Data return method, device and terminal equipment

Abstract

The present application provides a data return method, device and terminal equipment, which are applicable to the field of data processing technology. The method includes: classifying and processing the equipment working condition data to be returned to obtain the category information of the equipment working condition data to be returned; identifying the equipment status data to be returned to obtain the identification information of the equipment status data to be returned; generating multiple initial data sets to be returned based on the return time information, the category information of the equipment working condition data to be returned and the identification information of the equipment status data to be returned; screening the multiple initial data sets to be returned to obtain the target data set to be returned; extracting and processing the target data set to be returned to obtain the target data to be returned, so as to return the target data to be returned. The present application is used to filter invalid data in the equipment working condition data and equipment status data, improve the efficiency and accuracy of data return, and thus meet the stringent requirements of the Industrial Internet of Things for data management throughout the entire life cycle of equipment.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular relates to a data return method, apparatus, and terminal device. Background Art

[0002] In the Industrial Internet of Things (IIoT), as devices become increasingly intelligent and interconnected, the efficient management and transmission of massive amounts of operational data has become a key challenge. Currently, the amount of time, operating condition, and status data generated by industrial equipment in real time is exploding. This data, covering the entire lifecycle of equipment operation, is crucial for equipment fault diagnosis, performance optimization, and predictive maintenance.

[0003] Existing technologies usually identify industrial data based on fixed thresholds to determine abnormal operation or operational failure of industrial equipment, and then transmit the abnormal information or failure condition back.

[0004] However, the existing technology has problems with insufficient data classification accuracy and poor data recognition accuracy when returning data, which reduces the effectiveness and quality of the returned data. Summary of the Invention

[0005] In view of this, the embodiments of the present application provide a data return method, apparatus and terminal device, aiming to solve the problem of insufficient classification accuracy of return data and reduced effectiveness and efficiency of return data in the prior art.

[0006] A first aspect of an embodiment of the present application provides a data return method, including:

[0007] Acquire multiple return time information, multiple device working condition data to be returned, and multiple device status data to be returned; wherein the return time information, the device working condition data to be returned, and the device status data to be returned correspond to each other;

[0008] Classify the plurality of equipment operating condition data to be transmitted back according to a preset equipment operating condition data classification vector to obtain category information of the plurality of equipment operating condition data to be transmitted back;

[0009] Identify and process the plurality of device status data to be transmitted back according to a plurality of preset device status data identification vectors to obtain a plurality of device status data identification information to be transmitted back;

[0010] Generate multiple initial data sets to be returned based on the multiple return time information, the multiple types of device operating condition data to be returned, and the multiple identification information of device status data to be returned;

[0011] Screening the multiple initial data sets to be returned to obtain a target data set to be returned;

[0012] The target data set to be returned is subjected to data extraction processing to obtain target data to be returned, so as to return the target data to be returned.

[0013] A second aspect of an embodiment of the present application provides a data backhaul device, including:

[0014] A module for acquiring data to be returned, configured to acquire a plurality of return time information, a plurality of device operating condition data to be returned, and a plurality of device status data to be returned; wherein the return time information, the device operating condition data to be returned, and the device status data to be returned correspond to each other in a one-to-one manner;

[0015] a module for generating category information of equipment operating condition data to be transmitted back, configured to classify the plurality of equipment operating condition data to be transmitted back according to a preset equipment operating condition data classification vector, and obtain a plurality of category information of equipment operating condition data to be transmitted back;

[0016] a module for generating identification information of device status data to be transmitted back, configured to perform identification processing on the plurality of device status data to be transmitted back according to a plurality of preset device status data identification vectors, and obtain a plurality of identification information of device status data to be transmitted back;

[0017] An initial data set to be returned generating module, configured to generate a plurality of initial data sets to be returned based on the plurality of return time information, the plurality of device operating condition data category information to be returned, and the plurality of device status data identification information to be returned;

[0018] a target data set to be returned determining module, configured to filter the plurality of initial data sets to be returned to obtain a target data set to be returned;

[0019] The target data to be returned is generated by the module, which is used to extract the data to be returned from the target data set to be returned, obtain the target data to be returned, and return the target data to be returned.

[0020] A third aspect of an embodiment of the present application provides a terminal device, which includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the data return method described in the first aspect above.

[0021] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, comprising: storing a computer program, which, when executed by a processor, implements the steps of the data return method described in the first aspect above.

[0022] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows: the present application accurately locates high-value industrial data for feedback through corresponding processing and classification identification of multi-dimensional heterogeneous industrial data, effectively filters invalid data, reduces invalid transmission, improves the efficiency and accuracy of data feedback, enhances the standardization and operability of data processing, and is conducive to efficient traceability and analysis of equipment operation conditions, thereby meeting the stringent requirements of the Industrial Internet of Things for data management throughout the entire life cycle of equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 This is a schematic diagram of the implementation process of the data return method provided in Example 1 of the present application;

[0025] Figure 2 This is a schematic diagram of the implementation process of the data return method provided in Example 2 of the present application;

[0026] Figure 3 This is a schematic diagram of the implementation process of the data return method provided in Example 3 of the present application;

[0027] Figure 4 This is a schematic diagram of the implementation flow of the data return method provided in Example 4 of the present application;

[0028] Figure 5 This is a schematic diagram of the implementation process of the data return method provided in Example 5 of the present application;

[0029] Figure 6 This is a schematic diagram of the implementation flow of the data return method provided in Example 6 of the present application;

[0030] Figure 7 This is a schematic diagram of the implementation flow of the data return method provided in Example 7 of the present application;

[0031] Figure 8 This is a schematic diagram of the structure of the data return device provided in an embodiment of the present application;

[0032] Figure 9 It is a schematic diagram of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0034] In order to illustrate the technical solution described in this application, specific embodiments are provided below.

[0035] Figure 1 The following is a flowchart of the implementation of the data return method provided in Example 1 of the present application, which is detailed as follows:

[0036] Step S101 , obtaining a plurality of return time information, a plurality of device working condition data to be returned, and a plurality of device status data to be returned; the return time information, the device working condition data to be returned, and the device status data to be returned are in one-to-one correspondence.

