WO2024088031A1 - 一种数据采集方法、装置及相关设备 - Google Patents

一种数据采集方法、装置及相关设备 Download PDF

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Publication number
WO2024088031A1
WO2024088031A1 PCT/CN2023/123300 CN2023123300W WO2024088031A1 WO 2024088031 A1 WO2024088031 A1 WO 2024088031A1 CN 2023123300 W CN2023123300 W CN 2023123300W WO 2024088031 A1 WO2024088031 A1 WO 2024088031A1
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data
scene
user
relevance
target
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PCT/CN2023/123300
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English (en)
French (fr)
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欧争光
黄志炜
郑子木
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华为云计算技术有限公司
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Publication of WO2024088031A1 publication Critical patent/WO2024088031A1/zh

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  • the present application relates to the field of artificial intelligence (AI), and in particular to a data collection method, device and related equipment.
  • AI artificial intelligence
  • AI models have been widely used in video imaging, speech recognition, natural language processing and other related fields.
  • AI models usually need to be trained with a large number of samples.
  • the sensor will upload the collected raw data to the server, and then annotate the raw data in the server to obtain samples, and then use the samples to train the AI model.
  • the raw data collected by the sensors may contain a large amount of data that is irrelevant to the business scenario. These data will also be uploaded to the server, causing pressure on data transmission and storage and reducing model training efficiency.
  • the present application provides a data collection method, apparatus and related equipment for solving the problem that the original data collected by the sensor is irrelevant to the business scenario, resulting in high pressure on data transmission and storage and low efficiency of model training.
  • a data collection method comprising the following steps: a data collection device obtains scene information input by a user, wherein the scene information includes business scene information corresponding to a model that the user needs to train, a target sensor in at least one sensor is determined according to the scene information, a plurality of data are obtained by performing data collection using the target sensor, the number of the target sensors is one or more, a scene relevance of each of the plurality of data is determined according to the scene information, the scene relevance of each data is used to describe the degree of relevance between each data and the business scene, and a sample set for training the model is sent to the user, wherein the sample set is determined according to the scene relevance of each data.
  • the data acquisition device can obtain the scene information input by the user, and then determine the target sensor among at least one sensor according to the scene information, use the target sensor to perform data acquisition to obtain multiple data, and then determine the scene relevance of each of the multiple data according to the scene information, and send a sample set for training the above model to the user, wherein the sample set is determined according to the scene relevance of each data, so that the data in the sample set is data related to the business scenario, and uploading the data in the sample set to the server can avoid data irrelevant to the business scenario from being uploaded to the server, thereby reducing the pressure of data transmission and storage and improving model training efficiency.
  • the method before the data acquisition device acquires the scene information input by the user, the method further includes the following steps: the data acquisition device provides sensor information to the user, wherein the sensor information includes parameter information of at least one sensor owned by the data acquisition device, and the sensor information is used for the user to determine the scene information.
  • the sensor information may include parameter information of the sensor in the data acquisition device, such as resolution, sensitivity, linear range, stability, accuracy, frequency response characteristics, sampling frequency, etc., which is not specifically limited in this application. It should be understood that the types of parameter information of different sensors may be different, which is not specifically limited in this application.
  • the sensor information may also include other relevant information for confirming the business scenario.
  • the sensor information may also include the equipment information of the data acquisition device, such as the equipment model, equipment function, resource status, equipment status, etc. of the data acquisition device, wherein the resource status may include but is not limited to the memory resource status, the central processing unit (CPU) resource status, network occupancy, etc., and the equipment status may include but is not limited to whether the sensor of the data acquisition device is damaged, whether the data acquisition device is in an abnormal state, etc.
  • the sensor information may also include the status information of the communication network between the data acquisition device and the client held by the user, such as bandwidth, network speed, etc. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the data acquisition device may report sensor information to the client after establishing a communication connection with the client held by the user for the first time, or report sensor information to the client after receiving a reporting request initiated by the client, or report the updated sensor information to the client after the sensor information of the data acquisition device is updated.
  • the user can input the information of the business scenario required by the user according to the sensor information displayed by the client. If the client displays that the data acquisition device does not have the sensor information required by the business scenario, the user can select other data acquisition devices that can meet the needs of the business scenario.
  • the data collection device is used to avoid the data collection device being unable to collect data related to the user's business scenario, thereby reducing the efficiency of model training.
  • the sensor information of the data acquisition device is reported to the user, so that the user can determine the input scene information based on the sensor information of the data acquisition device, thereby avoiding the situation where the data acquisition device cannot meet the user's needs and improving the user's experience.
  • the scenario information may be information of a business scenario required by the user, such as the name, number, abbreviation, etc. of the business scenario, which is not specifically limited in this application.
  • the business scenario required by the user may include, but is not limited to, image recognition, voice recognition, text recognition, image detection, etc., which is not specifically limited in this application.
  • the client can determine one or more scenarios that can be realized by the sensor information based on the sensor information, and then obtain the scene information selected by the user after displaying the above one or more scenarios to the user.
  • the user can also input the scene information by himself based on the sensor information displayed by the client, which is not specifically limited in this application.
  • the client can display multiple scene information to the user for selection through a visual interface, or input scene information through an application programming interface (API), which is not specifically limited in this application.
  • API application programming interface
  • the above implementation method by obtaining the scenario information input by the user, can ensure that the data in the final sample set is relevant to the business scenario, thereby avoiding data irrelevant to the business scenario from being uploaded to the server, reducing the pressure of data transmission and storage, and improving model training efficiency.
  • the data acquisition device may store the correspondence between scene information and target sensors. After obtaining the scene information input by the user, the target sensor corresponding to the scene information can be determined based on the correspondence, such as an image recognition scene corresponding to an image sensor, a voice recognition scene corresponding to a microphone, a sound sensor, etc. This application does not make any specific limitations.
  • the multiple target sensors corresponding to the scene information include not only the target sensors for direct business needs, but also the target sensors for business expansion needs, wherein the data collected by the target sensors for business expansion needs can provide more data dimensions to supplement the data collected by the target sensors for direct business needs.
  • the target sensor for direct business needs is a camera, but the sample data of the associated modalities collected by other sensors such as radar sensors, infrared sensors, and laser sensors may also provide data dimension supplements in the image recognition scenario.
  • These sensors may be target sensors for business expansion needs in the business scenario.
  • the target sensors in the image recognition scenario include not only cameras, but also radar sensors, infrared sensors, laser sensors, etc.
  • the above implementation method determines the target sensor based on the scene information confirmed by the user, and uses the target sensor to collect multiple data.
  • the multiple data provided to the user are data related to the business scenario confirmed by the user, thereby avoiding providing the user with data irrelevant to the business scenario, improving communication efficiency, and at the same time reducing sample storage pressure and avoiding waste of resources.
  • the multiple data collected using the target sensors may include multiple data collected by the multiple target sensors at the same time.
