Data processing method and device for guidance evaluation

Abstract

The invention discloses a data processing method and device for guidance evaluation. The method comprises the steps that to-be-processed evaluation data is acquired, and the to-be-processed evaluation data is used for representing task data needing to be guided and evaluated; performing evaluation model matching processing based on task features on the to-be-processed evaluation data to obtain a task evaluation auxiliary model, the task evaluation auxiliary model being an evaluation auxiliary model corresponding to the task features of the to-be-processed evaluation data; and performing auxiliary evaluation processing based on the task evaluation auxiliary model on the to-be-processed evaluation data to obtain auxiliary evaluation data. By matching the collected data with the evaluation task, matching the corresponding evaluation model, and performing auxiliary evaluation on the collected data according to the evaluation model of the corresponding task, the technical effect of improving the guidance evaluation efficiency and accuracy is achieved.

Description

Technical Field

[0001] The present application relates to the field of troop training data collection and evaluation, and in particular, to a data processing method and device for command and control evaluation. Background Art

[0002] Training programs that meet actual combat needs are crucial to improving the combat capabilities of troops. During the exercise, it is necessary to collect the situation on the exercise site in real time to evaluate the exercise tasks. The collection and evaluation of traditional exercise evaluation data is mainly done manually. Different evaluators have different scoring standards and professional capabilities. The traditional collection of evaluation data is prone to problems such as strong subjectivity, low efficiency, and difficulty in data aggregation and calculation.

[0003] Therefore, this application is proposed to address the problems existing in the guidance and adjustment evaluation in the prior art. Summary of the invention

[0004] The main purpose of this application is to provide a data processing method and device for guidance and evaluation to solve the above-mentioned problems and achieve the technical effect of improving the accuracy and efficiency of exercise evaluation.

[0005] In order to achieve the above-mentioned purpose, in a first aspect of the present application, a data processing method for guidance and coordination evaluation is proposed, which is characterized in that it is applied in a guidance and coordination system to evaluate guidance and coordination tasks, and includes:

[0006] Acquire the evaluation data to be processed, wherein the evaluation data to be processed is task data indicating that a guidance and adjustment evaluation needs to be performed;

[0007] Performing task feature-based evaluation model matching processing on the evaluation data to be processed to obtain a task evaluation auxiliary model, wherein the task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task feature of the evaluation data to be processed;

[0008] The evaluation data to be processed is subjected to auxiliary evaluation processing based on the task evaluation auxiliary model to obtain auxiliary evaluation data.

[0009] Furthermore, the evaluation data to be processed is subjected to evaluation model matching processing based on task features, and the task evaluation auxiliary model obtained includes:

[0010] Preprocessing the evaluation data to be processed to obtain preprocessed evaluation data;

[0011] Performing task feature extraction processing on the pre-processed evaluation data to obtain task feature data to be evaluated;

[0012] According to the task feature data to be evaluated, a preset task evaluation database is traversed to obtain matching task feature data through screening, wherein the matching task feature data is task feature data in the preset task evaluation database that is used to indicate that the task feature data to be evaluated matches the task feature data to be evaluated;

[0013] An evaluation model corresponding to the matching task feature data is matched in an evaluation model database to obtain a task evaluation auxiliary model.

[0014] Further, matching the evaluation model corresponding to the matching task feature data in the evaluation model database to obtain the task evaluation auxiliary model includes:

[0015] Performing identification processing based on data type on the preprocessed evaluation data to obtain first preprocessed evaluation data and second preprocessed evaluation data, wherein the first preprocessed evaluation data is evaluation data used to represent the first data type, and the second preprocessed evaluation data is evaluation data used to represent the second data type;

[0016] The matching task feature data is processed with the first preprocessed evaluation data and the second preprocessed evaluation data to generate evaluation features, respectively, to obtain task first evaluation matching feature data and task second evaluation matching feature data;

[0017] The evaluation models corresponding to the first evaluation matching feature data of the task and the second evaluation matching feature data of the task are matched respectively in the task evaluation model database to obtain the task evaluation auxiliary model, wherein the task evaluation auxiliary model includes a first task evaluation auxiliary model and a second task evaluation auxiliary model.