[0037] In this embodiment, the transmitted time information can refer to a time stamp accurate to the millisecond level, marking the moment of data collection. It can include year, month, day, hour, minute, and millisecond. It can also be a unique anchor identifier for the data's time dimension. It can be a high-precision clock module deployed on the edge node, such as a GPS clock, atomic clock, or network time protocol, synchronized with a global time server. This information is obtained in real time at the moment of data collection via a system clock interface, such as the Linux clock_gettime() function, to ensure millisecond-level timestamp accuracy. The device operating condition data to be transmitted can refer to parameters reflecting the device's operating status and operating condition classification results. This can include device operating parameters such as temperature, pressure, and speed; production process stages such as startup, operation, and shutdown; and operating condition classification labels such as normal, warning, and fault. The device operating condition data to be transmitted can be collected in real time by industrial sensors such as temperature sensors and pressure transmitters. The device status data to be transmitted can refer to the real-time status parameters and comprehensive health assessment results of each device component. It can include sensor status data and actuator status data, and can be obtained through sensors or device diagnostic interfaces, such as sensor readings and communication status codes. Understandably, each piece of equipment condition data and status data to be transmitted back is bound to the same transmission time information, forming a three-dimensional meta-tag system of "time-condition-status" to support subsequent historical data backtracking and correlation analysis with millisecond-level accuracy. Understandably, the equipment condition data to be transmitted back focuses on the dynamic operating conditions of the equipment during operation, covering operating parameters such as temperature and pressure, as well as the stages of the production process, used to reflect "how the equipment operates" and the working scenarios it is in; the equipment status data to be transmitted back focuses on the health status of each component of the equipment, including the real-time status parameters and comprehensive status index of components such as sensors and actuators.

[0038] Step S102 : classifying the plurality of equipment operating condition data to be transmitted back according to a preset equipment operating condition data classification vector to obtain category information of the plurality of equipment operating condition data to be transmitted back.

[0039] In this embodiment, the preset equipment operating condition data classification vector can be manually set or generated by summarizing features of operating condition categories such as normal, warning, and fault based on a historical operating condition feature library. The equipment operating condition data to be transmitted back can be normalized to eliminate dimensional differences between different parameters. The preset equipment operating condition data classification vector is then called and the normalized equipment operating condition data to be transmitted back is matched and compared with the features of each category in the equipment operating condition data classification vector. The Euclidean distance between the normalized equipment operating condition data to be transmitted back and the features of each category in the equipment operating condition data classification vector is calculated to determine which operating condition feature vector has the highest degree of match with the current equipment operating condition data to be transmitted back. The equipment operating condition data to be transmitted back is then classified into the corresponding category, generating multiple pieces of equipment operating condition data category information to be transmitted back, such as labels for normal operating condition, warning operating condition, or fault operating condition, thereby completing the classification of the operating condition data.

[0040] Step S103 : performing identification processing on the plurality of device status data to be transmitted back according to a plurality of preset device status data identification vectors to obtain a plurality of identification information of the device status data to be transmitted back.

[0041] In this embodiment, the multiple preset device status data identification vectors can be manually set, constructed based on the normal operating thresholds and fault feature libraries of device components, and can include identification rules and health assessment criteria for different component states. The device status data to be transmitted back can be preprocessed, such as by denoising and normalization, to ensure data accuracy and consistency. The multiple preset device status data identification vectors are then invoked and matched one by one with the multiple preset device status data identification vectors. Based on the parameter ranges and characteristic patterns defined in the identification vectors, a determination is made as to whether the current status data meets the characteristics of a normal operating state, an abnormality warning, or a fault state. For example, a sensor reading threshold in the identification vector can be used to determine whether a sensor has failed, and an actuator state parameter range can be used to determine whether it is stuck. Finally, the identification results of each component are combined to generate multiple pieces of identification information for the device status data to be transmitted back. This information can include status identifiers for each component, such as normal / abnormal / faulty, and an overall comprehensive status index for the device, thereby completing the identification processing of the device status data.

[0042] Step S104 : generating a plurality of initial data sets to be transmitted back according to the plurality of transmission time information, the plurality of device operating condition data category information to be transmitted back, and the plurality of device status data identification information to be transmitted back.

[0043] In this embodiment, the acquired return time information, the category information of the equipment condition data to be returned, and the identification information of the equipment status data to be returned can be first associated one-to-one to ensure that each return time information corresponds to unique category information of the equipment condition data to be returned and identification information of the equipment status data to be returned. Then, with the return time information as the time axis benchmark, the three types of information corresponding to the same time point are integrated to form a complete data record, which contains a timestamp, a condition category label, and an equipment status identification result. These integrated data records are then grouped according to time sequence or condition category, for example, by time window, such as dividing data segments every minute or every hour, or grouping data records with the same condition category label. Then, the data record set in each group can be used as an initial data set to be returned, thereby generating multiple initial data sets to be returned based on time or condition classification, providing structured data units for subsequent data screening and processing.

[0044] Step S105 , screening the multiple initial data sets to be returned to obtain a target data set to be returned.

[0045] In this embodiment, the data records in the initial set of data to be returned are first extracted from the return time information, the category information of the equipment operating condition data to be returned, and the identification information of the equipment status data to be returned. Then, according to the preset filtering rules, for example, the data set with the category information of the equipment operating condition data to be returned indicating fault or the data set with the identification information of the equipment status data to be returned indicating component fault is preferentially retained. At the same time, the timeliness of the return time information is taken into consideration, such as the data within the past hour is set as the filtering priority. Then, the importance of the data records in the initial set is evaluated. For example, the priority of fault operating condition data is higher than that of normal operating condition data, and data with a low equipment status comprehensive index is preferentially retained. Then, a dynamic threshold filtering method is used to calculate the fitness value of the data in the current set by combining the three-dimensional characteristics of time, operating condition, and status. The fitness threshold is set to exclude low-value data. Then, considering the cache capacity of the edge node and the network transmission efficiency, the qualified data sets are sorted by priority, and the sets with high priority and data volume within the transmission capacity range are intercepted. Finally, the filtered high-value data sets are integrated to form the target data set to be returned, ensuring that the returned data contains key operating condition and status information and reducing invalid transmission.

[0046] In this embodiment, optionally, the return time information of each data in the initial data set to be returned, the category information of the equipment working condition data to be returned, and the identification information of the equipment status data to be returned can be extracted first. For the return time information, the time interval between it and the current moment is calculated. The shorter the time interval, the higher the corresponding time correlation weight. For example, an exponential decay function is used to convert the time interval into a weight coefficient so that near-real-time data obtains a higher weight. For the category information of the equipment working condition data to be returned, the weight of the fault working condition is set to be higher than that of the warning working condition and the normal working condition. The weight of the fault working condition can be manually set to 0.5, the warning working condition can be manually set to 0.3, and the normal working condition can be manually set to 0.2, so as to reflect the importance difference of different working conditions and the feedback It is understandable that for the identification information of the device status data to be transmitted back, if it is identified as an abnormal device status, the weight is normal. Different weights can be assigned according to the severity of the abnormal status. For example, the weight of serious faults is manually set to 0.4, the weight of general abnormalities is manually set to 0.2, and the weight of normal status is manually set to 0.1. Then, the weight coefficients of these three types of features are weighted and summed. Among them, the time information weight, working condition category weight and equipment status identification weight can be manually set to 0.3, 0.5 and 0.2 respectively. Finally, the fitness value of each data is obtained, which is used to comprehensively reflect the time urgency, working condition urgency and degree of abnormal status of the data, and provide a quantitative basis for subsequent dynamic threshold filtering.