  • the multiple data may be obtained after performing data alignment operation on the data collected by the multiple target sensors. It should be understood that since the acquisition frequencies of different target sensors are different, the data collected by the multiple target sensors may be subjected to data alignment processing to obtain the multiple data.
  • a data set and a corresponding search index can be established for multiple data collected by multiple target sensors within a cycle time and the collection time, and the data collected at the same time can be quickly located and filtered based on the index, so as to obtain multiple aligned data. It should be understood that when aligning the data collected by multiple target sensors, data compensation can also be appropriately performed to obtain multiple aligned data, which is not specifically limited in this application.
  • each of the above multiple data may include data collected by multiple sensors at the same time, such as a radar frame and an image frame at the same time can be regarded as one data.
  • one data corresponds to data collected by one sensor, such as an image frame, a text, etc., which is not limited in this application.
  • the above implementation method aligns multiple data collected by multiple target sensors so that the data in the final sample set is data collected by multiple target sensors at the same time, thereby improving data accuracy and avoiding the problem of poor model training effect due to data errors.
  • the scenario relevance of the data is determined based on the degree of correlation between the data and the business scenario described by the scenario information. The higher the degree of correlation, the higher the scenario relevance of the data, and vice versa.
  • the scene relevance of the data is determined based on whether the data contains a target.
  • the scene relevance of the data containing the target is higher than the scene relevance of the data not containing the target.
  • the target may be a target required by a business scenario.
  • the target required by the business scenario may be a pedestrian; for example, when the business scenario is vehicle recognition, the target required by the business scenario may be a vehicle.
  • the above examples are for illustration only. This application is not specifically limited.
  • the scene relevance of the data is determined based on the number of targets contained in the data. The higher the number of targets contained in the data, the higher the scene relevance of the data, and vice versa. For example, when the business scenario is vehicle identification, the scene relevance of image data A containing two vehicles is higher than the scene relevance of image data B containing one vehicle.
  • the business scenario is vehicle identification
  • the scene relevance of image data A containing two vehicles is higher than the scene relevance of image data B containing one vehicle.
  • the scene relevance of the data is determined based on the degree of contribution of the data to model training. For example, the worse the data label confidence, the worse the sample clarity, and the worse the training effect, the lower the scene relevance of the data, and vice versa.
  • the target border of data C is blurred, or the border position is wrong and does not completely frame the target, while the target border of data D completely and clearly frames the target, then the scene relevance of data C is lower than the scene relevance of data D.
  • the scene relevance of the data is determined according to the device state when the data acquisition device 200 collects data.
  • the data acquisition device 200 is a park robot.
  • the park robot is in an abnormal posture, such as when the park robot falls or overturns, the scene relevance of the data E collected by it is lower than the scene relevance of the data F collected in a normal posture.
  • an abnormal posture such as when the park robot falls or overturns
  • the scene relevance of the data E collected by it is lower than the scene relevance of the data F collected in a normal posture.
  • the scene relevance of each data can be determined by an evaluation model, wherein the evaluation model can be obtained by training a neural network using an evaluation sample set, wherein the evaluation sample set includes evaluation samples and labels corresponding to the evaluation samples, the evaluation samples include known data, and the labels corresponding to the evaluation samples include the scene relevance corresponding to the known data.
  • the labels of the evaluation samples can be determined according to evaluation criteria, and the evaluation criteria can include but are not limited to one or more of whether the evaluation sample contains a target, the number of targets contained, and the sample confidence, and can also include other evaluation criteria, which are not given examples one by one here.
  • the above evaluation criteria may also include the device status of the data acquisition device.
  • the device status When the device status is poor, the scene relevance of the sample is low.
  • the device status may include but is not limited to whether the sensor of the data acquisition device is damaged, whether the data acquisition device is in an abnormal state, etc.
  • the abnormal state may include that the movement route of the park robot is seriously deviated. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the data acquisition device can use a general evaluation model to determine the scene relevance corresponding to each data, or determine the evaluation model corresponding to the business scenario based on the scene information selected by the user.
  • the evaluation criteria for the scene relevance of data in different business scenarios may be different. Therefore, the evaluation model corresponding to the business scenario can be used to evaluate the scene relevance of the data, thereby improving the accuracy of the acquisition of the scene relevance of the data. For example, in the target recognition scenario, the more targets there are in the sample, the higher the sample scene relevance. In the target detection scenario, the higher the confidence of the target bounding box, the higher the sample scene relevance. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the sample set can be made more in line with the business scenario required by the user, thereby avoiding providing the user with data irrelevant to the business scenario, improving communication efficiency, and at the same time reducing sample storage pressure and avoiding waste of resources.
  • the method before determining the scene relevance of each of the multiple data based on the scene information, the method further includes the following steps: obtaining a collection strategy input by a user, wherein the collection strategy includes target sample features of the samples required for model training.
  • the data collection device can also further determine the scene relevance of the data in combination with the collection strategy issued by the customer.
  • the data collection device can perform the following steps on any first data among the multiple data: when the first data meets the collection strategy, the first data is input into the evaluation model to obtain the scene relevance of the first data, or the first data is input into the evaluation model to obtain the first scene relevance, the second scene relevance is obtained according to the collection strategy, and the scene relevance of the first data is obtained according to the first scene relevance and the second scene relevance.
  • the acquisition of the first scene relevance and the second scene relevance can be carried out simultaneously or successively, and this application does not make specific limitations.
  • the collection strategy can include the texture features of the "fire hydrant" samples, where the texture features are used to describe the surface properties of the target in the image, such as the coarseness and density of the image texture.
  • texture features include grayscale co-occurrence matrix features, autoregressive texture model features, Tamura texture features, wavelet transform features, etc., which are not specifically limited in this application. Among them, the more similar the sample features of the data are to the target sample features, the higher the scene relevance of the data.
  • the collection strategy includes a target collection condition, wherein the data in the sample set is data collected under the target collection condition, the scene relevance of the data collected under the target collection condition is high, and the scene relevance of the data collected under the non-target collection condition is low, or the data collected under the non-target collection condition is not evaluated for scene relevance, such as the scene relevance is 0, and only the data collected under the target collection condition is input into the evaluation model for scene relevance evaluation. For example, if the user needs to collect samples collected by the target sensor when the ambient temperature is higher than 40 degrees Celsius, then the scene relevance evaluation is not performed for the samples collected by the target sensor when the ambient temperature is lower than 40 degrees Celsius.
  • the collection strategy may include a target indicator, the data that meets the target indicator has a high scene relevance, and the data that does not meet the target indicator has a low scene relevance, or the data that does not meet the target indicator is not evaluated for scene relevance, and only the data that meets the target indicator is input into the evaluation model. For example, if the user needs samples with a confidence level higher than a threshold value X, then the scene relevance evaluation is not performed for data with a confidence level lower than the threshold value X. It should be understood that the above examples are for illustration only and are not specifically limited in this application.
