A method, device, equipment and storage medium for monitoring and analyzing training behaviors

Abstract

This invention belongs to the field of practical training monitoring technology, specifically relating to a method, device, equipment, and storage medium for monitoring and analyzing practical training behavior. The method includes: S1, a first monitoring module collects and processes the speech data of trainees, and a second monitoring module collects and processes the action data of trainees; S2, a local analysis module compares and analyzes the features of the received speech data with the features of different standard speech data to determine the transmission time, and transmits the received speech data to a remote analysis module from the transmission time; S3, a remote analysis module compares and analyzes the features of the speech data sent from the local analysis module with the features of some standard speech data to obtain a second analysis result, and determines the speech analysis result based on the first and second analysis results; S4, a remote analysis module determines the final analysis result based on the speech analysis result and the action analysis result. This invention can ensure the accuracy of speech data recognition.

Description

Technical Field

[0001] This invention belongs to the field of practical training monitoring technology, specifically relating to a method, device, equipment, and storage medium for monitoring and analyzing practical training behavior. Background Technology

[0002] In aircraft emergency response training, relying on manual evaluation of trainees' performance results in high training costs. Therefore, methods have emerged to automatically collect trainees' verbal data and analyze it to automatically assess their performance.

[0003] Similar prior art includes Chinese patent application CN101622660A, which provides a speech recognition device, a speech recognition method, and a speech recognition program. The speech model storage unit (7) pre-stores speech models with multiple levels of detail representing speech characteristics. The detail judgment unit (9) selects the level of detail among the levels of detail of the speech models stored in the speech model storage unit (7) that is closest to the characteristic characteristics of the input signal. The parameter setting unit (10) controls speech recognition-related parameters based on the selected level of detail. Another similar prior art is Chinese patent application CN104485106A, which proposes a speech recognition method, a speech recognition system, and a speech recognition device. The speech recognition method includes: acquiring a speech string; creating a syntax analysis tree based on the speech string, the syntax analysis tree being used to determine whether the speech string can be initially recognized and to parse the lexical attribute information of at least one lexical in the speech string; and creating a semantic analysis tree based on the initial recognition result, the semantic analysis tree obtaining pre-stored attribute information based on the parsed lexical attribute information to create a speech recognition result. However, the identification methods for the two patent applications mentioned above are quite complex, and the accuracy of identification needs to be improved. Summary of the Invention

[0004] A first monitoring module collects speech data, and a second monitoring module collects action data. The first collection module continuously sends speech data to a local analysis module, but only sends speech data to a remote analysis module when the transmission time arrives. The remote analysis module determines the speech analysis result based on the first and second analysis results, and determines the final analysis result based on the speech analysis result and action analysis result. This invention aims to provide a simple, fast, and highly accurate method for recognizing speech data.

[0005] To achieve the aforementioned objectives, this invention provides a method for monitoring and analyzing training behaviors, which mainly includes the following steps:

[0006] S1. Set up a first monitoring module and a second monitoring module at the training site. The first monitoring module collects and processes the speech data of the trainees, and the second monitoring module collects and processes the action data of the trainees. The first monitoring module sends the collected speech data to the local analysis module set up at the training site, and the second monitoring module transmits the collected action data to the remote analysis module.

[0007] S2. The local analysis module compares and analyzes the features of the received speech data with the features of different pre-stored standard speech data to determine the transmission time. The local analysis module transmits the received speech data to the remote analysis module from the transmission time. The local analysis module also transmits the first analysis result of the received speech data to the remote analysis module.

[0008] S3. The remote analysis module compares and analyzes the features of the speech data sent by the local analysis module with the features of some pre-stored standard speech data to obtain a second analysis result, and the remote analysis module determines the speech analysis result based on the first analysis result and the second analysis result.

[0009] S4. The remote analysis module receives the action data sent by the second monitoring module, compares and analyzes the features of the received action data with the features of different pre-stored standard action data to obtain the action analysis results, and determines the final analysis result based on the speech analysis results and the action analysis results.

[0010] As a preferred technical solution of the present invention, the standard discourse data includes several words, and the process of generating partial standard discourse data corresponding to the standard discourse data includes dividing the several words that make up the standard discourse data into two groups, so that different words in the latter group constitute the partial standard discourse data.