[0047] Step S106 , performing data extraction processing on the target data set to be returned to obtain target data to be returned, so as to return the target data to be returned.

[0048] In this embodiment, all data records in the target data set to be returned can be traversed first. Each record includes return time information, category information of the device operating condition data to be returned, and identification information of the device status data to be returned. Then, extraction rules are manually set according to the needs of the industrial scenario. For example, data records with the category information of the device operating condition data to be returned indicating fault or data records with the identification information of the device status data to be returned indicating component fault are preferentially extracted. At the same time, the order of return time information is considered to ensure that near-real-time data is preferentially extracted. Then, the data records are checked for integrity to check whether the three-dimensional feature information is missing, and records with incomplete information are eliminated to ensure the availability of the returned data. Then, complete data records that meet the extraction rules are packaged according to a preset data format. For example, fields such as the return time information, the operating condition category label, and the device status identification result are combined into a data packet in a fixed format. Finally, the packaged data packet is compressed to reduce the data transmission volume. At the same time, a check code is added to ensure the accuracy of the data during the return process. Finally, the target data to be returned is obtained and transmitted to the cloud or a higher-level system via the network for storage and analysis.

[0049] The data feedback method provided in the embodiment of the present application, through corresponding processing and classification identification of multi-dimensional heterogeneous industrial data, accurately locates high-value industrial data for feedback, effectively filters invalid data, reduces invalid transmission, improves the efficiency and accuracy of data feedback, enhances the standardization and operability of data processing, and is conducive to the efficient traceability and analysis of equipment operation status, thereby meeting the stringent requirements of the Industrial Internet of Things for data management throughout the entire life cycle of equipment.

[0050] Figure 2 The flowchart of the data return method provided in the second embodiment of the present application is shown. The difference between the second embodiment and the first embodiment is that the step S102 specifically includes:

[0051] Step S201 : converting the format of the plurality of device operating condition data to be transmitted back to obtain a plurality of device operating condition data vectors to be transmitted back.

[0052] In this embodiment, the equipment operating condition data to be transmitted back includes equipment operating parameters, such as temperature, pressure, and speed, as well as information about production process stages. Format conversion is required to unify these different types of raw data into structured vectors. For example, numerical parameters like temperature and pressure are directly used as vector elements, while data records of production process stages, such as startup, operation, and shutdown, are converted into binary vector elements through one-hot encoding. This ultimately forms a dimensional unified vector of the equipment operating condition data to be transmitted back, facilitating subsequent computational processing.

[0053] Step S202 : obtaining a plurality of features of the equipment operating condition data to be transmitted back according to the plurality of equipment operating condition data vectors to be transmitted back and a preset equipment operating condition data classification vector.

[0054] In this embodiment, the preset equipment condition data classification vector can be manually set or constructed based on a historical condition feature library, including standard feature vectors for normal, warning, and fault conditions. The equipment condition data vector to be transmitted back can be matched with the preset equipment condition data classification vectors, and matching features between the current data vector and each category of equipment condition data classification vector can be extracted by calculating Euclidean distance, cosine similarity, or other methods. For example, if the cosine similarity between a certain equipment condition data vector to be transmitted back and the fault condition classification vector is 0.85, and the similarity with the normal condition classification vector is 0.3, this feature can be characterized as a "high fault matching degree."

[0055] In this embodiment, multiple device operating condition data vectors to be transmitted back may be multiplied or convolved with a preset device operating condition data classification vector, and the results of the multiplication or convolution calculation may be used as multiple device operating condition data features to be transmitted back.

[0056] Step S203 , calculating the correlation between the multiple features of the equipment operating condition data to be transmitted back, and obtaining the correlation between the multiple features of the equipment operating condition data to be transmitted back.

[0057] In this embodiment, correlation calculation can be used to measure the correlation between different features in the same operating condition data, avoiding feature redundancy. Methods such as the Pearson correlation coefficient and mutual information can be used to calculate the correlation between various features, such as the abnormal correlation between temperature and pressure, or the correlation between production process stages and parameter fluctuations. For example, in the operating condition data features of a certain piece of equipment to be transmitted, the Pearson correlation coefficient between temperature anomalies and pressure anomalies is 0.92, indicating a strong correlation between the two and requiring comprehensive consideration during classification.

[0058] Step S204 : obtaining category information of the multiple equipment operating condition data to be transmitted back according to the multiple features of the equipment operating condition data to be transmitted back, the multiple feature correlations of the equipment operating condition data to be transmitted back, and a preset equipment operating condition data feature correlation threshold.

[0059] In this embodiment, the preset device operating condition data feature correlation threshold can be manually set and can be used to determine the validity of the feature combination. If the correlation between the device operating condition data feature to be transmitted and the device operating condition data feature to be transmitted of a certain classification vector is higher than the device operating condition data feature correlation threshold, and the correlation between the features conforms to the typical pattern of the category, then the data is classified into the corresponding category, and the category information of the device operating condition data to be transmitted is obtained.

[0060] The data feedback method provided in the embodiment of the present application converts the operating condition data of the equipment to be returned into a vector of a unified format, eliminates the interference caused by differences in data types, and then accurately extracts features from the vectors after format conversion, effectively distinguishes similar operating conditions, improves the robustness of operating condition classification, provides more detailed decision-making basis for subsequent data screening and feedback, and further optimizes the accuracy and effectiveness of industrial data feedback.

[0061] Figure 3 The flowchart of the data return method provided in the third embodiment of the present application is shown. The difference between the third embodiment and the second embodiment is that the step S204 specifically includes:

[0062] In step S301 , based on preset category and quantity information of the equipment operating condition data to be transmitted back, a plurality of features of the equipment operating condition data to be transmitted back are randomly extracted to obtain a plurality of calibration features of the equipment operating condition data to be transmitted back.

[0063] In this embodiment, the preset number of categories of equipment condition data to be transmitted back can be set manually. Usually, the equipment condition data to be transmitted back has three categories: normal, warning, and fault. Therefore, the preset number of categories of equipment condition data to be transmitted back can be taken as 3, and 3 can be randomly selected from multiple features of equipment condition data to be transmitted back as calibration features to ensure that each category corresponds to at least one representative feature to avoid classification deviation due to feature redundancy or missing.

[0064] Step S302 : screening the characteristic correlation degrees of the plurality of equipment operating condition data to be transmitted back according to the plurality of calibration characteristics of the equipment operating condition data to be transmitted back, and obtaining the plurality of calibration characteristic correlation degrees of the equipment operating condition data to be transmitted back.