  • the collection strategy includes data priority information, wherein the scene relevance of data with high priority is higher than the scene relevance of data with low priority.
  • data with high scene relevance is uploaded first than data with low scene relevance.
  • data with low priority can be collected after data with high priority is collected.
  • the priority of difficult samples in the collection strategy issued by the user is higher than that of non-difficult samples.
  • difficult samples can be uploaded first, and then non-difficult samples can be uploaded.
  • This application does not make specific restrictions.
  • the recognition accuracy of the business model currently trained by the user is poor, and it is necessary to collect samples with rich elements first.
  • data with rich elements refers to the number of targets in the data collected by the target sensor being higher than the threshold.
  • the target here can be the target required by the user, or it can be a target that is not required by the user, it can be an identified target, or it can be an unknown target.
  • the target sensor includes a laser sensor and an image sensor. In the first data collected by the target sensor, the image sensor collects and identifies object A in the scene, and the laser sensor detects obstacles A, B, and C in the scene.
  • object A is a known target
  • objects B and C are unknown targets.
  • the first data is element-rich data.
  • the collection strategy may include an upload logic, and the upload logic includes any one of real-time upload, upload when resource occupancy is low, scheduled upload, and periodic interval upload.
  • the sample set is uploaded to the server according to the upload logic, wherein the server is used for users to train models.
  • the upload logic may include real-time upload, upload when resource occupancy is low, scheduled upload, periodic interval upload, etc., which are not specifically limited in this application.
  • the collection strategy that the user can input may include real-time upload.
  • the collection strategy that the user input may include upload when resource occupancy is low.
  • the data collection device can obtain local resource status, including memory, CPU, network occupancy, etc., and package and upload when the resource status is reduced. It should be understood that the above upload logic is used for illustration. In different application scenarios, users can flexibly set the upload logic required by the scenario, and examples are not given here one by one.
  • the user's collection strategy can be sent to the data collection device in the form of an instruction.
  • the instruction may include multiple parameters, one parameter corresponding to an upload strategy, such as parameter 1 corresponding to the upload logic, parameter 2 corresponding to the priority information, parameter 3 corresponding to the target indicator, parameter 4 corresponding to the target collection condition, parameter 5 corresponding to the target sample feature, etc., which are not illustrated one by one here.
  • the evaluation unit can determine the collection strategy sent by the user according to the parameters in the instruction.
  • the upload logic can be determined according to the value of parameter 1, such as uploading when the resource occupancy is low, and the target sample feature can be determined according to the value of parameter 5, such as the texture feature of "fire hydrant", and then the scene relevance of each data in the multiple data is determined according to the collection strategy and the evaluation model.
  • parameter 1 such as uploading when the resource occupancy is low
  • parameter 5 such as the texture feature of "fire hydrant”
  • the scene relevance of each data in the multiple data is determined according to the collection strategy and the evaluation model.
  • the above implementation method by obtaining the user's collection strategy and determining the sample set used for training the model according to the collection strategy, can make the sample set more in line with the business scenario required by the user, thereby avoiding providing the user with data irrelevant to the business scenario, improving communication efficiency, and at the same time reducing sample storage pressure and avoiding waste of resources.
  • the sample set may include data whose scene relevance is higher than a threshold, and the data acquisition device uploads the data whose scene relevance is higher than the threshold to the server.
  • the data in the sample set is obtained by the data acquisition device after sorting the data according to the scene relevance of the data, and the upload order of data with high scene relevance is earlier than that of data with low scene relevance.
  • the specific order can be determined according to the user's collection strategy.
  • the data acquisition device may first store the sample set in a memory of the data acquisition device, which establishes a communication connection with the server, so that the server can obtain the sample set in the memory based on the user's reporting policy, wherein the above-mentioned memory may be a removable storage medium, such as a U disk, mobile hard disk, floppy disk, CD, memory card, etc. of the data acquisition device, which is not specifically limited in this application.
  • the data acquisition device may also label the sample set to obtain a label for each data in the sample set.
  • the label may be a manually labeled label or a machine labeled label, which may be determined according to the processing capability of the data acquisition device and is not specifically limited in this application.
  • the scene relevance of each data can be displayed to the user through the client.
  • the user can select data in the sample set according to business needs, and then upload the sample set to the server.
  • the above implementation method determines the sample set according to the scenario relevance of the data, so that the data in the sample set are all related to the business scenarios required by the user, thereby avoiding providing the user with data irrelevant to the business scenario, improving communication efficiency, and at the same time reducing the pressure on sample storage and avoiding waste of resources.
  • a new acquisition strategy is obtained, and the new acquisition strategy is used to train the model.
  • the collection strategy determines a new sample set, wherein the new collection strategy is determined by the server after training the model using the sample set, or the new collection strategy is input by the user.
  • the server can obtain an updated collection strategy based on the collection strategy of the incremental learning stage, and send the updated collection strategy to the data collection device, so that the data collection device can perform scene relevance evaluation on the new data collected by the target sensor according to the updated collection strategy.
  • the updated collection strategy may include priority information, and the priority information includes information on giving priority to collecting difficult samples.
  • a new collection strategy can also be obtained based on the training results of the business model and the data dimension of the current sample, wherein the new collection strategy is used to obtain associated data, and the data dimension of the associated data is different from the data dimension of the current sample, such as the current sample is unimodal data, and the associated data is multimodal data.
  • the data dimension of the current sample is unimodal data, such as only video frames collected by a camera
  • the business model is a model for identifying fire hydrants.
  • the training results of the model show that the accuracy of the model prediction is low. At this time, more samples are needed.
  • the server can obtain associated data based on this, such as samples collected by lasers and samples collected by radars. Associated samples can improve the prediction ability of the model. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the server can display the training results of the business model to the user and receive the updated collection strategy input by the user.
  • the user can also send a new collection strategy to the data collection device through the client, which is not specifically limited in this application.
  • the user believes that the business model has a high ability to identify samples of category A, but a poor ability to identify samples of category B based on the training results of the business model.
  • the current business scenario requires the business model to have the ability to identify samples of category B, and the user hopes to obtain more samples of category B to further train the business model.
  • the user can input a new collection strategy through the client.
  • the new collection strategy includes target sample features, that is, texture features of category B samples, and may also include priority information, that is, information on the priority collection of category B samples.
  • the client 100 can send the above new collection strategy to the data collection device, so that it can prioritize the collection of category B data to the server. After the server labels the category B data, it can obtain category B samples.
  • the above implementation method updates the collection strategy according to the training results, and then uses the new collection strategy to obtain a new sample set to further train the model.
  • the model is continuously trained with sample sets related to the business scenario, which solves the problem that the sample set requirements of the model will change over time and improves the user experience.