[0018] Furthermore, the evaluation data to be processed is subjected to auxiliary evaluation processing based on the task evaluation auxiliary model, and the obtained auxiliary evaluation data includes:

[0019] Performing auxiliary evaluation processing based on multiple task evaluation auxiliary models on the evaluation data to be processed to obtain multiple process auxiliary evaluation data, wherein the multiple process auxiliary evaluation data are evaluation data used to represent the auxiliary evaluation of the task evaluation auxiliary models corresponding to the multiple auxiliary evaluation items respectively;

[0020] Performing quantitative model building processing on the plurality of process auxiliary evaluation data and the evaluation data to be processed to obtain evaluation quantitative model data;

[0021] The auxiliary evaluation data is generated according to the evaluation quantitative model data and the plurality of process auxiliary evaluation data.

[0022] Furthermore, the plurality of process auxiliary evaluation data and the evaluation data to be processed are subjected to quantitative model building processing to obtain evaluation quantitative model data including:

[0023] Normalizing the plurality of process auxiliary evaluation data to obtain a plurality of quantitative auxiliary evaluation data, wherein the plurality of quantitative auxiliary evaluation data are auxiliary evaluation data used to represent normalized plurality of process auxiliary evaluation data;

[0024] A quantitative model building process is performed on the plurality of quantitative auxiliary evaluation data and the evaluation data to be processed to obtain the quantitative evaluation model.

[0025] Furthermore, before obtaining the evaluation data to be processed, the method further includes:

[0026] Obtain the guidance and adjustment demand data to be processed;

[0027] Performing identification processing based on the collection features on the to-be-processed guidance and adjustment demand data to obtain first collection feature data and second collection feature data, wherein the first collection feature data is feature data for indicating the guidance and adjustment collection position, and the second collection feature data is feature data for indicating the guidance and adjustment collection type;

[0028] Collection prompt information is generated according to the first collection feature data and the second collection feature data, and the collection prompt information is output to the guidance collection object end.

[0029] According to a second aspect of the present application, a data processing device for guidance and coordination evaluation is proposed, which is applied to a guidance and coordination system to implement the evaluation of guidance and coordination tasks, including:

[0030] A data acquisition module, used to acquire the evaluation data to be processed, wherein the evaluation data to be processed is task data indicating that a guidance and adjustment evaluation needs to be performed;

[0031] A matching module is used to perform task feature-based evaluation model matching processing on the evaluation data to be processed to obtain a task evaluation auxiliary model, wherein the task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task feature of the evaluation data to be processed;

[0032] The auxiliary evaluation module is used to perform auxiliary evaluation processing on the evaluation data to be processed based on the task evaluation auxiliary model to obtain auxiliary evaluation data.

[0033] Furthermore, the matching module includes:

[0034] A preprocessing module, used for preprocessing the evaluation data to be processed to obtain preprocessed evaluation data;

[0035] A task feature extraction module is used to perform task feature extraction processing on the pre-processed evaluation data to obtain task feature data to be evaluated;

[0036] A feature matching module is used to traverse a preset task evaluation database according to the task feature data to be evaluated, and screen to obtain matching task feature data, wherein the matching task feature data is task feature data in the preset task evaluation database that matches the task feature data to be evaluated;

[0037] The evaluation model matching module is used to match the evaluation model corresponding to the matching task feature data in the evaluation model database to obtain a task evaluation auxiliary model.

[0038] According to a third aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned data processing method for guidance and coordination evaluation.

[0039] According to the fourth aspect of the present application, an electronic device is proposed, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the above-mentioned data processing method for guidance evaluation.