[0065] In this embodiment, the corresponding feature correlations can be screened based on the extracted calibration features of the equipment operating condition data to be transmitted back. For example, if the calibration features of the equipment operating condition data to be transmitted back are "high temperature," "high pressure," and "shutdown signal," the correlations between these three features and other features of the equipment operating condition data to be transmitted back are screened, such as the correlation between "high temperature and high pressure" and "shutdown signal and high temperature," as the calibration feature correlations of the equipment operating condition data to be transmitted back. This forms a calibration feature correlation set for subsequent validity determination.

[0066] Step S303, determine whether the correlation degree of the calibration characteristics of the equipment operating condition data to be transmitted back is greater than the preset equipment operating condition data characteristic correlation degree threshold; if so, enter step S304; if not, skip the equipment operating condition data characteristics to be transmitted back corresponding to the correlation degree of the calibration characteristics of the equipment operating condition data to be transmitted back and the equipment operating condition data calibration characteristics to be transmitted back corresponding to the correlation degree of the calibration characteristics of the equipment operating condition data to be transmitted back.

[0067] In this embodiment, the preset equipment operating condition data feature correlation threshold can be manually set to filter weakly correlated features, and a value of 0.7 can be used. If the correlation of a calibrated feature, such as the correlation between high temperature and high pressure of 0.85, is greater than the threshold of 0.7, the corresponding feature combination is retained. If a correlation, such as the correlation between the shutdown signal and temperature of 0.4, is less than the threshold of 0.7, the feature and its correlation are skipped to prevent invalid features from interfering with the classification decision.

[0068] Step S304 , generating an array of categories of the equipment condition data to be transmitted back according to the equipment condition data features to be transmitted back corresponding to the correlation degree of the calibration features of the equipment condition data to be transmitted back and the calibration features of the equipment condition data to be transmitted back corresponding to the correlation degree of the calibration features of the equipment condition data to be transmitted back.

[0069] In this embodiment, the device operating condition data features that pass the threshold screening and are to be transmitted back can be sorted by correlation with the calibration features to generate a category array for the device operating condition data to be transmitted back. For example, if the qualifying feature combination is "high temperature - high pressure" and "shutdown signal - no correlation feature," the category array can be represented as [high temperature - high pressure, fault condition matching degree 0.85], which is used to quantitatively represent the operating condition category.

[0070] Step S305 , calculating the median of the category array of the equipment operating condition data to be transmitted back, and obtaining the median of the category array of the equipment operating condition data to be transmitted back.

[0071] In this embodiment, the median of the association or matching values ​​in the category array can be calculated to reduce the impact of extreme values. For example, if the array is [0.78, 0.85, 0.92], the median is 0.85. This median is used as the median of the category array of the device operating condition data to be transmitted, and is used as the benchmark value for category judgment to improve classification stability.

[0072] Step S306, determine whether the value in the category array of the equipment operating condition data to be returned is equal to the calibration feature of the equipment operating condition data to be returned corresponding to the correlation degree of the calibration feature of the equipment operating condition data to be returned; if so, proceed to step S307; if not, proceed to step S308.

[0073] In this embodiment, if the feature combination corresponding to the median value in the category array of the equipment operating condition data to be transmitted back is consistent with the calibration feature of the equipment operating condition data to be transmitted back, the classification is considered valid; if the median value in the category array of the equipment operating condition data to be transmitted back does not correspond to the calibration feature of the equipment operating condition data to be transmitted back, it means that there is a deviation in the feature extraction or correlation calculation, and readjustment is required.

[0074] Step S307 : obtaining category information of the equipment operating condition data to be transmitted back according to the category array of the equipment operating condition data to be transmitted back.

[0075] In this embodiment, the operating condition category can be determined based on the valid category array of the equipment operating condition data to be transmitted back. For example, if the median value in the category array of the equipment operating condition data to be transmitted back corresponds to the fault operating condition matching degree, then the fault operating condition category information is generated; if the median value in the category array of the equipment operating condition data to be transmitted back corresponds to the warning operating condition matching degree, then the warning category information is generated to ensure that the category label is consistent with the feature correlation analysis result.

[0076] Step S308 , using the median of the category array of the equipment operating condition data to be transmitted back as a calibration feature of the equipment operating condition data to be transmitted back, and returning to step S302 .

[0077] In this embodiment, if the median of the category array of the equipment operating condition data to be transmitted back is inconsistent with the calibration features of the equipment operating condition data to be transmitted back, the median of the category array of the equipment operating condition data to be transmitted back is used as the new calibration features of the equipment operating condition data to be transmitted back, and the correlation degree is re-screened. The feature combination is iteratively optimized until the median of the category array of the equipment operating condition data to be transmitted back matches the calibration features of the equipment operating condition data to be transmitted back, thereby improving the classification accuracy.

[0078] The data feedback method provided in the embodiment of the present application dynamically adjusts the feature combination of working condition classification through random feature extraction, correlation screening and iterative optimization mechanism based on a preset number of categories, effectively copes with the complexity and uncertainty of industrial data, reduces classification errors caused by feature redundancy or missing, makes the working condition category information more in line with the actual operating status of the equipment, further improves the robustness and adaptability of the working condition classification, provides a more reliable decision-making basis for subsequent data screening and feedback, and optimizes the accuracy and effectiveness of industrial data feedback.

[0079] Figure 4 The following is a flowchart of the data transmission method according to the fourth embodiment of the present application, which differs from the first embodiment in that:

[0080] The plurality of preset device status data identification vectors include a preset device status data probe identification vector, a preset device status data tag identification vector, and a preset device status data value range identification vector;

[0081] The step S103 specifically includes:

[0082] Step S401, based on the multiple device status data to be transmitted back, the preset device status data probe identification vector, the preset device status data tag identification vector and the preset device status data value range identification vector, obtain multiple device status data probe identification features to be transmitted back, multiple device status data tag identification features to be transmitted back and multiple device status data value range identification features to be transmitted back.

[0083] In this embodiment, the preset device state data probe identification vector, the preset device state data tag identification vector, and the preset device state data range identification vector can all be manually set. The device state data to be transmitted back can be multiplied by the preset device state data probe identification vector, the preset device state data tag identification vector, and the preset device state data range identification vector, respectively, and the multiplication results are used as the device state data probe identification feature to be transmitted back, the device state data tag identification feature to be transmitted back, and the device state data range identification feature to be transmitted back.

[0084] In this embodiment, optionally, the preset device status data probe identification vector can be a physical location or communication interface for locating device components, such as a sensor ID or an actuator address. The device status data to be transmitted back can be matched with the probe identification vector, and the component identity feature can be extracted as the probe identification feature of the device status data to be transmitted back, such as "sensor ID-temperature probe"; the preset device status data label identification vector can include semantic labels of component status, such as "normal", "abnormal", and "fault". The status data can be assigned label features through text matching or rule mapping as label identification features of the device status data to be transmitted back, such as "temperature abnormality"; the preset device status data value range identification vector can be used to define the valid range of parameters, such as the normal value range of the temperature probe is [20°C, 80°C]. The status data parameters can be compared with the value range vector, and the features out of range can be extracted as the value range identification features of the device status data to be transmitted back, such as "temperature = 95°C-exceeding the upper limit".