  • a data acquisition device which includes an acquisition unit for acquiring scene information input by a user, wherein the scene information includes business scene information corresponding to a model that the user needs to train, an acquisition unit for determining a target sensor among at least one sensor according to the scene information, and using the target sensor to perform data acquisition to obtain multiple data, wherein the number of target sensors is one or more, an evaluation unit for determining the scene relevance of each of the multiple data according to the scene information, and an upload unit for sending a sample set for training the model to the user, wherein the sample set is determined according to the scene relevance of each data.
  • the data acquisition device can obtain the scene information input by the user, and then determine the target sensor among at least one sensor according to the scene information, use the target sensor to perform data acquisition to obtain multiple data, and then determine the scene relevance of each of the multiple data according to the scene information, and send a sample set for training the above model to the user, wherein the sample set is determined according to the scene relevance of each data, so that the data in the sample set is data related to the business scenario, and uploading the data in the sample set to the server can avoid data irrelevant to the business scenario from being uploaded to the server, thereby reducing the pressure of data transmission and storage and improving model training efficiency.
  • the scene relevance of the data in the sample set is higher than a threshold, or the data in the sample set is obtained by sorting multiple data according to their scene relevance.
  • the device includes a reporting capability unit, which is used to provide sensor information to the user before the data acquisition device obtains scene information input by the user, wherein the sensor information includes parameter information of at least one sensor owned by the data acquisition device, and the sensor information is used for the user to determine the scene information.
  • a reporting capability unit which is used to provide sensor information to the user before the data acquisition device obtains scene information input by the user, wherein the sensor information includes parameter information of at least one sensor owned by the data acquisition device, and the sensor information is used for the user to determine the scene information.
  • the acquisition unit is also used to obtain an acquisition strategy input by a user, wherein the acquisition strategy includes target sample features of samples required for model training; and an evaluation unit is used to determine a first scene relevance of each data based on scene information, determine a second scene relevance of each data based on the acquisition strategy, and determine a scene relevance of each data based on the first scene relevance and the second scene relevance, wherein the higher the similarity between the data features of each data and the target sample features, the higher the second scene relevance of the data.
  • the collection strategy includes a target collection condition, wherein the data in the sample set is data collected under the target collection condition.
  • the collection strategy includes priority information of the data, wherein the scene relevance of data with a high priority is higher than the scene relevance of data with a low priority.
  • the collection strategy includes an upload logic, and the upload logic includes any one of real-time upload, upload when resource usage is low, scheduled upload, and periodic interval upload.
  • the upload unit is used to upload the sample set to the server according to the upload logic, wherein the server is used for users to train models.
  • the evaluation unit is used to determine an evaluation model corresponding to scene information, use the evaluation model to perform scene relevance evaluation on data, and determine a first scene relevance of the data, wherein different scene information corresponds to different evaluation models.
  • the collection unit is also used to obtain a new collection strategy and determine a new sample set based on the new collection strategy, wherein the new collection strategy is determined by the server after training the model using the sample set, or the new collection strategy is input by the user.
  • a computing device which includes a processor and a memory, the memory is used to store code, and the processor is used to execute the code to implement the method described in the first aspect.
  • a computer-readable storage medium comprising instructions, which, when executed on a computing device, enable the computing device to implement the method described in the first aspect.
  • a computer program product includes a code, and when the code is executed on a computer, the computer implements the method described in the first aspect.
  • FIG1 is an architecture diagram of a data acquisition system provided by the present application.
  • FIG2 is a flow chart of a data collection method provided by the present application.
  • FIG3 is an example diagram of a configuration page provided by the present application.
  • FIG4 is an example diagram of another configuration page provided by the present application.
  • FIG5 is a schematic diagram of the structure of a computing device provided by the present application.
  • AI is a theory, method, technology and application system that uses digital computers or computing devices controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
  • the application scenarios of artificial intelligence are very wide, such as face recognition, vehicle recognition, pedestrian re-identification, data processing applications, robotics, autonomous driving, etc.
  • AI model is a collection of mathematical methods to implement AI. A large number of samples can be used to train the AI model so that the trained AI model can obtain prediction capabilities.
  • sensors will upload the collected raw data to the server in a unified manner.
  • the samples obtained after processing the raw data in the server can be used to train the AI model.
  • the data processing here may include but is not limited to data cleaning, sample labeling, data screening, etc. Data processing is a laborious and time-consuming process that requires expensive labor costs to build a usable data set.
  • the raw data collected by the sensor may contain a large amount of data without scene relevance, such as duplicate data, unclear and unusable data, or data irrelevant to the business scenario, etc. These data without scene relevance will also be uploaded to the server, causing pressure on data transmission and storage, and reducing the efficiency of model training.
  • Even if the data collection rules are predetermined in advance it will take a lot of time and energy for technical personnel to formulate the collection rules. Not only will there be human errors and it will be difficult to scale, but the rules must be manually customized every time data is collected, and the update of the collection rules is also prone to untimely situations.
  • the raw data collected by sensors may contain a large amount of data that is irrelevant to the business scenario. These data will be uploaded to the server, which may easily cause pressure on data transmission and storage and reduce the efficiency of model training.
  • the present application provides a data acquisition system, wherein the data acquisition device in the system can obtain scene information input by the user, and the scene information includes information on the business scenario corresponding to the model that the user needs to train, and then determine a target sensor in at least one sensor according to the scene information, use the target sensor to perform data acquisition to obtain multiple data, and then determine the scene relevance of each of the multiple data according to the scene information, and send a sample set for training the above model to the user, wherein the sample set is determined according to the scene relevance of each data, so that the data in the sample set is data related to the business scenario, and uploading the data in the sample set to the server can avoid uploading data irrelevant to the business scenario to the server, thereby reducing the pressure of data transmission and storage and improving the efficiency of model training.
  • FIG1 is an architecture diagram of a data acquisition system provided by the present application.
  • the data acquisition system may include The client 100, the data acquisition device 200 and the server 300, wherein there is a communication connection between the client 100, the data acquisition device 200 and the server 300, which can be a wired network or a wireless network, which is not specifically limited in this application.
  • the number of the client 100, the data acquisition device 200 and the server 300 can be one or more, which is not specifically limited in this application.
  • the client 100 may be a computing device, a terminal device or an edge computing device that interacts with a user.
  • the computing device may include but is not limited to a physical server, a virtual machine, a container, a chip, etc., wherein a physical server may include but is not limited to an ARM server, an X86 server, etc.; a virtual machine refers to a complete computer system with complete hardware system functions implemented by network function virtualization (NFV) technology and simulated by software, running in a completely isolated environment; a container refers to a group of processes that are resource-constrained and isolated from each other; a terminal device may be a smart phone, a handheld processing device, a tablet computer, a mobile notebook, an augmented reality (AR) device, a virtual reality (VR) device, an integrated handheld device, a wearable device, a vehicle-mounted device, an intelligent conference device, an intelligent advertising device, an intelligent home appliance, etc., which are not specifically limited here.
  • AR augmented reality
  • VR virtual reality
  • An edge computing device refers to a computing device that is close to the data acquisition device 200 and has computing power.