[0011] As a preferred embodiment of the present invention, step S2 includes the following steps:

[0012] S21. For each standard speech data, the local analysis module compares and analyzes the features of the standard speech data with the features of the received speech data, calculates the difference value between the two, and determines whether there is a difference value that is changing from greater than the difference value threshold to less than the difference value threshold. If not, this step is repeated. If yes, the moment of successful determination is taken as the transmission moment, and the received speech data is transmitted to the remote analysis module from the transmission moment to continue to the next step.

[0013] S22. The local analysis module calculates the time interval from the transmission time to the current time, compares the features of the received speech data with the features of the standard speech data corresponding to the judged difference value, and calculates the difference value between the two. When the time interval does not reach the first time interval threshold but the difference value is greater than the difference value threshold two, this step is repeated. When the time interval does not reach the first time interval threshold but the difference value is less than or equal to the difference value threshold two, the first analysis result of successful analysis is generated and transmitted to the remote analysis module. When the time interval reaches the first time interval threshold but the difference value is greater than the difference value threshold two, the first analysis result of failed analysis is generated and transmitted to the remote analysis module.

[0014] As a preferred embodiment of the present invention, the difference threshold value two is less than the difference threshold value one.

[0015] As a preferred embodiment of the present invention, step S3 includes the following steps:

[0016] S31. When the remote analysis module obtains the speech data sent by the local analysis module, it compares and analyzes the features of the speech data with the features of some pre-stored standard speech data, calculates the degree of difference between the two, and calculates the time interval from the start of the analysis and processing to the current time.

[0017] S32. When the time interval does not reach the second time interval threshold but the difference value is greater than the difference value threshold three, the remote analysis module repeats S31. When the time interval does not reach the second time interval threshold but the difference value is less than or equal to the difference value threshold three, the remote analysis module generates the second analysis result of successful analysis and ends the analysis process. When the time interval reaches the second time interval threshold but the difference value is greater than the difference value threshold three, the remote analysis module generates the second analysis result of failed analysis and ends the analysis process.

[0018] S33. If both the first analysis result and the second analysis result are successful, the remote analysis module sets the discourse analysis result to success; otherwise, it sets the discourse analysis result to failure.

[0019] As a preferred embodiment of the present invention, the local analysis module transmits the received speech data to the remote analysis module, including the following steps:

[0020] S211. The local analysis module sets the count value p to 1, and the local analysis module takes the received speech data as the initial data, divides the initial data into first speech data and second speech data, performs specific operations on the first speech data and second speech data to obtain the p-th intermediate data, and increments the count value p by 1.

[0021] S212. The local analysis module takes the first speech data as the initial data, and continues to divide the initial data into the first speech data and the second speech data. It performs specific operations on the first speech data and the second speech data to obtain the p-th intermediate data. The local analysis module determines whether the termination condition is met. If not, it increments the count value p by 1 and repeats this step. If yes, it continues to the next step.

[0022] S213. The local analysis module performs secret processing on the last first speech data to obtain secret data, and the local analysis module sends the secret data, as well as the first intermediate data to the pth intermediate data, to the remote analysis module.

[0023] As a preferred embodiment of the present invention, the remote analysis module obtains the speech data sent by the local analysis module through the following steps:

[0024] S311. The local analysis module performs recovery processing on the secret data to obtain the last first speech data, performs specific operations on the last first speech data and the p-th intermediate data to obtain the last second speech data, recovers the previous first speech data from the last first speech data and the last second speech data, and decrements the count value p by 1.

[0025] S312. The local analysis module performs specific operations on the previous first speech data and the p-th intermediate data to obtain the previous second speech data, recovers the new previous first speech data from the previous first speech data and the previous second speech data, decrements the count value p by 1, and determines whether the count value p is equal to 1. If not, repeat this step; if yes, continue to the next step.

[0026] S313, The local analysis module performs specific operations on the previous first speech data and the first intermediate data to obtain the previous second speech data, and recovers the initial data from the previous first speech data and the previous second speech data.

[0027] This invention also provides a training behavior monitoring and analysis device, comprising the following modules:

[0028] The first monitoring module is set up at the training site to collect and process the speech data of the trainees, and to send the collected speech data to the local analysis module set up at the training site.