[0040] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0041] In the present application, the evaluation data to be processed is obtained, wherein the evaluation data to be processed is task data for indicating the need for guidance and coordination evaluation; the evaluation data to be processed is matched with the evaluation model based on the task characteristics to obtain a task evaluation auxiliary model, wherein the task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task characteristics of the evaluation data to be processed; the evaluation data to be processed is subjected to auxiliary evaluation processing based on the task evaluation auxiliary model to obtain auxiliary evaluation data. By matching the collected data with the evaluation task and the corresponding evaluation model, and performing auxiliary evaluation on the collected data according to the evaluation model of the corresponding task, the technical effect of improving the efficiency and accuracy of guidance and coordination evaluation is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0043] Figure 1 A flowchart of a data processing method for guidance and evaluation provided in this application;

[0044] Figure 2 A flowchart of a data processing method for guidance and evaluation provided in this application;

[0045] Figure 3 A flowchart of a data processing method for guidance and evaluation provided in this application;

[0046] Figure 4 A schematic diagram of a data processing device for guidance and evaluation provided by the present application;

[0047] Figure 5 A schematic diagram of another data processing device for guidance and evaluation provided in the present application. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0049] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0050] In the present application, the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings. These terms are mainly used to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation.

[0051] In addition, some of the above terms may be used to express other meanings in addition to indicating orientation or positional relationship. For example, the term "on" may also be used to express a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0052] In addition, the terms "installed", "set", "provided with", "connected", "connected", and "socketed" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be an internal connection between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0053] Wireless network communication technology and Internet technology have developed rapidly, promoting the continuous enhancement and intelligence of mobile communication equipment. The characteristics of mobile business applications such as access anytime and anywhere are increasingly valued. With the widespread application of mobile Internet, the demand for on-site accompanying applications through mobile portable terminals in practical applications is also increasing. During the exercise, the guidance and evaluation personnel are connected to the guidance and evaluation system network through portable terminals. During the exercise evaluation process, the training personnel collect data according to the task requirements. This application proposes a data processing method and device for guidance and evaluation, so as to realize the evaluation of the data collected by the guidance and evaluation, reduce the manual intervention in the guidance and evaluation data collection and evaluation process, and improve the accuracy and efficiency of the guidance and evaluation.

[0054] In some optional embodiments of the present application, a data processing method for guidance and adjustment evaluation is proposed, which is applied to a guidance and adjustment system to implement the evaluation of guidance and adjustment tasks. Figure 1 A flowchart of a data processing method for guidance and evaluation provided in this application, such as Figure 1 As shown, the method comprises the following steps:

[0055] S101: Obtaining evaluation data to be processed;

[0056] The evaluation data to be processed is used to indicate task data that requires guidance and coordination evaluation. The evaluation data to be processed is the exercise site data collected by the guidance and coordination personnel terminal, including image data, video data, audio data and other types of evaluation data to be processed.

[0057] S102: performing task feature-based evaluation model matching processing on the evaluation data to be processed to obtain a task evaluation auxiliary model;

[0058] The task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task characteristics of the evaluation data to be processed.

[0059] In some optional embodiments of the present application, a data processing method for guidance evaluation is proposed. Figure 2 A flowchart of a data processing method for guidance and evaluation provided in this application, such as Figure 2 As shown, the method comprises the following steps:

[0060] S201: preprocessing the evaluation data to be processed to obtain preprocessed evaluation data;

[0061] De-noising the evaluation data to be processed, including:

[0062] The signal noise model is constructed for the evaluation data to be processed.

[0063] y(t)=x(t)+n(t)

[0064] Among them, y(t) is the observed signal, x(t) is the original signal, and n(t) is the noise, which is usually assumed to be white noise with zero mean and Gaussian distribution characteristics;

[0065] The evaluation data to be processed is processed in the wavelet domain.

[0066]

[0067] Among them, w j,k is the wavelet coefficient, j represents the scale, k represents the position, is the wavelet basis function; threshold processing is performed on the evaluation data to be processed.