  • the edge computing device can pre-process the data collected by the data acquisition device 200, and then send the pre-processed data to a remote end for storage, thereby saving communication bandwidth, reducing data traffic, and improving data security.
  • the data acquisition device 200 may be a computing device, terminal device, robot or vehicle of one or more sensors, wherein one or more sensors may include an image sensor, specifically a camera for facial image recognition and eye iris recognition, such as a voice sensor, specifically a microphone array for voice recognition, and may also include other information acquisition sensors that can sense the specified measured quantity (physical quantity, chemical quantity, state quantity and mechanical quantity) and convert it into a usable output signal according to a certain rule, such as a laser sensor, a photosensitive sensor, a gas sensor, a radar sensor, a chemical sensor, a pressure sensor, a temperature sensor, a fluid sensor, an acceleration sensor, a gyroscope sensor, a geomagnetic sensor, an infrared sensor, an electrocardiogram sensor, a pulse pressure sensor, etc.
  • a laser sensor a photosensitive sensor
  • gas sensor specifically a camera for facial image recognition and eye iris recognition
  • voice sensor specifically a microphone array for voice recognition
  • other information acquisition sensors that can sense the specified measured quantity (physical quantity,
  • the server 300 may be a computing device, such as a bare metal server (BMS), a virtual machine, a container, an edge computing device, etc.
  • BMS refers to a general physical server, for example, an ARM server or an X86 server.
  • the description of the virtual machine and the container can refer to the above content, which will not be repeated here.
  • the server 300 may also be a server for storing data, such as a storage array, which is not specifically limited in this application.
  • the server 300 may also be a server cluster composed of multiple computing devices mentioned above, such as a centralized server cluster or a distributed server cluster, which is not specifically limited in this application.
  • the client 100 can be deployed in the server 300, or the client 100 can be deployed in the data acquisition device 200, or the data acquisition device 200 and the server 300 can be the same computing device, or the client 100, the data acquisition device 200 and the server 300 are the same computing device, or the client 100, the data acquisition device 200 and the server 300 are on different computing devices, and this application does not make any specific limitation.
  • the server 300 can also be deployed in a public cloud, and the client 100 is the console or API of the public cloud, which is not specifically limited in this application.
  • the data acquisition device 200 is a campus robot
  • the client 100 can be a web-based management system
  • the server 300 is an object storage service (OBS) device in the public cloud
  • the user 400 can purchase the OBS storage service of the public cloud through the client 100
  • the data collected by the campus robot can be uploaded to the public cloud for storage.
  • OBS object storage service
  • the client 100, the data acquisition device 200 and the server 300 can be divided into a plurality of unit modules.
  • the client 100 may include a scene confirmation unit 101 and a policy issuing unit 102.
  • the data acquisition device 200 may include a reporting capability unit 201, a collection unit 202, an evaluation unit 203 and an upload unit 204.
  • the server 300 may include a model training unit 301 and a policy updating unit 302. It should be understood that the division method in FIG1 is for illustration, and the client 100, the data acquisition device 200 and the server 300 may also include more or fewer unit modules, which is not specifically limited in this application.
  • the reporting capability unit 201 in the data acquisition device 200 is used to obtain sensor information in the data acquisition device 200 and report it to the client 100.
  • the sensor information may include parameter information of the sensor in the data acquisition device 200, such as resolution, sensitivity, linear range, stability, accuracy, frequency response characteristics, sampling frequency, etc., which is not specifically limited in this application. It should be understood that the parameter information types of different sensors may be different, which is not specifically limited in this application.
  • the sensor information may also include other relevant information for confirming the business scenario.
  • the sensor information may also include the device information of the data acquisition device 200, such as the device model, device function, resource status, device status, etc. of the data acquisition device 200, wherein the resource status may include but is not limited to memory, CPU, network occupancy, etc., and the device status may include but is not limited to whether the sensor of the data acquisition device 200 is damaged, whether the data acquisition device 200 is in an abnormal state, etc.
  • the sensor information may also include the status information of the communication network between the data acquisition device 200 and the client 100, such as bandwidth, network speed, etc. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the reporting capability unit 201 may report sensor information to the client 100 after establishing a communication connection with the client 100 for the first time, or report sensor information to the client 100 after receiving a reporting request initiated by the client 100, or report the updated sensor information to the client after the sensor information of the data acquisition device 200 is updated.
  • This application does not make any specific limitations.
  • the scene confirmation unit 101 in the client 100 is used to receive the sensor information reported by the reporting capability unit 201 , display the sensor information to the user 400 , receive the scene information input by the user 400 , and return the scene information to the data acquisition device 200 .
  • the scene information may be information about the business scene required by the user, such as the name, number, abbreviation, etc. of the business scene, which is not specifically limited in this application.
  • the business scene required by the user may include, but is not limited to, image recognition, voice recognition, text recognition, image detection, etc., which is not specifically limited in this application.
  • image recognition the user can enter the scene information as number 01
  • voice recognition the user can enter the scene information as number 02
  • the user's business scene is text recognition the user can enter the scene information as number 03, and so on.
  • the user can input information about the business scenario required by the user based on the sensor information displayed by the client 100. If the client 100 displays that the data acquisition device 200 does not have the sensor information required for the business scenario, the user can select other data acquisition devices 200 that can meet the needs of the business scenario, thereby avoiding the data acquisition device 200 being unable to collect data related to the user's business scenario, thereby reducing the efficiency of model training.
  • the client 100 can determine one or more scenarios that can be realized by the sensor information based on the sensor information, and then obtain the scene information selected by the user after displaying the above one or more scenes to the user.
  • the user can also input the scene information by himself according to the sensor information displayed by the client 100, which is not specifically limited in this application.
  • the client 100 can display multiple scene information to the user for selection through a visual interface, or input the scene information through an application program interface (API), which is not specifically limited in this application.
  • API application program interface
  • the collection unit 202 in the data collection device 200 is used to receive the scene information sent by the client 100, determine the target sensor according to the scene information, and use the target sensor to collect multiple data, wherein the number of the target sensors can be one or more.
  • the data acquisition device 200 may store the correspondence between scene information and target sensors, such as image recognition scenes corresponding to image sensors, and speech recognition scenes corresponding to microphones, acoustic sensors, etc. This application does not make specific limitations.
  • the multiple target sensors corresponding to the scene information include not only the target sensors required by the business directly, but also the target sensors required by the business expansion, wherein the data collected by the target sensors required by the business expansion can provide more data dimensions to supplement the data collected by the target sensors required by the business directly.
  • the target sensor required by the business directly is a camera, but the sample data of the associated modalities collected by other sensors such as radar sensors, infrared sensors, and laser sensors may also provide data dimension supplements in the image recognition scenario. These sensors may be the target sensors with business expansion requirements in the business scenario.
  • the target sensors in the image recognition scenario include not only cameras, but also radar sensors, infrared sensors, laser sensors, etc. It should be understood that the above examples are used to illustrate that in different business scenarios, the target sensors required by the business directly and the target sensors required by the business expansion are different, and examples are not given one by one here.