[0029] The second monitoring module is set up at the training site to collect and process the action data of the trainees, and to transmit the collected action data to the remote analysis module.

[0030] The local analysis module is used to compare and analyze the features of the received speech data with the features of different pre-stored standard speech data to determine the transmission time, and transmit the received speech data to the remote analysis module from the transmission time. It also transmits the first analysis result of the received speech data to the remote analysis module.

[0031] The remote analysis module is used to compare and analyze the features of the speech data sent by the local analysis module with the features of some pre-stored standard speech data to obtain a second analysis result. Based on the first analysis result and the second analysis result, the speech analysis result is determined. It is also used to receive the action data sent by the second monitoring module, compare and analyze the features of the received action data with the features of different pre-stored standard action data to obtain the action analysis result, and determine the final analysis result based on the speech analysis result and the action analysis result.

[0032] The present invention also provides an apparatus including a memory and a processor, the memory being used to store a computer program, and the processor being used to implement the method described in any one of the foregoing when executing the computer program.

[0033] The present invention also provides a storage medium storing program instructions, wherein the program instructions, when executed, control the device where the storage medium is located to perform any of the methods described above.

[0034] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0035] In this invention, firstly, a first monitoring module collects and processes the speech data of trainees, and a second monitoring module collects and processes the action data of trainees. The first monitoring module sends the collected speech data to a local analysis module located at the training site, and the second monitoring module transmits the collected action data to a remote analysis module. Secondly, the local analysis module compares the features of the received speech data with the features of different standard speech data to determine the transmission time, and transmits the received speech data to the remote analysis module from the transmission time, also transmitting the first analysis result of the received speech data to the remote analysis module. Thirdly, the remote analysis module compares the features of the speech data sent by the local analysis module with the features of some standard speech data to obtain a second analysis result, and determines the speech analysis result based on the first and second analysis results. Finally, the remote analysis module receives the action data sent by the second monitoring module, compares the features of the received action data with the features of different standard action data to obtain the action analysis result, and determines the final analysis result based on the speech analysis result and the action analysis result. This invention not only enables simple and rapid identification of trainees' speech data while ensuring accuracy, but also guarantees the security of speech data transmitted from the local analysis module to the remote analysis module. Attached Figure Description

[0036] Figure 1 This is a flowchart of a practical training behavior monitoring and analysis method according to the present invention;

[0037] Figure 2 This is a structural diagram of a training behavior monitoring and analysis device according to the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0039] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0040] This invention provides, for example Figure 1 The training behavior monitoring and analysis method shown is mainly implemented by performing the following steps:

[0041] S1. Set up a first monitoring module and a second monitoring module at the training site. The first monitoring module collects and processes the speech data of the trainees, and the second monitoring module collects and processes the action data of the trainees. The first monitoring module sends the collected speech data to the local analysis module set up at the training site, and the second monitoring module transmits the collected action data to the remote analysis module.

[0042] S2. The local analysis module compares and analyzes the features of the received speech data with the features of different pre-stored standard speech data to determine the transmission time. The local analysis module transmits the received speech data to the remote analysis module from the transmission time. The local analysis module also transmits the first analysis result of the received speech data to the remote analysis module.

[0043] S3. The remote analysis module compares and analyzes the features of the speech data sent by the local analysis module with the features of some pre-stored standard speech data to obtain a second analysis result, and the remote analysis module determines the speech analysis result based on the first analysis result and the second analysis result.

[0044] S4. The remote analysis module receives the motion data sent by the second monitoring module, compares and analyzes the features of the received motion data with the features of different pre-stored standard motion data to obtain the motion analysis results, and determines the final analysis result based on the speech analysis results and the motion analysis results.

[0045] Specifically, in order to achieve automatic analysis of trainees' performance in aircraft emergency response training, S1 to S4 are proposed. In actual application scenarios, S1 to S4 are executed repeatedly to analyze whether multiple statements and corresponding actions of trainees are standardized. The correspondence between statements and actions means that statements and actions occur in the same time period. For ease of understanding, this embodiment takes the automatic analysis of a trainee's statement and corresponding action as an example.