[0085] Step S402 : obtaining a plurality of probe tag matching features of the device state data to be transmitted back according to the plurality of probe identification features of the device state data to be transmitted back and the plurality of tag identification features of the device state data to be transmitted back.

[0086] In this embodiment, the probe identification feature of the device state data to be transmitted back and the tag identification feature of the device state data to be transmitted back may be multiplied, and the multiplication result is used as the probe tag matching feature of the device state data to be transmitted back.

[0087] In this embodiment, the probe identification feature can optionally be associated and matched with the tag identification feature to form a combined feature of "component identity - status label." For example, if the probe identification feature is "sensor ID - temperature probe" and the tag identification feature is "temperature abnormality," the matching feature would be "sensor ID - temperature probe - abnormality." This clarifies the specific component corresponding to the abnormal state and avoids mismatches between the tag and component mapping.

[0088] Step S403 : obtaining a plurality of feature identification information of the device status data to be transmitted back according to the plurality of probe tag matching features of the device status data to be transmitted back and the plurality of value range identification features of the device status data to be transmitted back.

[0089] In this embodiment, the probe tag matching feature of the device status data to be transmitted back and the value range identification feature of the device status data to be transmitted back may be multiplied, and the multiplication result is used as the feature identification information of the device status data to be transmitted back.

[0090] In this embodiment, the probe tag matching feature can optionally be combined with the value range identification feature to generate comprehensive identification information that includes the component identity, status label, and parameter deviation. For example, if the probe tag matching feature is "Sensor ID - Temperature Probe - Abnormal" and the value range identification feature is "Temperature = 95°C - Exceeded Upper Limit," the comprehensive identification information would be "Sensor ID - Temperature Probe - Temperature 95°C (Exceeded Upper Limit) - Abnormal." This quantifies the specific parameters and degree of the abnormal state, providing detailed information for subsequent status assessment.

[0091] Step S404 : normalizing the plurality of feature identification information of the device status data to be transmitted back to obtain a plurality of identification information of the device status data to be transmitted back.

[0092] In this embodiment, the numerical parameters in the feature identification information of the device status data to be transmitted can be normalized to the range [0, 1] to eliminate dimensionality effects. Text labels such as "abnormal" and "fault" can be encoded, such as "abnormal = 1" and "fault = 2," to form a uniformly formatted identification information vector. For example, the normalized identification information can be [sensor ID, 1 (abnormal), 0.95 (normalized temperature deviation value)], facilitating subsequent in-depth calculations and ensuring the standardization and computability of the status identification results.

[0093] The data feedback method provided in the embodiment of the present application realizes multi-dimensional deconstruction and precise identification of the device status data to be returned through multi-dimensional vectors, quantifies the degree of difference between different device status data to be returned, avoids confusion between the device status data to be returned, and provides a more detailed priority basis for further screening of subsequent return data, thereby optimizing the accuracy and effectiveness of the device status information in the industrial data return process, and providing more reliable data support for equipment health diagnosis and fault tracing.

[0094] Figure 5 The flowchart of the data return method provided in the fifth embodiment of the present application is shown. The difference between the fifth embodiment and the first embodiment is that the step S104 specifically includes:

[0095] In step S501, according to the preset upper limit of the data representation information to be returned and the preset lower limit of the data representation information to be returned, the multiple return time information, the multiple device operating condition data category information to be returned, and the multiple device status data identification information to be returned are segmented and intercepted to obtain multiple return time information arrays, multiple device operating condition data category information arrays, and multiple device status data identification information arrays to be returned.

[0096] In this embodiment, the preset upper limit of the characterization information of the data to be returned and the preset lower limit of the characterization information of the data to be returned can be manually set, and can be used to determine the time window or characteristic interval of the data segmentation. For example, the time window is set to 1 minute, the lower limit is the current time-1 minute, and the upper limit is the current time. The data whose return time information falls within the window can be intercepted to form a return time information array; at the same time, the corresponding equipment working condition data category information and equipment status data identification information to be returned within the same time window are intercepted to generate a working condition category information array and a status identification information array respectively. If the amount of data is large, it can be segmented equidistantly according to time sequence or feature dimension, such as every 10 data as a segment, to ensure that the amount of data in each segment meets the transmission efficiency requirements.

[0097] Step S502 , according to a preset splicing order of the representation information of the data to be returned, the multiple return time information arrays, the multiple device operating condition data category information arrays to be returned, and the multiple device status data identification information arrays to be returned are spliced ​​to obtain multiple initial data set elements to be returned.

[0098] In this embodiment, the preset order of splicing the representation information of the data to be returned can be manually set, such as "time-operating condition-status", to standardize the data combination logic. The return time information array, operating condition category information array, and state identification information array within the same time window can be sequentially spliced ​​to form structured data units. These structured data units are then spliced ​​together to form the initial data set element to be returned. For example, the return time information array is [2025-05-20, 10:30:00.001, ...], the operating condition category information array is [2 (fault), ...], and the state identification information array is [sensor ID - abnormal, ...]. After splicing, the initial data set element to be returned is formed as "[timestamp, operating condition label, state identification result]", ensuring the temporal consistency of the three-dimensional information.

[0099] Step S503 : Based on the preset number of elements in the initial data set to be returned, multiple initial data sets to be returned are generated according to multiple elements in the initial data set to be returned.

[0100] In this embodiment, the preset number of elements in the initial data set to be transmitted back can be manually set and can be used to control the set size, that is, to limit the number of elements in each set. The elements of the spliced ​​initial data set to be transmitted back can be grouped sequentially, with the number of elements in each group meeting the preset requirements, to generate multiple initial data sets to be transmitted back. For example, if there are 500 elements in total and each group is preset to have 100 elements, then five initial sets will be generated, each containing data from a continuous time window, facilitating subsequent set-by-set filtering and processing, thereby balancing edge node cache capacity and data integrity requirements.

[0101] The data return method provided in the embodiment of the present application uses preset interception boundaries and splicing rules to structure and segment multidimensional data according to time windows or feature intervals, making the generation of the initial data set to be returned more standardized and controllable. The segmented interception adapts to the data volume requirements of different industrial scenarios and avoids transmission delays caused by data accumulation. The sequential splicing and grouping strategies ensure that the temporal correlation of the generated initial data set to be returned is preserved, providing structured data units for subsequent screening and processing, improving data screening efficiency, and thus optimizing the organizational logic and processing performance of industrial data return.