  • the target sensor is determined based on the scene information confirmed by the user, and multiple data are collected using the target sensor.
  • the multiple data provided to the user are data related to the business scenario confirmed by the user, thereby avoiding providing the user with data irrelevant to the business scenario, improving communication efficiency, reducing the sample storage pressure of the server 300, and avoiding waste of resources.
  • the multiple data collected by the collection unit 202 using the target sensors may include multiple data collected by the multiple target sensors at the same time.
  • the multiple data may be obtained after performing data alignment operation on the data collected by the multiple target sensors. It should be understood that since the collection frequencies of different target sensors are different, the data collected by the multiple target sensors may be subjected to data alignment processing to obtain the multiple data.
  • a data set and a corresponding search index can be established for multiple data collected by multiple target sensors within a cycle time and the collection time, and the data collected at the same time can be quickly located and filtered out based on the index, so as to obtain multiple aligned data.
  • the image sensor collected 3 image frames at 00:01, 00:03, and 00:05
  • the radar sensor collected 6 radar frames at 00:01, 00:02, 00:03, 00:04, 00:05, and 00:06.
  • 3 groups of aligned data can be obtained.
  • the first group of data includes radar frames and image frames at 00:01
  • the second group of data includes radar frames and image frames at 00:03
  • the third group of data includes radar frames and image frames at 00:01.
  • data compensation can also be performed appropriately to obtain multiple aligned data.
  • the image sensor collects 3 image frames at 00:01, 00:03, and 00:05
  • the radar sensor collects 3 radar frames at 00:02, 00:04, and 00:06.
  • the compensation frames of 00:02, 00:04, and 00:06 can be determined based on the image frames collected at 00:01, 00:03, and 00:05, thereby obtaining multiple aligned data, such as the radar frame and image frame at 00:02. This will not be explained one by one here. It should be understood that The above examples are for illustration only and are not specifically limited in this application.
  • each of the above multiple data may include data collected by multiple sensors at the same time, such as a radar frame and an image frame at the same time can be regarded as one data.
  • one data corresponds to data collected by one sensor, such as an image frame, a text, etc., which is not limited in this application.
  • the evaluation unit 203 of the data collection device 200 can perform scene relevance evaluation on the multiple data collected by the collection unit 202 to obtain scene relevance corresponding to the multiple data.
  • the scenario relevance of the data is determined based on the degree of correlation between the data and the business scenario described by the scenario information. The higher the degree of correlation, the higher the scenario relevance of the data, and vice versa.
  • the scene relevance of the data is determined based on whether the data contains a target.
  • the scene relevance of data containing a target is higher than the scene relevance of data not containing a target.
  • the target may be a target required for a business scenario.
  • the target required for the business scenario may be a pedestrian; for example, if the business scenario is vehicle recognition, the target required for the business scenario may be a vehicle.
  • the above examples are for illustration only and are not specifically limited in this application.
  • the scene relevance of the data can also be determined based on the number of targets contained in the data. The higher the number of targets contained in the data, the higher the scene relevance of the data, and vice versa. For example, when the business scenario is vehicle identification, the scene relevance of image data A containing 2 vehicles is higher than the scene relevance of image data B containing 1 vehicle.
  • the business scenario is vehicle identification
  • the scene relevance of image data A containing 2 vehicles is higher than the scene relevance of image data B containing 1 vehicle.
  • the scene relevance of the data can also be determined based on the degree of contribution of the data to the model training.
  • the worse the label confidence, the worse the sample clarity, and the worse the training effect the lower the scene relevance of the data, and vice versa.
  • the target border of data C is blurred, or the border position is wrong, or the target border does not frame the complete target, and the target border of data D completely and clearly frames the target, then the scene relevance of data C is lower than the scene relevance of data D.
  • the above examples are for illustration only and are not specifically limited in this application.
  • the scene relevance of the data can also be determined based on the device state when the data acquisition device 200 collects data.
  • the data acquisition device 200 is a park robot.
  • the park robot is in an abnormal posture, such as when the park robot falls or overturns, the scene relevance of the data E collected by it is lower than the scene relevance of the data F collected in a normal posture.
  • an abnormal posture such as when the park robot falls or overturns
  • the scene relevance of the data E collected by it is lower than the scene relevance of the data F collected in a normal posture.
  • the evaluation unit 203 may input multiple data into an evaluation model to obtain the scene relevance corresponding to each of the multiple data, wherein the evaluation model may be obtained by training a neural network using an evaluation sample set, wherein the evaluation sample set includes evaluation samples and labels corresponding to the evaluation samples, the evaluation samples include known data, and the labels corresponding to the evaluation samples include the scene relevance corresponding to the known data.
  • the labels of the evaluation samples may be determined according to evaluation criteria, and the evaluation criteria may include but are not limited to one or more of whether the evaluation sample contains a target, the number of targets contained, and the sample confidence, and may also include other evaluation criteria, which are not illustrated one by one here.
  • the evaluation unit 203 can use a general evaluation model to determine the scene relevance corresponding to each data, or determine the evaluation model corresponding to the business scenario based on the scene information selected by the user. It should be understood that the evaluation criteria for the scene relevance of data in different business scenarios may be different, so the evaluation model corresponding to the business scenario can be used to evaluate the scene relevance of the data, thereby improving the accuracy of the acquisition of the scene relevance of the data. For example, in the target recognition scenario, the more targets there are in the sample, the higher the sample scene relevance. In the target detection scenario, the higher the confidence of the target bounding box, the higher the sample scene relevance. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the above evaluation criteria may also include the device status of the data acquisition device 200.
  • the device status When the device status is poor, the scene relevance of the sample is low.
  • the device status may include but is not limited to whether the sensor of the data acquisition device 200 is damaged, whether the data acquisition device 200 is in an abnormal state, etc.
  • the abnormal state may include that the movement route of the park robot is seriously deviated. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the evaluation criteria in the target detection scenario may include the confidence of the sample, the number of targets in the sample, whether the device status was abnormal when the sample was collected, etc. If the sample is multimodal data, such as image data collected by a camera and laser data collected by a laser sensor, then the evaluation criteria may also include the target matching degree between the image data and the laser data. It should be understood that the above examples are for illustration only and are not specifically limited in this application.
  • the policy issuing unit 102 of the client 100 may obtain the collection policy issued by the user and send it to the data collection device 200.
  • the evaluation unit 203 of the data collection device 200 may further determine the scene relevance of the first data in combination with the collection policy issued by the user.
  • the evaluation unit 203 may input the first data into the evaluation model to obtain the scene relevance of the first data when the first data conforms to the collection strategy, or the evaluation unit 203 may input the first data into the evaluation model to obtain the first scene relevance, and The strategy obtains the second scene relevance, and obtains the scene relevance of the first data according to the first scene relevance and the second scene relevance.