[0046] In S1, a first monitoring module and a second monitoring module are set up at the training site. The first monitoring module collects and processes the trainees' speech data, while the second monitoring module collects and processes their action data, such as images showing the trainees' actions. The first monitoring module then sends the collected speech data to a local analysis module, which is also located at the training site. The second monitoring module transmits the collected action data to a remote analysis module. It's important to note that the first monitoring module collects speech data and sends it to the local analysis module simultaneously. The second monitoring module, before the trainee finishes speaking a sentence, collects typical images (i.e., action data) for the corresponding time period and transmits them to the remote analysis module. In S2, the local analysis module compares the features of the received speech data with the features of different standard speech data to determine the transmission time. From this transmission time, it transmits the received speech data to the remote analysis module and then transmits the first analysis result of the received speech data to the remote analysis module. The detailed process of S2 will be described below. In S3, the remote analysis module compares the features of the speech data sent by the local analysis module with the features of some standard speech data to obtain a second analysis result. It also determines the speech analysis result based on the first and second analysis results. The detailed process of S3 will be described below. In S4, the remote analysis module receives action data sent by the second monitoring module and compares the features of the received action data with the features of different standard action data simultaneously to obtain the action analysis result. That is, the remote analysis module compares the received image with multiple standard images for consistency. The actions in the standard images are the actions that the trainee should perform during the time period of speaking the corresponding standard speech data. The remote analysis module considers the action in the standard image with the highest consistency score to be the action in the received image. If the consistency score is higher than the consistency score threshold, a successful action analysis result is generated, meaning the trainee's action is considered standard. Then, the remote analysis module determines the final analysis result based on the speech analysis result and the action analysis result. Specifically, a successful final analysis result is only generated when both the speech analysis result and the action analysis result are successful.

[0047] Furthermore, the standard discourse data includes several words, and the process of generating partial standard discourse data corresponding to the standard discourse data includes dividing the several words that make up the standard discourse data into two groups, so that different words in the latter group constitute the partial standard discourse data.

[0048] Specifically, standard discourse data is introduced here. Standard discourse data refers to the discourse data that trainees should speak during training. For example, a standard discourse data is "cross your arms and hug your chest". In addition, in order to obtain partial standard discourse data corresponding to the standard discourse data, the words that make up the standard discourse data are first divided into two groups. Then, the different words in the second group constitute the partial standard discourse data. For example, "cross your arms" corresponds to the different words in the first group, and "hug your chest" corresponds to the different words in the second group. That is, "hug your chest" is the partial standard discourse data corresponding to "cross your arms and hug your chest".

[0049] Furthermore, S2 includes the following steps:

[0050] S21. For each standard speech data, the local analysis module compares and analyzes the features of the standard speech data with the features of the received speech data, calculates the difference value between the two, and determines whether there is a difference value that is changing from greater than the difference value threshold to less than the difference value threshold. If not, this step is repeated. If yes, the moment of successful determination is taken as the transmission moment, and the received speech data is transmitted to the remote analysis module from the transmission moment to continue to the next step.

[0051] S22. The local analysis module calculates the time interval from the transmission time to the current time, compares the features of the received speech data with the features of the standard speech data corresponding to the judged difference value, and calculates the difference value between the two. When the time interval does not reach the first time interval threshold but the difference value is greater than the difference value threshold two, this step is repeated. When the time interval does not reach the first time interval threshold but the difference value is less than or equal to the difference value threshold two, a first analysis result of successful analysis is generated and transmitted to the remote analysis module. When the time interval reaches the first time interval threshold but the difference value is greater than the difference value threshold two, a first analysis result of failed analysis is generated and transmitted to the remote analysis module.

[0052] Furthermore, the threshold value for the degree of difference is less than the threshold value for the degree of difference.