[0102] Figure 6 The flowchart of the data return method provided in the sixth embodiment of the present application is shown. The difference between the sixth embodiment and the first embodiment is that the step S105 specifically includes:

[0103] Step S601 , calculating and obtaining screening measurement information of multiple initial data sets to be returned based on the multiple initial data sets to be returned, the preset relevance weights of the time data to be returned, the preset importance weights of the working condition data to be returned, and the preset weights of the device status data to be returned.

[0104] In this embodiment, the preset relevance weight of the time data to be transmitted back, the preset importance weight of the working condition data to be transmitted back, and the preset weight of the device status data to be transmitted back can all be manually set. For example, the preset relevance weight of the time data to be transmitted back can be taken as 0.3, the preset importance weight of the working condition data to be transmitted back can be taken as 0.5, and the preset weight of the device status data to be transmitted back can be taken as 0.2, which can be used to quantify the importance of three-dimensional features. For each initial set of data to be transmitted back, the time relevance mean of all data in the set can be calculated first, such as the proportion of data in the past 1 hour; the working condition importance mean, such as the proportion of fault working condition data); the device status weight mean, such as the proportion of abnormal state data, and then the weighted sum of the preset relevance weight of the time data to be transmitted back, the preset importance weight of the working condition data to be transmitted back, and the preset weight of the device status data to be transmitted back can be used to obtain the initial data set to be transmitted back screening measurement information. For example, if the mean time correlation of an initial data set to be returned is 0.8, the mean working condition importance is 0.6, and the mean state weight is 0.7, then the screening measurement information = 0.3×0.8+0.5×0.6+0.2×0.7=0.68.

[0105] Step S602 , determining whether the maximum value of the plurality of initial to-be-returned data set screening metric information is greater than a preset to-be-returned data set screening metric threshold; if so, proceeding to step S603 ; if not, proceeding to step S604 .

[0106] In this embodiment, a preset threshold for screening metrics of the data set to be returned can be used to determine whether a high-validity data set exists among the multiple initial data sets to be returned. If the maximum value of the screening metrics of the multiple initial data sets to be returned is greater than the preset threshold for screening metrics of the data set to be returned, it indicates that a high-validity data set exists and no adjustment is required. If the maximum value of the screening metrics of the multiple initial data sets to be returned is less than or equal to the preset threshold for screening metrics of the data set to be returned, it indicates that iterative optimization is required to improve the validity of the data set.

[0107] Step S603 : The initial data set to be returned corresponding to the maximum value of the metric information of the plurality of initial data sets to be returned is selected as the target data set to be returned.

[0108] In this embodiment, the set with the largest screening metric information is directly selected as the target data set to be returned, thereby ensuring that the effectiveness of the returned data is maximized.

[0109] Step S604 : The initial data set to be returned corresponding to the maximum value of the metric information of the plurality of initial data sets to be returned is collected as a reference set of data to be returned.

[0110] In this embodiment, the initial set of data to be returned corresponding to the maximum value of the screening metric information of the multiple initial sets of data to be returned is used as a benchmark set of data to be returned for subsequent iterative optimization.

[0111] Step S605 : obtaining a plurality of to-be-transmitted data sets to be adjusted based on the plurality of initial to-be-transmitted data sets and the reference to-be-transmitted data sets.

[0112] In this embodiment, characteristic differences between each initial set of data to be returned and the reference set of data to be returned, such as time correlation difference and working condition importance difference, are calculated to determine the set to be adjusted as the set of data to be returned to be adjusted.

[0113] Step S606 : numerically adjusting the multiple sets of data to be returned to be adjusted according to the multiple sets of data to be returned to be adjusted and the randomly generated offset disturbance value of the set of data to be returned to be adjusted, to obtain multiple offset-adjusted sets of data to be returned.

[0114] In this embodiment, an offset disturbance value of the data set to be returned may be randomly generated in the interval [-0.1, 0.1], and used to adjust characteristics such as time correlation and working condition importance according to the disturbance value. The adjusted set is used as the offset adjustment set of the data to be returned.

[0115] Step S607 : generating a plurality of intermediate data sets to be transmitted back according to the plurality of offset adjustment sets of data to be transmitted back and the reference set of data to be transmitted back.

[0116] In this embodiment, multiple offset adjustment sets of data to be transmitted back and reference sets of data to be transmitted back may be merged to generate multiple intermediate sets of data to be transmitted back.

[0117] Step S608 , combining the plurality of intermediate data sets to be returned into a plurality of initial data sets to be returned, and returning to step S601 .

[0118] In this embodiment, the intermediate data set to be returned is taken as a new initial data set to be returned, and the intermediate data set to be returned is screened and optimized through iterative calculation until the maximum value of the screening metric information of the intermediate data set to be returned exceeds the preset screening metric threshold of the data set to be returned, so as to ensure that the effectiveness of the returned data set is maximized.

[0119] The data return method provided in the embodiment of the present application uses weighted calculation to screen metric information of the initial data set to be returned to quantify the effectiveness of the initial data set to be returned, combines a preset data set to be returned screening metric threshold to perform threshold judgment, and an iterative optimization mechanism for the initial data set to be returned, thereby dynamically improving the overall effectiveness of the returned data, avoiding invalid returns due to insufficient data effectiveness, adaptively matching industrial scenarios with uneven data value distribution, ensuring that key data is returned first, and improving the efficiency of equipment fault tracing and health analysis.

[0120] Figure 7 The flowchart of the data return method provided in the seventh embodiment of the present application is shown. The difference between the seventh embodiment and the first embodiment is that step S106 specifically includes:

[0121] Step S701 : Based on a preset threshold value of the number of bytes of the returned data frame, extract the target data set to be returned to obtain a plurality of initial data to be returned.

[0122] In this embodiment, the preset return data frame byte count threshold can be manually set, and can be set to 1024 bytes to limit the amount of data returned in a single time. The target data set to be returned can be traversed, and the data records can be extracted and encapsulated into data packets in sequence according to the time sequence or importance priority of the data records to ensure that the number of bytes of each data packet does not exceed the return data frame byte count threshold. For example, a complete data record contains a timestamp, a working condition label, and a state identification result. After encapsulation, it is 800 bytes. If it meets the return data frame byte count threshold requirement, it will be used as the initial data to be returned; if a record is 1200 bytes after encapsulation, it will be split into the next data packet to avoid transmission errors caused by excessive data frames.

[0123] Step S702 , performing splicing processing on the multiple initial data to be returned to obtain target data to be returned.

[0124] In this embodiment, multiple initial data packets to be transmitted back that meet the byte count threshold can be spliced ​​together in chronological order or priority order to form the complete target data packet to be transmitted back. For example, the fault condition data packets can be spliced ​​together first, followed by the warning condition data packets, to ensure that high-priority data is transmitted first. During the splicing process, data frame header identifiers and frame trailer checksums can be added to facilitate splitting and verification when the cloud receives the data.

[0125] Step S703: The target data to be returned is returned according to the preset data return bandwidth information.