  • the acquisition of the first scene relevance and the second scene relevance can be performed simultaneously or successively, and this application does not make specific limitations.
  • the acquisition strategy may include target sample features.
  • the acquisition strategy may include texture features of the "fire hydrant" sample, where texture features are used to describe the surface properties of the target in the image, such as the coarseness and density of the image texture.
  • texture features include gray-level co-occurrence matrix features, autoregressive texture model features, Tamura texture features, wavelet transform features, etc., which are not specifically limited in this application.
  • the evaluation unit 203 can determine the scene relevance of the first data according to the size of the similarity between the features of the first data and the target sample features. For example, the greater the similarity, the higher the scene relevance, and the lower the similarity, the lower the scene relevance. Finally, the second scene relevance of the first data is determined according to the similarity, and the first scene relevance of the first data is determined according to the output value of the evaluation model, and then the scene relevance of the first data is obtained according to the first scene relevance and the second scene relevance.
  • the evaluation unit 203 can input the first data into the evaluation model to obtain the scene relevance of the first data when it is determined that the similarity between the features of the first data and the target sample features is higher than the threshold standard, wherein the above-mentioned threshold standard can be carried in the collection strategy input by the user, or it can be a threshold standard preset by the data acquisition device 200, and this application does not make specific limitations.
  • the threshold standard can be carried in the collection strategy input by the user, or it can be a threshold standard preset by the data acquisition device 200, and this application does not make specific limitations.
  • the collection strategy may include a target collection condition, where the scene relevance of the data collected under the target collection condition is high, and the scene relevance of the data collected under the non-target collection condition is low, or the scene relevance of the data collected under the non-target collection condition is not evaluated, such as the scene relevance is 0, and only the data collected under the target collection condition is input into the evaluation model for scene relevance evaluation. For example, if the user needs to collect samples collected by the target sensor when the ambient temperature is higher than 40 degrees Celsius, then the scene relevance evaluation is not performed for the samples collected by the target sensor when the ambient temperature is lower than 40 degrees Celsius.
  • the collection strategy may include a target indicator, and the data scene relevance that meets the target indicator is high, and the data scene relevance that does not meet the target indicator is low, or the data that does not meet the target indicator is not evaluated for scene relevance, and only the data that meets the target indicator is input into the evaluation model for scene relevance evaluation. For example, if the user needs a sample with a confidence level higher than a threshold value X, then the scene relevance evaluation is not performed for the data with a confidence level lower than the threshold value X. It should be understood that the above examples are for illustration purposes and are not specifically limited in this application.
  • the collection strategy may include priority information, wherein the scene relevance of data with high priority is higher than that of data with low priority.
  • data with high scene relevance is uploaded first than data with low scene relevance.
  • data with low priority can be collected after data with high priority is collected.
  • the priority of difficult samples in the collection strategy issued by the user is higher than that of non-difficult samples.
  • difficult samples can be uploaded first, and then non-difficult samples can be uploaded.
  • This application does not make specific restrictions. For another example, if the recognition accuracy of the business model currently trained by the user is poor, it is necessary to collect samples with rich elements first.
  • the priority of data with rich elements is higher than that of data with single elements, and the scene relevance of data with rich elements is higher than that of data with single elements.
  • data with rich elements refers to the number of targets in the data collected by the target sensor being higher than the threshold.
  • the target here can be the target required by the user, or it can be a target not required by the user, it can be an identified target, or it can be an unknown target.
  • the target sensor includes a laser sensor and an image sensor. In the first data collected by the target sensor, the image sensor collects and identifies object A in the scene, and the laser sensor detects obstacles A, B, and C in the scene.
  • object A is a known target
  • objects B and C are unknown targets.
  • the first data is element-rich data.
  • the collection strategy may include upload logic, and the upload logic may include real-time upload, upload when resource usage is low, scheduled upload, periodic interval upload, etc., which are not specifically limited in this application.
  • the collection strategy that the user can input may include real-time upload.
  • the collection strategy that the user can input may include upload when resource usage is low.
  • the data collection device 200 can obtain local resource status, including memory, CPU, network usage, etc., and package and upload when the resource status is reduced. It should be understood that the above upload logic is used for illustration. In different application scenarios, users can flexibly set the upload logic required by the scenario, and examples are not given here one by one.
  • the user's collection strategy can be sent to the evaluation unit 203 in the form of an instruction, and the instruction may include multiple parameters, one parameter corresponding to an upload strategy, such as parameter 1 corresponding to the upload logic, parameter 2 corresponding to the priority information, parameter 3 corresponding to the target indicator, parameter 4 corresponding to the target collection condition, parameter 5 corresponding to the target sample feature, etc., which are not illustrated one by one here.
  • the evaluation unit can determine the collection strategy issued by the user according to the parameters in the instruction.
  • the upload logic can be determined according to the value of parameter 1, such as uploading when the resource occupancy is low, and the target sample feature can be determined according to the value of parameter 5, such as the texture feature of "fire hydrant", and then the scene relevance of each data in the multiple data is determined according to the collection strategy and the evaluation model.
  • parameter 1 such as uploading when the resource occupancy is low
  • parameter 5 such as the texture feature of "fire hydrant”
  • the scene relevance of each data in the multiple data is determined according to the collection strategy and the evaluation model.
  • the data collection device 200 may determine the scene relevance of each data based on the evaluation model.
  • the data collection device 200 receives the collection strategy input by the user, it determines the scene relevance of each data based on the collection strategy and the evaluation model. This application does not specifically limit this.
  • the uploading unit 204 may determine a sample set according to the scene relevance of each data in the plurality of data, and upload the sample set to the server 300 .
  • the sample set may include data with a scene relevance higher than a threshold, and the uploading unit 204 uploads the data with a scene relevance higher than the threshold to the server 300.
  • the data in the sample set is obtained after the uploading unit 204 sorts the data according to the scene relevance of the data, and the upload order of the data with high scene relevance is earlier than the data with low scene relevance, which can be specifically determined according to the user's collection strategy. For example, if the user's collection strategy does not set priority information, the data with a scene relevance higher than the threshold can be uniformly uploaded to the server 300 at this time. If the user's collection strategy is set with priority information, the data can be sorted according to the scene relevance of the data, and the data with high scene relevance is uploaded to the server 300 first. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the uploading unit 204 may first store the sample set in a memory of the data acquisition device 200, which establishes a communication connection with the server 300, so that the server 300 can obtain the sample set in the memory based on the user's reporting policy, wherein the above-mentioned memory may be a removable storage medium, such as a U disk, a mobile hard disk, a floppy disk, a CD, a memory card, etc. of the data acquisition device 200, which is not specifically limited in this application.
  • the uploading unit 204 may also label the sample set to obtain a label for each data in the sample set.
  • the label may be a manually labeled label or a machine labeled label, which may be determined according to the processing capability of the data acquisition device 200 and is not specifically limited in this application.