[0068]

[0069] w j,k is the denoised wavelet coefficient, λ is the threshold used to suppress noise;

[0070] The denoised wavelet coefficients are reconstructed into the denoised signal through inverse wavelet transform

[0071]

[0072] S202: performing task feature extraction processing on the pre-processed evaluation data to obtain feature data of the task to be evaluated;

[0073] The pre-processed evaluation data is processed for scene text recognition to obtain scene text feature data, and the scene text feature data is processed for task feature extraction based on a natural language processing (NLP) model to obtain task feature data to be evaluated.

[0074] S203: traversing a preset task evaluation database according to the task feature data to be evaluated, and screening to obtain matching task feature data;

[0075] The matching task feature data is used to represent the task feature data in the preset task evaluation database that matches the task feature data to be evaluated. The task feature data to be evaluated and the task evaluation features in the preset task evaluation database are subjected to similarity calculation processing, and the task evaluation features whose similarity with the task feature data to be evaluated meets the preset range are screened. The task evaluation features are the matching task feature data.

[0076] In an optional embodiment of the present application, the similarity between the task feature to be evaluated and the task evaluation feature is calculated by cosine similarity. The cosine similarity S can be expressed as:

[0077]

[0078] Where a is the feature vector of the task feature to be evaluated, b is the vector of the task evaluation feature, where a·b represents the dot product of vectors a and b, ‖a‖ and ‖b‖ represent the modulus lengths of vectors a and b respectively. The value range of cosine similarity is [-1,1]. The closer the value is to 1, the higher the similarity. The task feature to be evaluated is matched with the task evaluation feature in the preset task evaluation database, so that the corresponding evaluation model is called according to the matched task feature data, and the pre-processed evaluation data corresponding to the task feature data to be evaluated is assisted in evaluation processing according to the evaluation model.

[0079] In another optional embodiment of the present application, there may be multiple task features to be evaluated in the pre-processed evaluation data, and the above-mentioned task feature matching processing is performed on each of the multiple task features to be evaluated. The first task feature to be evaluated is matched with the first matching task feature, and the first task evaluation model is called according to the first matching task feature. When the first task evaluation model is used, an auxiliary evaluation is performed on the evaluation data to be processed corresponding to the first task feature to be evaluated to obtain a first auxiliary evaluation result, thereby completing the evaluation of the first item. The second task feature to be evaluated is matched with the second matching task feature, and the second task evaluation model is called according to the second matching task feature. When the second task evaluation model is used, an auxiliary evaluation is performed on the evaluation data to be processed corresponding to the second task feature to be evaluated to obtain a second auxiliary evaluation result, thereby completing the evaluation of the second item.

[0080] S204: Matching the evaluation model corresponding to the matching task feature data in the evaluation model database to obtain a task evaluation auxiliary model.

[0081] In some optional embodiments of the present application, a data processing method for guidance and evaluation is proposed, comprising:

[0082] The preprocessed evaluation data is processed based on data type identification to obtain first preprocessed evaluation data and second preprocessed evaluation data, wherein the first preprocessed evaluation data is used to represent evaluation data corresponding to the first data type, and the second preprocessed evaluation data is used to represent evaluation data corresponding to the second data type. When evaluating any task, the collected evaluation data to be processed are of different types, such as voice, video, picture, etc. Different types of data are evaluated and processed by different evaluation models. The evaluation model corresponding to the evaluation data to be processed is determined according to the task characteristics and data type characteristics, so as to realize the evaluation processing of the type data corresponding to the evaluation task in the data to be processed to obtain the evaluation result; the matching task feature data is processed with the first preprocessed evaluation data and the second preprocessed evaluation data for evaluation feature generation, respectively, to obtain the first evaluation matching feature data of the task and the second evaluation matching feature data of the task, respectively; the evaluation models corresponding to the first evaluation matching feature data of the task and the second evaluation matching feature data of the task are matched in the task evaluation model database to obtain the task evaluation auxiliary model, wherein the task evaluation auxiliary model includes the first task evaluation auxiliary model and the second task evaluation auxiliary model.

[0083] S103: Perform auxiliary evaluation processing on the evaluation data to be processed based on the task evaluation auxiliary model to obtain auxiliary evaluation data.