[0053] Specifically, the detailed process of S2 is described here. In S21, for each standard discourse data, the local analysis module compares and analyzes the characteristics of the standard discourse data with the characteristics of the received discourse data. For example, it compares and analyzes the fluctuation patterns corresponding to the standard discourse data and the fluctuation patterns of the received discourse data, and calculates the degree of difference between the two. During the initial period when the first monitoring module sends discourse data to the local analysis module, S21 is executed multiple times. The degree of difference is consistently large, but it shows a gradual decreasing trend. The local analysis module always determines whether there is a degree of difference that is changing from being greater than the degree of difference threshold to being less than the degree of difference threshold, that is, it always determines that the degree of difference is equal to the degree of difference value. If the threshold one moment has arrived, the moment of successful judgment, that is, the moment when the difference degree value equals the difference degree value threshold one, is taken as the transmission moment. At this time, it is considered that the features of the received speech data match the features of different words in the previous group of the corresponding standard speech data. The received speech data is transmitted to the remote analysis module from the transmission moment. It should be noted that because the local analysis module compares and analyzes the features of the speech data sent by the first monitoring module with the features of multiple standard speech data at the same time, the moment corresponding to the earliest judgment of the difference degree value is used as the transmission moment. If not, the local analysis module continues to receive the speech data sent by the first monitoring module and repeats S21. In step S22, the local analysis module continues to receive speech data sent by the first monitoring module, calculates the time interval from the transmission time to the current time, and compares the features of the received speech data with the features of the standard speech data corresponding to the determined difference value. It calculates the difference value between the two. If the time interval does not reach the first time interval threshold and the difference value is greater than the second difference value threshold, step S22 is repeated. If the time interval does not reach the first time interval threshold and the difference value is less than or equal to the second difference value threshold, the features of the received speech data are considered to match the features of the corresponding standard speech data, generating a successful first analysis result, which is then transmitted to the remote analysis module. If the time interval reaches the first time interval threshold and the difference value is greater than the second difference value threshold, the features of the received speech data are considered to not match the features of the corresponding standard speech data, generating a failed first analysis result, which is also transmitted to the remote analysis module. It should be noted that the second difference value threshold is less than the first difference value threshold. Through this method, the local analysis module can quickly obtain the first analysis result regarding the speech data sent by the first monitoring module.

[0054] Furthermore, S3 includes the following steps:

[0055] S31. When the remote analysis module receives the speech data sent by the local analysis module, it compares and analyzes the features of the speech data with the features of some pre-stored standard speech data, calculates the degree of difference between the two, and calculates the time interval from the start of the analysis and processing to the current time.

[0056] S32. When the time interval does not reach the second time interval threshold but the difference value is greater than the difference value threshold three, the remote analysis module repeats S31. When the time interval does not reach the second time interval threshold but the difference value is less than or equal to the difference value threshold three, the remote analysis module generates a second analysis result of successful analysis and ends the analysis process. When the time interval reaches the second time interval threshold but the difference value is greater than the difference value threshold three, the remote analysis module generates a second analysis result of failed analysis and ends the analysis process.

[0057] S33. If both the first and second analysis results are successful, the remote analysis module will set the discourse analysis result as successful; otherwise, it will set the discourse analysis result as unsuccessful.

[0058] Specifically, the detailed process of S3 is described here. In S31, when the remote analysis module obtains the speech data sent by the local analysis module, it compares and analyzes the features of the speech data with the features of the pre-stored partial standard speech data. The partial standard speech data refers to the partial standard speech data corresponding to the standard speech data corresponding to the value of the difference that exists. Thus, the difference value between the two is calculated. At the same time, the remote analysis module calculates the time interval from the start time of the analysis and processing to the current time. In S32, if the time interval does not reach the second time interval threshold and the difference value is greater than the third difference value threshold, the remote analysis module repeats S31. If the time interval does not reach the second time interval threshold and the difference value is less than or equal to the third difference value threshold, the remote analysis module considers the features of the speech data sent by the local analysis module to match the features of some standard speech data, generates a successful second analysis result, and ends the analysis process. That is, when no speech data is received from the local analysis module, the remote analysis module stops the analysis process from S31 to S33, which reduces the processing burden. If the time interval reaches the second time interval threshold and the difference value is greater than the third difference value threshold, the remote analysis module considers the features of the speech data sent by the local analysis module to not match the features of some standard speech data, generates a failed second analysis result, and ends the analysis process. In S33, if both the first and second analysis results are successful, the remote analysis module sets the speech analysis result to success, meaning that the speech spoken by the trainee during the training is standard; otherwise, the remote analysis module sets the speech analysis result to failure. Using the above methods, the remote analysis module can quickly generate a second analysis result on the speech data sent by the local analysis module, and comprehensively consider the second analysis result and the first analysis result generated by the local analysis module to determine the speech analysis result, thus ensuring the accuracy of the speech analysis result.

