the cooperation performance data includes facial data, voice data, body movement data, physiological signal data, and eye movement data of the test object.
[0214] Step S4, for each of the different test subjects, determining a collaboration ability related feature of the test subject according to the collaboration performance data of the test subject.
[0215] In the present example, the collaboration ability related feature is a feature for characterizing the collaboration ability of the test subject. The collaboration ability related feature includes a communication effectiveness feature, a team collaboration feature, a leadership and following feature, an emotional intelligence feature. The communication effectiveness feature includes an interaction frequency feature, a response speed feature, a language clarity feature, the team collaboration feature includes a role assignment feature, a coordination degree feature, a conflict resolution ability feature, the leadership and following feature includes an initiative feature, a guidance ability feature, a support feature, and the emotional intelligence feature includes an empathy ability feature, an emotion regulation feature, a social awareness feature.
[0216] Step S5, inputting the communication effectiveness feature into the collaboration ability evaluation model to obtain a first communication ability score of the test subject.
[0217] Step S6, inputting the team collaboration feature and the leadership and following feature into the collaboration ability evaluation model to obtain a first collaboration ability score of the test subject.
[0218] Step S7, inputting the emotional intelligence feature into the collaboration ability evaluation model to obtain a first emotional intelligence score of the test subject.
[0219] Step S8, obtaining a second communication ability score according to the relative position ranking of the first communication ability score in a reference communication ability score set and an evaluation result conversion relationship.
[0220] Step S9, obtaining a second collaboration ability score according to the relative position ranking of the first collaboration ability score in a reference collaboration ability score set and an evaluation result conversion relationship.
[0221] Step S10, obtaining a second emotional intelligence score according to the relative position ranking of the first emotional intelligence score in a reference emotional intelligence score set and an evaluation result conversion relationship.
[0222] Step S11, outputting corresponding improvement prompt information through the head-mounted display device of the test subject according to the second communication ability score, the second collaboration ability score, and the second emotional intelligence score of the test subject.
[0223] <Device Embodiment>
[0224] Figure 3 is a structural schematic diagram of an electronic device according to an embodiment.
[0225] As Figure 3As shown, the electronic device 300 includes a processor 310 and a memory 320 for storing a computer program executable, and the processor 310 is configured to execute the method according to the control of the computer program, as described in any of the embodiments of the present disclosure.
[0226] <Medium Embodiment>
[0227] In the embodiments of the present disclosure, a computer readable storage medium is also provided, which stores a computer program readable and executable by a computer, and the computer program is configured to execute the method according to any of the embodiments of the present disclosure when being read and executed by the computer.
[0228] The present application can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present application.
[0229] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, 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 disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a holographic storage medium, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
[0230] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.
[0231] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.
[0232] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.
[0233] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or nonvolatile memory, or a suitable combination of the different types of computer readable storage media. The computer readable program instructions can also be downloaded to a computer, other programmable data processing apparatus, or other device from a computer readable storage medium or to an external computer or external storage device via a data signal that can be transmitted for example via a wired medium or a wireless medium such as the Internet or Wireless Application Protocol (WAP) signaling.
[0234] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.
[0235] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and
[0236] Embodiments of the application have been described above. The description is illustrative of the embodiments of the application and is not meant to be limiting. Numerous modifications and variations are possible in light of the above teachings without departing from the scope and spirit of the described embodiments of the application. No limitation is intended to the details of construction or design except as described in the claims which follow.
Claims
1. A method of assessing collaboration capabilities, characterized by, The method comprises: obtaining collaboration performance data of different test objects in a collaboration scenario; wherein the collaboration performance data of the test objects comprises facial data, voice data, body movement data, physiological signal data, and eye movement data of the test objects; for each of the different test objects, determining collaboration ability related features of the test object according to the collaboration performance data of the test object; wherein the collaboration ability related features are features for representing the collaboration ability of the test object; determining a collaboration ability evaluation result of the test object according to the collaboration ability related features and a collaboration ability evaluation model; outputting the collaboration ability evaluation result of the test object.
2. The method of claim 1, wherein, The collaboration ability related features comprise communication effectiveness features, team collaboration features, leadership and following features, and emotional intelligence features, and the determining of the collaboration ability evaluation result of the test object according to the collaboration ability related features and the collaboration ability evaluation model comprises: inputting the communication effectiveness features into the collaboration ability evaluation model to obtain a communication ability score of the test object; inputting the team collaboration features and the leadership and following features into the collaboration ability evaluation model to obtain a collaboration ability score of the test object; inputting the emotional intelligence features into the collaboration ability evaluation model to obtain an emotional intelligence score of the test object.
3. The method of claim 2, wherein, The communication effectiveness features comprise at least one of interaction frequency features, response speed features, and language clarity features, the team collaboration features comprise at least one of role allocation features, coordination degree features, and conflict resolution ability features, the leadership and following features comprise at least one of initiative features, guidance ability features, and support features, and the emotional intelligence features comprise at least one of empathy ability features, emotion regulation features, and social awareness features.
4. The method of claim 1, wherein, The determining of the collaboration ability evaluation result of the test object according to the collaboration ability related features and the collaboration ability evaluation model comprises: inputting the collaboration ability related features into the collaboration ability evaluation model to obtain a first evaluation result of the test object; determining a second evaluation result of the test object as the collaboration ability evaluation result of the test object according to the relative position ranking of the first evaluation result in a reference evaluation result set and an evaluation result conversion relationship.
5. The method of claim 1, wherein, The obtaining of the collaboration performance data of different test objects in a collaboration scenario comprises: receiving task configuration parameters for constructing a virtual collaboration scenario; wherein the task configuration parameters comprise at least one of a virtual task type, a number of test objects, and roles of the test objects; constructing a corresponding virtual collaboration scenario according to the task configuration parameters, and respectively displaying the virtual collaboration scenario to different test objects through a head-mounted display device; under the condition that the virtual collaboration scenario is respectively displayed to different test objects through a head-mounted display device, obtaining the collaboration performance data of the different test objects.
6. The method of claim 1, wherein, After the determining of the collaboration ability evaluation result of the test object, the method further comprises: According to the cooperation ability evaluation result of the test object, corresponding improvement prompt information is output through a head-mounted display device of the test object.
7. The method of claim 1, wherein, The cooperation ability evaluation model is obtained through the following steps: Obtain a training sample set; each training sample in the training sample set includes sample cooperation ability related features and target cooperation scores; Train the cooperation ability evaluation model through the training sample set to obtain the trained cooperation ability evaluation model.
8. The method of claim 1, wherein, The cooperation ability related features of the test object are determined according to the cooperation performance data, including: Data preprocessing is performed on the cooperation performance data to obtain processed cooperation performance data; According to the processed cooperation performance data, the cooperation ability related features of the test object are determined. 9.An electronic device, comprising a memory and a processor, wherein the memory is configured to store a computer program, and the computer program is configured to control the processor to perform operations to execute the method according to any one of claims 1 to 8. 10.A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program, when executed by a processor, implements the method according to any one of claims 1 to 8.