[0126] In this embodiment, the preset data return bandwidth information can be manually set and used to control the data transmission rate. The maximum transmission frame rate can be calculated based on the bandwidth limit, and an adaptive packetization strategy can be used during transmission to dynamically adjust the packet size when the bandwidth fluctuates to ensure a stable return process.

[0127] The data return method provided in the embodiment of the present application controls the data packet size by using a threshold on the number of bytes in the return data frame to avoid fragmentation errors during transmission. The adaptive transmission strategy based on data return bandwidth information adapts to different network environments to avoid data loss or delay due to bandwidth limitations. This effectively balances data transmission efficiency and network stability in industrial Internet of Things scenarios, ensures real-time and complete return of key equipment data, and provides reliable data transmission guarantee for equipment fault diagnosis and remote monitoring.

[0128] Corresponding to the method of the above embodiment, Figure 8 The structural block diagram of the data return device provided in an embodiment of the present application is shown. For the convenience of explanation, only the parts related to the embodiment of the present application are shown. Figure 8 The exemplary data return device may be the execution subject of the data return method provided in the aforementioned embodiment 1.

[0129] Reference Figure 8 , the data return device includes:

[0130] The data acquisition module 810 is used to acquire a plurality of return time information, a plurality of device operating condition data to be returned, and a plurality of device status data to be returned; the return time information, the device operating condition data to be returned, and the device status data to be returned are in one-to-one correspondence;

[0131] The module 820 for generating the category information of the equipment operating condition data to be transmitted back is configured to classify the plurality of equipment operating condition data to be transmitted back according to a preset equipment operating condition data classification vector to obtain a plurality of category information of the equipment operating condition data to be transmitted back;

[0132] The device status data identification information generating module 830 is configured to perform identification processing on the plurality of device status data to be transmitted back according to a plurality of preset device status data identification vectors to obtain a plurality of device status data identification information to be transmitted back;

[0133] An initial data set to be returned generating module 840 is configured to generate a plurality of initial data sets to be returned based on the plurality of return time information, the plurality of device operating condition data category information to be returned, and the plurality of device status data identification information to be returned;

[0134] The target data set to be returned is determined by a module 850, configured to filter the plurality of initial data sets to be returned to obtain a target data set to be returned;

[0135] The target data to be returned generating module 860 is configured to perform data extraction processing on the target data set to be returned to obtain target data to be returned, so as to return the target data to be returned.

[0136] The process of each module in the data return device provided in the embodiment of the present application realizing its own function can be specifically referred to the aforementioned Figure 1 The description of the first embodiment is omitted here.

[0137] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0138] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0139] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0140] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0141] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish descriptions and should not be understood as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used in the text to describe various elements in some embodiments of the present application, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first table can be named a second table, and similarly, a second table can be named a first table without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0142] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0143] The data return method provided in the embodiments of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). The embodiments of the present application do not impose any restrictions on the specific types of terminal devices.

[0144] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a TV set-top box (STB), customer premise equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network.

[0145] As an example and not a limitation, when the terminal device is a wearable device, the wearable device can also be a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are full-featured, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0146] Figure 9 This is a schematic diagram of the structure of a terminal device provided by an embodiment of the present application. Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 Only one is shown), a memory 91, wherein the memory 91 stores a computer program 92 that can be run on the processor 90. When the processor 90 executes the computer program 92, the steps in the above-mentioned various data return method embodiments are implemented, such as Figure 1 Alternatively, when the processor 90 executes the computer program 92, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, Figure 8Functions of modules 810 to 860 are shown.

[0147] The terminal device 9 can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device can include, but is not limited to, a processor 90 and a memory 91. It can be understood by those skilled in the art that Figure 9 It is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include an input and sending device, a network access device, a bus, etc.

[0148] The processor 90 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0149] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard drive or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 91 may include both an internal storage unit of the terminal device 9 and an external storage device. The memory 91 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 91 may also be used to temporarily store data that has been sent or is about to be sent.

[0150] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0151] An embodiment of the present application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, the terminal device implements the steps of any of the above-mentioned method embodiments.

[0152] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0153] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0154] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0155] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0156] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0157] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0158] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A data return method, characterized in that: include: Acquire multiple return time information, multiple device working condition data to be returned, and multiple device status data to be returned; wherein the return time information, the device working condition data to be returned, and the device status data to be returned correspond to each other; Classify the plurality of equipment operating condition data to be transmitted back according to a preset equipment operating condition data classification vector to obtain category information of the plurality of equipment operating condition data to be transmitted back; Identify and process the plurality of device status data to be transmitted back according to a plurality of preset device status data identification vectors to obtain a plurality of device status data identification information to be transmitted back; Generate multiple initial data sets to be returned based on the multiple return time information, the multiple types of device operating condition data to be returned, and the multiple identification information of device status data to be returned; Screening the multiple initial data sets to be returned to obtain a target data set to be returned; The target data set to be returned is subjected to data extraction processing to obtain target data to be returned, so as to return the target data to be returned.

2. The data return method according to claim 1, wherein: The step of classifying the plurality of equipment operating condition data to be transmitted back according to a preset equipment operating condition data classification vector to obtain category information of the plurality of equipment operating condition data to be transmitted back specifically includes: Performing format conversion on the plurality of device operating condition data to be transmitted back to obtain a plurality of device operating condition data vectors to be transmitted back; Obtaining a plurality of features of the equipment operating condition data to be transmitted back according to the plurality of equipment operating condition data vectors to be transmitted back and a preset equipment operating condition data classification vector; Calculating the correlation between the plurality of characteristics of the equipment operating condition data to be transmitted back, to obtain the correlation between the plurality of characteristics of the equipment operating condition data to be transmitted back; According to the multiple features of the equipment operating condition data to be transmitted back, the multiple feature correlations of the equipment operating condition data to be transmitted back, and a preset equipment operating condition data feature correlation threshold, multiple category information of the equipment operating condition data to be transmitted back is obtained.