[0084] In some optional embodiments of the present application, a data processing method for guidance evaluation is proposed. Figure 3 A flowchart of a data processing method for guidance and evaluation provided in this application, such as Figure 3 As shown, the method comprises the following steps:

[0085] S301: performing auxiliary evaluation processing based on multiple task evaluation auxiliary models on the evaluation data to be processed to obtain multiple process auxiliary evaluation data;

[0086] The plurality of process auxiliary evaluation data are evaluation data used to represent the auxiliary evaluation of the task evaluation auxiliary model corresponding to the plurality of auxiliary evaluation items respectively;

[0087] S302: Performing quantitative model construction processing on multiple process auxiliary evaluation data and evaluation data to be processed to obtain evaluation quantitative model data;

[0088] In some optional embodiments of the present application, a data processing method for guidance and evaluation is proposed, comprising:

[0089] A plurality of process auxiliary evaluation data are normalized to obtain a plurality of quantitative auxiliary evaluation data, wherein the plurality of quantitative auxiliary evaluation data are auxiliary evaluation data used to represent the normalized plurality of process auxiliary evaluation data; a quantitative model is constructed for the plurality of quantitative auxiliary evaluation data and the evaluation data to be processed to obtain a quantitative evaluation model.

[0090] The process-assisted evaluation data is normalized to quantify the evaluation data of different evaluation items. It can be standardized by Z-score, x = (xu) / σ, where x is the original data, u is the mean, and σ is the standard deviation.

[0091] In an optional embodiment of the present application, y is the evaluation result, x=(x 1, x 2, ,…,x n ), the feature vector of the evaluation data, the quantitative model can be expressed as: y = β0 + β1x1 + β2x2 + ... + β n x n +∈, where, β0, β1,…,β n is the model parameter, ∈ is the error term, solve the model parameter, Where m is the number of samples, y i is the evaluation result of the i-th sample, x ij is the jth feature of the i-th sample, and an evaluation quantitative model is constructed.

[0092] S303: Generate auxiliary evaluation data according to the evaluation quantification model data and a plurality of process auxiliary evaluation data.

[0093] After obtaining the auxiliary evaluation data, the auxiliary evaluation data and the collected evaluation data to be processed are packaged, and the packaged data are uploaded to the guidance and control system.

[0094] In some optional embodiments of the present application, a data processing method for guidance and coordination evaluation is proposed. Before obtaining the evaluation data to be processed, the method further includes:

[0095] Acquire the guidance and dispatching demand data to be processed; perform identification processing on the guidance and dispatching demand data based on the collection features to obtain the first collection feature data and the second collection feature data, wherein the first collection feature data is the feature data used to indicate the guidance and dispatching collection position, and the second collection feature data is the feature data used to indicate the guidance and dispatching collection type; generate collection prompt information according to the first collection feature data and the second collection feature data, and output the collection prompt information to the guidance and dispatching collection object end.

[0096] In another optional embodiment of the present application, the data acquisition software at the conditioning object end receives the data acquisition task information issued by the exercise data acquisition subsystem, wherein through the flow control and data transmission mechanism based on the CAN (Controller Area Network) network, when the DCM (Diagnostic Communication Manager) module receives new data, it feeds back the available buffer size and sends a flow control frame to the sender, including:

[0097] First frame reception: After receiving the first frame data, the DCM module feeds back the available buffer size, for example, 25 bytes;

[0098] Flow control status: The CAN network transport layer module CanTp sends a flow control status frame (continue sending, wait) to the sender according to the remaining buffer size;

[0099] Data reception and monitoring: CanTp provides the data of each received frame to the upper-layer application and monitors the remaining buffer size.