[0059] Furthermore, the local analysis module transmits the received speech data to the remote analysis module, including the following steps:

[0060] S211. The local analysis module sets the count value p to 1, and the local analysis module takes the received speech data as the initial data, divides the initial data into the first speech data and the second speech data, performs specific operations on the first speech data and the second speech data to obtain the p-th intermediate data, and increments the count value p by 1.

[0061] S212. The local analysis module takes the first speech data as the initial data, and continues to divide the initial data into the first speech data and the second speech data. It performs specific operations on the first speech data and the second speech data to obtain the p-th intermediate data. The local analysis module determines whether the termination condition is met. If not, it increments the count value p by 1 and repeats this step. If yes, it continues to the next step.

[0062] S213. The local analysis module performs secret processing on the last first speech data to obtain secret data, and the local analysis module sends the secret data, as well as the first intermediate data to the pth intermediate data, to the remote analysis module.

[0063] Specifically, this section describes the detailed process of the local analysis module transmitting the received speech data to the remote analysis module. In S211, the local analysis module sets the count value p to 1 and treats the received speech data as initial data. It divides the initial data into first speech data and second speech data, which have the same data size. Specific operations are performed on the first and second speech data to obtain the p-th intermediate data. For ease of understanding, a specific operation on 0 and 1 yields 1, while specific operations on 0 and 0, and 1 and 1 yield 0. The count value p is then incremented by 1. In S212, the local analysis module treats the first speech data as initial data and continues to divide the initial data into first and second speech data. Specific operations are performed on the first and second speech data to obtain the p-th intermediate data. The analysis module then determines whether the termination condition is met, specifically whether p is greater than or equal to a preset threshold P. If not, the count value p is incremented by 1, and S212 is repeated. If yes, S213 is executed. In S213, the local analysis module performs secret processing on the last first speech data to obtain secret data, which is the first speech data that generates the p-th intermediate data. Here, p and P are the same. The secret processing can be, for example, using an encryption algorithm in the prior art. The local analysis module also sends the secret data, as well as the first intermediate data, the second intermediate data, and so on up to the p-th intermediate data, to the remote analysis module. Here, p and P are the same. Through the above method, even if the data sent by the local analysis module to the remote analysis module is illegally intercepted, the speech data sent by the local analysis module cannot be obtained, thus ensuring the security of the speech data.

[0064] Furthermore, the remote analysis module obtains the speech data sent by the local analysis module through the following steps:

[0065] S311. The local analysis module recovers the secret data to obtain the last first speech data, performs specific operations on the last first speech data and the p-th intermediate data to obtain the last second speech data, recovers the previous first speech data from the last first speech data and the last second speech data, and decrements the count value p by 1.

[0066] S312. The local analysis module performs specific operations on the previous first speech data and the p-th intermediate data to obtain the previous second speech data. It recovers the new previous first speech data from the previous first speech data and the previous second speech data, decrements the count value p by 1, and determines whether the count value p is equal to 1. If not, this step is repeated. If yes, the next step is continued.

[0067] S313. The local analysis module performs specific operations on the previous first speech data and the first intermediate data to obtain the previous second speech data, and recovers the initial data from the previous first speech data and the previous second speech data.

[0068] Specifically, this section describes how the remote analysis module obtains the speech data sent by the local analysis module. In S311, the local analysis module recovers the secret data to obtain the last first speech data, which is also the first speech data that generates the p-th intermediate data (where p and P are the same). It then performs specific operations on the last first speech data and the p-th intermediate data to obtain the last second speech data. Furthermore, it recovers the previous first speech data from the last first and last second speech data by concatenating them, and then decrements the count value p by 1. In S312, the local analysis module performs specific operations on the previous first speech data and the p-th intermediate data to obtain the previous second speech data. It then recovers a new previous first speech data from the previous first and second speech data. This new previous first speech data actually refers to the first speech data preceding the current previous first speech data. Finally, it decrements the count value p by 1 and checks if the count value p equals 1. If not, it repeats S312; otherwise, it continues with S313. In S313, the local analysis module performs specific operations on the previous first speech data and the first intermediate data. The previous first speech data actually refers to the previous first speech data finally obtained after multiple executions of S312, which is used to obtain the previous second speech data. The initial data is then recovered from the previous first speech data and the previous second speech data. Through the above method, the remote analysis module can recover speech data from the data sent by the local analysis module.