3. The data return method according to claim 2, wherein: The step of obtaining category information of the plurality of equipment operating condition data to be transmitted back according to the plurality of equipment operating condition data to be transmitted back, the plurality of correlation degrees of the equipment operating condition data to be transmitted back, and a preset equipment operating condition data characteristic correlation degree threshold specifically includes: According to the preset number of categories of the equipment working condition data to be transmitted back, randomly extracting the features of the plurality of equipment working condition data to be transmitted back, to obtain a plurality of calibration features of the equipment working condition data to be transmitted back; According to the plurality of calibration features of the equipment working condition data to be transmitted back, the characteristic correlation degrees of the plurality of equipment working condition data to be transmitted back are screened to obtain the plurality of calibration feature correlation degrees of the equipment working condition data to be transmitted back; When the correlation degree of the calibration feature of the equipment working condition data to be transmitted back is greater than a preset equipment working condition data characteristic correlation degree threshold, a category array of the equipment working condition data to be transmitted back is generated according to the equipment working condition data characteristics to be transmitted back corresponding to the correlation degree of the calibration feature of the equipment working condition data to be transmitted back and the calibration feature of the equipment working condition data to be transmitted back corresponding to the correlation degree of the calibration feature of the equipment working condition data to be transmitted back; Calculating the median of the category array of the equipment operating condition data to be transmitted back to obtain the median value of the category array of the equipment operating condition data to be transmitted back; Determine whether the value in the category array of the equipment operating condition data to be transmitted back is equal to the calibration feature of the equipment operating condition data to be transmitted back corresponding to the correlation degree of the calibration feature of the equipment operating condition data to be transmitted back; If yes, then obtaining the category information of the device operating condition data to be transmitted back according to the category array of the device operating condition data to be transmitted back; If not, the median of the category array of the equipment operating condition data to be returned is used as the calibration feature of the equipment operating condition data to be returned, and the process returns to the step of screening the correlation degrees of the characteristics of the multiple equipment operating condition data to be returned based on the multiple calibration features of the equipment operating condition data to be returned, and obtaining the correlation degrees of the calibration features of the multiple equipment operating condition data to be returned.

4. The data return method according to claim 1, wherein: The plurality of preset device status data identification vectors include a preset device status data probe identification vector, a preset device status data tag identification vector, and a preset device status data value range identification vector; The step of performing identification processing on the plurality of device status data to be transmitted back according to the plurality of preset device status data identification vectors to obtain the plurality of device status data identification information to be transmitted back specifically includes: According to the plurality of device status data to be transmitted back, the preset device status data probe identification vector, the preset device status data tag identification vector, and the preset device status data value range identification vector, a plurality of device status data probe identification features to be transmitted back, a plurality of device status data tag identification features to be transmitted back, and a plurality of device status data value range identification features to be transmitted back are obtained; Obtaining a plurality of probe tag matching features of the device status data to be transmitted back according to the plurality of probe identification features of the device status data to be transmitted back and the plurality of tag identification features of the device status data to be transmitted back; Obtaining multiple device status data feature identification information based on the multiple device status data probe tag matching features and the multiple device status data value range identification features; Normalization processing is performed on the plurality of feature identification information of the device status data to be transmitted back to obtain a plurality of identification information of the device status data to be transmitted back.

5. The data return method according to claim 1, wherein: The step of generating multiple initial data sets to be transmitted back based on the multiple transmission time information, the multiple device operating condition data category information to be transmitted back, and the multiple device status data identification information to be transmitted back specifically includes: According to a preset upper bound for intercepting the representation information of the data to be transmitted back and a preset lower bound for intercepting the representation information of the data to be transmitted back, segmentally intercepting the multiple pieces of the return time information, the multiple pieces of the category information of the device operating condition data to be transmitted back, and the multiple pieces of the identification information of the device status data to be transmitted back, to obtain multiple arrays of the return time information, multiple arrays of the category information of the device operating condition data to be transmitted back, and multiple arrays of the identification information of the device status data to be transmitted back; According to a preset splicing order of the representation information of the data to be returned, the multiple arrays of return time information, the multiple arrays of category information of the device operating condition data to be returned, and the multiple arrays of identification information of the device status data to be returned are spliced ​​to obtain multiple initial set elements of the data to be returned; Based on the preset number of elements in the initial data set to be returned, multiple initial data sets to be returned are generated according to multiple elements in the initial data set to be returned.

6. The data return method according to claim 1, wherein: The step of screening the multiple initial data sets to be returned to obtain the target data set to be returned specifically includes: Calculating the screening metric information of the multiple initial data sets to be returned based on the multiple initial data sets to be returned, the preset relevance weights of the time data to be returned, the preset importance weights of the working condition data to be returned, and the preset weights of the device status data to be returned; Determining whether a maximum value of the plurality of initial to-be-returned data set screening metric information is greater than a preset to-be-returned data set screening metric threshold; If so, the initial data set to be returned corresponding to the maximum value of the screening metric information of the multiple initial data sets to be returned is used as the target data set to be returned; If not, the initial set of data to be returned corresponding to the maximum value of the screening measurement information of the multiple initial sets of data to be returned is used as the benchmark set of data to be returned; Obtaining a plurality of to-be-transmitted data sets to be adjusted based on the plurality of initial to-be-transmitted data sets and the to-be-transmitted data reference sets; According to the multiple sets of data to be returned to be adjusted and the randomly generated offset disturbance values ​​of the sets of data to be returned, numerically adjusting the multiple sets of data to be returned to be adjusted to obtain multiple offset-adjusted sets of data to be returned; generating a plurality of intermediate data sets to be transmitted back according to the plurality of offset adjustment sets of data to be transmitted back and the reference set of data to be transmitted back; The multiple intermediate data sets to be returned are combined into multiple initial data sets to be returned, and the process returns to the step of calculating and obtaining the filtering measurement information of multiple initial data sets to be returned based on the multiple initial data sets to be returned, the preset correlation weights of the time data to be returned, the preset importance weights of the working condition data to be returned, and the preset weights of the equipment status data to be returned.

7. The data return method according to claim 1, wherein: The step of extracting and processing the target data set to be returned to obtain the target data to be returned, and returning the target data to be returned, specifically includes: Based on a preset threshold value of the number of bytes of the returned data frame, extracting the target data set to be returned and obtaining a plurality of initial data to be returned; Splicing the plurality of initial data to be returned to obtain target data to be returned; The target data to be returned is returned according to the preset data return bandwidth information.

8. A data return device, characterized in that: include: The module for acquiring data to be returned is used to acquire multiple pieces of return time information, multiple pieces of equipment working condition data to be returned, and multiple pieces of equipment status data to be returned; The return time information, the device operating condition data to be returned, and the device status data to be returned correspond one to one; a module for generating category information of equipment operating condition data to be transmitted back, configured to classify the plurality of equipment operating condition data to be transmitted back according to a preset equipment operating condition data classification vector, and obtain a plurality of category information of equipment operating condition data to be transmitted back; a module for generating identification information of device status data to be transmitted back, configured to perform identification processing on the plurality of device status data to be transmitted back according to a plurality of preset device status data identification vectors, and obtain a plurality of identification information of device status data to be transmitted back; An initial data set to be returned generating module, configured to generate a plurality of initial data sets to be returned based on the plurality of return time information, the plurality of device operating condition data category information to be returned, and the plurality of device status data identification information to be returned; a target data set to be returned determining module, configured to filter the plurality of initial data sets to be returned to obtain a target data set to be returned; The target data to be returned is generated by the module, which is used to extract the data to be returned from the target data set to be returned, obtain the target data to be returned, and return the target data to be returned.

9. A terminal device, characterized in that: The terminal device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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