[0100] In some optional embodiments of the present application, a data processing device for guidance and adjustment evaluation is proposed, which is applied to a guidance and adjustment system to implement the evaluation of guidance and adjustment tasks. Figure 4 A schematic diagram of a data processing device for guidance and evaluation provided in this application, such as Figure 4 As shown, including:

[0101] The data acquisition module 41 is used to acquire the evaluation data to be processed, wherein the evaluation data to be processed is task data indicating that a guidance and adjustment evaluation needs to be performed;

[0102] A matching module 42 is used to perform task feature-based evaluation model matching processing on the evaluation data to be processed to obtain a task evaluation auxiliary model, wherein the task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task feature of the evaluation data to be processed;

[0103] The auxiliary evaluation module 43 is used to perform auxiliary evaluation processing on the evaluation data to be processed based on the task evaluation auxiliary model to obtain auxiliary evaluation data.

[0104] In some optional embodiments of the present application, a data processing device for guidance and adjustment evaluation is proposed, which is applied to a guidance and adjustment system to implement the evaluation of guidance and adjustment tasks. Figure 5 A schematic diagram of a data processing device for guidance and evaluation provided in this application, such as Figure 5 As shown, including:

[0105] A preprocessing module 51 is used to preprocess the evaluation data to be processed to obtain preprocessed evaluation data;

[0106] A task feature extraction module 52 is used to perform task feature extraction processing on the pre-processed evaluation data to obtain task feature data to be evaluated;

[0107] A feature matching module 53 is used to traverse a preset task evaluation database according to the task feature data to be evaluated, and screen to obtain matching task feature data, wherein the matching task feature data is task feature data in the preset task evaluation database that matches the task feature data to be evaluated;

[0108] The evaluation model matching module 54 is used to match the evaluation model corresponding to the matching task feature data in the evaluation model database to obtain a task evaluation auxiliary model.

[0109] The specific manner of executing the operation of each unit in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0110] In summary, in the present application, the evaluation data to be processed is obtained, wherein the evaluation data to be processed is task data used to indicate the need for guidance and coordination evaluation; the evaluation data to be processed is matched with the evaluation model based on the task characteristics to obtain a task evaluation auxiliary model, wherein the task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task characteristics of the evaluation data to be processed; the evaluation data to be processed is subjected to auxiliary evaluation processing based on the task evaluation auxiliary model to obtain auxiliary evaluation data. By matching the collected data with the evaluation task and the corresponding evaluation model, and performing auxiliary evaluation on the collected data according to the evaluation model of the corresponding task, the technical effect of improving the efficiency and accuracy of guidance and coordination evaluation is achieved.

[0111] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0112] Obviously, those skilled in the art should understand that the above-mentioned units or steps of the present application can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0113] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method for guidance and evaluation, characterized in that: Applied to the guidance system to evaluate the guidance tasks, including: Acquire the evaluation data to be processed, wherein the evaluation data to be processed is task data indicating that a guidance and adjustment evaluation needs to be performed; Performing task-feature-based evaluation model matching processing on the evaluation data to be processed to obtain a task evaluation auxiliary model, wherein the task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task features of the evaluation data to be processed; The evaluation data to be processed is subjected to auxiliary evaluation processing based on the task evaluation auxiliary model to obtain auxiliary evaluation data.

2. The data processing method according to claim 1, characterized in that: The evaluation data to be processed is subjected to evaluation model matching processing based on task features to obtain a task evaluation auxiliary model including: Preprocessing the evaluation data to be processed to obtain preprocessed evaluation data; Performing task feature extraction processing on the pre-processed evaluation data to obtain task feature data to be evaluated; According to the task feature data to be evaluated, a preset task evaluation database is traversed to obtain matching task feature data through screening, wherein the matching task feature data is task feature data in the preset task evaluation database that is used to indicate that the task feature data to be evaluated matches the task feature data to be evaluated; An evaluation model corresponding to the matching task feature data is matched in an evaluation model database to obtain a task evaluation auxiliary model.