[0069] According to another aspect of the embodiments of the present invention, reference is made to... Figure 2 As shown, a training behavior monitoring and analysis device is also provided, including a first monitoring module, a second monitoring module, a local analysis module, and a remote analysis module, to implement the training behavior monitoring and analysis method described above. The functions of each module are as follows:

[0070] The first monitoring module is set up at the training site to collect and process the speech data of the trainees, and to send the collected speech data to the local analysis module set up at the training site.

[0071] The second monitoring module is set up at the training site to collect and process the action data of the trainees, and to transmit the collected action data to the remote analysis module.

[0072] The local analysis module is used to compare and analyze the features of the received speech data with the features of different pre-stored standard speech data to determine the transmission time, and transmit the received speech data to the remote analysis module from the transmission time. It also transmits the first analysis result of the received speech data to the remote analysis module.

[0073] The remote analysis module is used to compare and analyze the features of the speech data sent by the local analysis module with the features of some pre-stored standard speech data to obtain a second analysis result. Based on the first analysis result and the second analysis result, the speech analysis result is determined. It is also used to receive the action data sent by the second monitoring module, compare and analyze the features of the received action data with the features of different pre-stored standard action data to obtain the action analysis result, and determine the final analysis result based on the speech analysis result and the action analysis result.

[0074] According to another aspect of the present invention, an apparatus is also provided, including a memory and a processor, the memory being used to store a computer program, and the processor being used to implement the method of any one of the above when executing the computer program.

[0075] According to another aspect of the present invention, a storage medium is also provided, which stores program instructions, wherein the program instructions, when executed, control the device where the storage medium is located to perform any of the methods described above.

[0076] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0077] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0078] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] The above-described embodiments are merely examples of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring and analyzing practical training behavior, characterized in that, Includes the following steps: S1. Set up a first monitoring module and a second monitoring module at the training site. The first monitoring module collects and processes the speech data of the trainees, and the second monitoring module collects and processes the action data of the trainees. The first monitoring module sends the collected speech data to the local analysis module set up at the training site, and the second monitoring module transmits the collected action data to the remote analysis module. S2. The local analysis module compares and analyzes the features of the received speech data with the features of different pre-stored standard speech data to determine the transmission time. The local analysis module transmits the received speech data to the remote analysis module from the transmission time. The local analysis module also transmits the first analysis result of the received speech data to the remote analysis module. S3. The remote analysis module compares and analyzes the features of the speech data sent by the local analysis module with the features of some pre-stored standard speech data to obtain a second analysis result, and the remote analysis module determines the speech analysis result based on the first analysis result and the second analysis result. S4. The remote analysis module receives the action data sent by the second monitoring module, compares and analyzes the features of the received action data with the features of different pre-stored standard action data to obtain the action analysis results, and determines the final analysis result based on the speech analysis results and the action analysis results.

2. The method according to claim 1, characterized in that, The standard discourse data includes several words, and the process of generating partial standard discourse data corresponding to the standard discourse data includes dividing the several words that make up the standard discourse data into two groups, so that different words in the latter group constitute the partial standard discourse data.

3. The method according to claim 2, characterized in that, S2 includes the following steps: S21. For each standard speech data, the local analysis module compares and analyzes the features of the standard speech data with the features of the received speech data, calculates the difference value between the two, and determines whether there is a difference value that is changing from greater than the difference value threshold to less than the difference value threshold. If not, this step is repeated. If yes, the moment of successful determination is taken as the transmission moment, and the received speech data is transmitted to the remote analysis module from the transmission moment to continue to the next step. S22. The local analysis module calculates the time interval from the transmission time to the current time, compares the features of the received speech data with the features of the standard speech data corresponding to the judged difference value, and calculates the difference value between the two. When the time interval does not reach the first time interval threshold but the difference value is greater than the difference value threshold two, this step is repeated. When the time interval does not reach the first time interval threshold but the difference value is less than or equal to the difference value threshold two, the first analysis result of successful analysis is generated and transmitted to the remote analysis module. When the time interval reaches the first time interval threshold but the difference value is greater than the difference value threshold two, the first analysis result of failed analysis is generated and transmitted to the remote analysis module.

4. The method according to claim 3, characterized in that, The threshold value for the degree of difference is less than the threshold value for the degree of difference.

5. The method according to claim 3, characterized in that, S3 includes the following steps: S31. When the remote analysis module obtains the speech data sent by the local analysis module, it compares and analyzes the features of the speech data with the features of some pre-stored standard speech data, calculates the degree of difference between the two, and calculates the time interval from the start of the analysis and processing to the current time. S32. When the time interval does not reach the second time interval threshold but the difference value is greater than the difference value threshold three, the remote analysis module repeats S31. When the time interval does not reach the second time interval threshold but the difference value is less than or equal to the difference value threshold three, the remote analysis module generates the second analysis result of successful analysis and ends the analysis process. When the time interval reaches the second time interval threshold but the difference value is greater than the difference value threshold three, the remote analysis module generates the second analysis result of failed analysis and ends the analysis process. S33. If both the first analysis result and the second analysis result are successful, the remote analysis module sets the discourse analysis result to success; otherwise, it sets the discourse analysis result to failure.

6. The method according to claim 5, characterized in that, The local analysis module transmits the received speech data to the remote analysis module, including the following steps: S211. The local analysis module sets the count value p to 1, and the local analysis module takes the received speech data as the initial data, divides the initial data into first speech data and second speech data, performs specific operations on the first speech data and second speech data to obtain the p-th intermediate data, and increments the count value p by 1. S212. The local analysis module takes the first speech data as the initial data, and continues to divide the initial data into the first speech data and the second speech data. It performs specific operations on the first speech data and the second speech data to obtain the p-th intermediate data. The local analysis module determines whether the termination condition is met. If not, it increments the count value p by 1 and repeats this step. If yes, it continues to the next step. S213. The local analysis module performs secret processing on the last first speech data to obtain secret data, and the local analysis module sends the secret data, as well as the first intermediate data to the pth intermediate data, to the remote analysis module.

7. The method according to claim 6, characterized in that, The remote analysis module obtains the speech data sent by the local analysis module through the following steps: S311. The local analysis module performs recovery processing on the secret data to obtain the last first speech data, performs specific operations on the last first speech data and the p-th intermediate data to obtain the last second speech data, recovers the previous first speech data from the last first speech data and the last second speech data, and decrements the count value p by 1. S312. The local analysis module performs specific operations on the previous first speech data and the p-th intermediate data to obtain the previous second speech data, recovers the new previous first speech data from the previous first speech data and the previous second speech data, decrements the count value p by 1, and determines whether the count value p is equal to 1. If not, repeat this step; if yes, continue to the next step. S313, The local analysis module performs specific operations on the previous first speech data and the first intermediate data to obtain the previous second speech data, and recovers the initial data from the previous first speech data and the previous second speech data.

8. A training behavior monitoring and analysis device, used to implement the method as described in any one of claims 1 to 7, characterized in that, Includes the following modules: The first monitoring module is set up at the training site to collect and process the speech data of the trainees, and to send the collected speech data to the local analysis module set up at the training site. The second monitoring module is set up at the training site to collect and process the action data of the trainees, and to transmit the collected action data to the remote analysis module. The local analysis module is used to compare and analyze the features of the received speech data with the features of different pre-stored standard speech data to determine the transmission time, and transmit the received speech data to the remote analysis module from the transmission time. It also transmits the first analysis result of the received speech data to the remote analysis module. The remote analysis module is used to compare and analyze the features of the speech data sent by the local analysis module with the features of some pre-stored standard speech data to obtain a second analysis result. Based on the first analysis result and the second analysis result, the speech analysis result is determined. It is also used to receive the action data sent by the second monitoring module, compare and analyze the features of the received action data with the features of different pre-stored standard action data to obtain the action analysis result, and determine the final analysis result based on the speech analysis result and the action analysis result.

9. A device, characterized in that, The system includes a memory and a processor, the memory being used to store a computer program, and the processor being used to implement the method as described in any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that, The storage medium stores program instructions, wherein when the program instructions are executed, they control the device where the storage medium is located to perform the method as described in any one of claims 1 to 7.

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