3. The data processing method according to claim 2, characterized in that: Matching the evaluation model corresponding to the matching task feature data in the evaluation model database to obtain the task evaluation auxiliary model includes: Performing identification processing based on data type on the preprocessed evaluation data to obtain first preprocessed evaluation data and second preprocessed evaluation data, wherein the first preprocessed evaluation data is evaluation data used to represent the first data type, and the second preprocessed evaluation data is evaluation data used to represent the second data type; The matching task feature data is processed with the first preprocessed evaluation data and the second preprocessed evaluation data to generate evaluation features, respectively, to obtain task first evaluation matching feature data and task second evaluation matching feature data; The evaluation models corresponding to the first evaluation matching feature data of the task and the second evaluation matching feature data of the task are matched respectively in the task evaluation model database to obtain the task evaluation auxiliary model, wherein the task evaluation auxiliary model includes a first task evaluation auxiliary model and a second task evaluation auxiliary model.

4. The data processing method according to claim 1, characterized in that: The to-be-processed evaluation data is subjected to auxiliary evaluation processing based on the task evaluation auxiliary model, and the obtained auxiliary evaluation data includes: Performing auxiliary evaluation processing based on multiple task evaluation auxiliary models on the evaluation data to be processed to obtain multiple process auxiliary evaluation data, wherein the multiple process auxiliary evaluation data are evaluation data used to represent the auxiliary evaluation of the task evaluation auxiliary models corresponding to the multiple auxiliary evaluation items respectively; Performing quantitative model building processing on the plurality of process auxiliary evaluation data and the evaluation data to be processed to obtain evaluation quantitative model data; The auxiliary evaluation data is generated according to the evaluation quantitative model data and the plurality of process auxiliary evaluation data.

5. The data processing method according to claim 1, characterized in that: The quantitative model building process is performed on the plurality of process auxiliary evaluation data and the evaluation data to be processed to obtain evaluation quantitative model data including: Normalizing the plurality of process auxiliary evaluation data to obtain a plurality of quantitative auxiliary evaluation data, wherein the plurality of quantitative auxiliary evaluation data are auxiliary evaluation data used to represent normalized plurality of process auxiliary evaluation data; A quantitative model building process is performed on the plurality of quantitative auxiliary evaluation data and the evaluation data to be processed to obtain the quantitative evaluation model.

6. The data processing method according to claim 1, characterized in that: Before obtaining the evaluation data to be processed, the method further includes: Obtain the guidance and adjustment demand data to be processed; Performing identification processing based on the collection features on the to-be-processed guidance and adjustment demand data to obtain first collection feature data and second collection feature data, wherein the first collection feature data is feature data for indicating the guidance and adjustment collection position, and the second collection feature data is feature data for indicating the guidance and adjustment collection type; Collection prompt information is generated according to the first collection feature data and the second collection feature data, and the collection prompt information is output to the guidance collection object end.

7. A data processing device for guidance and evaluation, characterized in that: Applied to the guidance system to evaluate the guidance tasks, including: A data acquisition module, used to acquire the evaluation data to be processed, wherein the evaluation data to be processed is task data indicating that a guidance and adjustment evaluation needs to be performed; A matching module is used to perform task feature-based evaluation model matching processing on the evaluation data to be processed to obtain a task evaluation auxiliary model, wherein the task evaluation auxiliary model is an evaluation auxiliary model corresponding to the task feature of the evaluation data to be processed; The auxiliary evaluation module is used to perform auxiliary evaluation processing on the evaluation data to be processed based on the task evaluation auxiliary model to obtain auxiliary evaluation data.

8. The data processing device according to claim 7, characterized in that: Matching modules, including: A preprocessing module, used for preprocessing the evaluation data to be processed to obtain preprocessed evaluation data; A task feature extraction module is used to perform task feature extraction processing on the pre-processed evaluation data to obtain task feature data to be evaluated; A feature matching module is used to traverse a preset task evaluation database according to the task feature data to be evaluated, and screen to obtain matching task feature data, wherein the matching task feature data is task feature data in the preset task evaluation database that matches the task feature data to be evaluated; The evaluation model matching module is used to match the evaluation model corresponding to the matching task feature data in the evaluation model database to obtain a task evaluation auxiliary model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the data processing method for guidance and coordination evaluation described in any one of claims 1-6.

10. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the data processing method for guidance and coordination evaluation as described in any one of claims 1-6.

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