  • the uploading unit 204 can display the scene relevance of each data to the user through the client 100.
  • the user can select the data in the sample set according to business needs, and then upload the sample set to the server 300. This application does not make specific limitations.
  • the model training unit 301 of the server 300 can receive the sample set reported by the data acquisition device 200.
  • the business model is trained using the sample set to obtain a training result.
  • the business model is the model that the user is training, and the model can be a machine learning model, specifically, one or more of an artificial neural network model, a Bayesian network model, a decision tree model, a vector machine model, a random forest model, and an association rule model, and can also be other types of machine learning models, which are not illustrated one by one here.
  • the uploading unit 204 does not label the sample set, and the model training unit 301 may also label the sample set.
  • it may be manual labeling or machine labeling, which is not specifically limited in this application.
  • the strategy updating unit 302 of the server 300 may determine an updated collection strategy according to the training result of the service model.
  • the business model is a model that the user is training.
  • the training process of the model may include multiple training stages.
  • the sample collection strategies required for each training stage may be different.
  • the strategy update unit 302 may store the collection strategies required for each training stage of the business model, determine the collection strategies required for the next training stage based on the current training results of the business model, and obtain an updated collection strategy.
  • the strategy update unit 302 can obtain an updated collection strategy based on the collection strategy of the incremental learning stage, and send the updated collection strategy to the data acquisition device 200, so that the data acquisition device 200 can perform scene relevance evaluation on the new data collected by the target sensor according to the updated collection strategy.
  • the updated collection strategy may include priority information, and the priority information includes information on the priority collection of difficult samples.
  • a new acquisition strategy can also be obtained based on the training results of the business model and the data dimension of the current sample, wherein the new acquisition strategy is used to obtain associated data, and the data dimension of the associated data is different from the data dimension of the current sample, such as the current sample is unimodal data, and the associated data is multimodal data.
  • the data dimension of the current sample is unimodal data, such as only video frames collected by a camera
  • the business model is a model for identifying fire hydrants.
  • the training results of the model show that the accuracy of the model prediction is low. At this time, more samples are needed.
  • the strategy update unit 302 can obtain associated data based on this, such as samples collected by laser and samples collected by radar. Associated samples can improve the prediction ability of the model. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the strategy updating unit 302 can display the training results of the business model to the user and receive the updated collection strategy input by the user.
  • the user can also send a new collection strategy to the data collection device 200 through the strategy issuing unit 102 of the client 100, which is not specifically limited in this application.
  • the user believes that the business model has a high ability to identify samples of category A, but a poor ability to identify samples of category B, based on the training results of the business model.
  • the current business scenario requires the business model to have the ability to identify samples of category B, and the user hopes to obtain more samples of category B to further train the business model.
  • the user can input a new collection strategy through the client 100.
  • the new collection strategy includes the target sample features, that is, the texture features of the samples of category B, and may also include the priority
  • the client 100 can then send the new collection strategy to the data collection device 200 through the strategy issuing unit 102, so that it can preferentially collect data of category B to the server 300, and the server 300 can obtain samples of category B after marking the data of category B.
  • the strategy updating unit 302 in the server 300 which is not specifically limited in this application.
  • the present application provides a data acquisition system, in which a data acquisition device can obtain scene information input by a user, wherein the scene information includes information about a business scenario corresponding to a model that the user needs to train, and then determine a target sensor in at least one sensor based on the scene information, use the target sensor to perform data acquisition to obtain multiple data, and then determine the scene relevance of each of the multiple data based on the scene information, and send a sample set for training the above-mentioned model to the user, wherein the sample set is determined based on the scene relevance of each data, so that the data in the sample set is data related to the business scenario, and uploading the data in the sample set to the server can avoid uploading data irrelevant to the business scenario to the server, thereby reducing the pressure of data transmission and storage and improving model training efficiency.
  • FIG2 is a flow chart of a data acquisition method provided by the present application. The method can be applied to the data acquisition system shown in FIG1 . As shown in FIG2 , the method can include the following steps.
  • Step S201 the data collection device 200 reports sensor information to the client 100.
  • This step can be implemented by the reporting capability unit 201 in the embodiment of Figure 1.
  • the description of the data collection device 200 and the client 100 can refer to the embodiment of Figure 1, and will not be repeated here.
  • the sensor information may include parameter information of the sensor in the data acquisition device 200, such as resolution, sensitivity, linear range, stability, accuracy, frequency response characteristics, sampling frequency, etc., which is not specifically limited in this application. It should be understood that the types of parameter information of different sensors may be different, which is not specifically limited in this application.
  • the sensor information may also include other relevant information for confirming the business scenario.
  • the sensor information may also include the device information of the data acquisition device 200, such as the device model, device function, resource status, device status, etc. of the data acquisition device 200, wherein the resource status may include but is not limited to memory, CPU, network occupancy, etc., and the device status may include but is not limited to whether the sensor of the data acquisition device 200 is damaged, whether the data acquisition device 200 is in an abnormal state, etc.
  • the sensor information may also include the status information of the communication network between the data acquisition device 200 and the client 100, such as bandwidth, network speed, etc. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the data acquisition device 200 may report sensor information to the client 100 after establishing a communication connection with the client 100 for the first time, or report sensor information to the client 100 after receiving a reporting request initiated by the client 100, or report the updated sensor information to the client after the sensor information of the data acquisition device 200 is updated.
  • This application does not make specific limitations.
  • Step S202 The client 100 sends the scene information input by the user to the data collection device 200. This step can be implemented by the scene confirmation unit 101 in the embodiment of FIG1.
  • the scenario information may be information about the business scenario required by the user, such as the name, number, abbreviation, etc. of the business scenario, which is not specifically limited in this application.
  • the business scenario required by the user may include, but is not limited to, image recognition, voice recognition, text recognition, image detection, etc., which is not specifically limited in this application.
  • the user can input information about the business scenario required by the user based on the sensor information displayed by the client 100. If the client 100 displays that the data acquisition device 200 does not have the sensor information required for the business scenario, the user can select other data acquisition devices 200 that can meet the needs of the business scenario, thereby avoiding the data acquisition device 200 being unable to collect data related to the user's business scenario, thereby reducing the efficiency of model training.
  • the client 100 can determine one or more scenarios that can be realized by the sensor information based on the sensor information, and then obtain the scene information selected by the user after displaying the above one or more scenarios to the user.
  • the user can also input the scene information by himself based on the sensor information displayed by the client 100, which is not specifically limited in this application.
  • the client 100 can display multiple scene information to the user for selection through a visual interface, or input the scene information through an API, which is not specifically limited in this application.
  • Step S203 the data acquisition device 200 determines the target sensor according to the scene information. This step can be implemented by the acquisition unit 202 in the embodiment of Fig. 1.
  • the number of the target sensor can be one or more.
  • the data acquisition device 200 may store the correspondence between scene information and target sensors, such as image recognition scenes corresponding to image sensors, and speech recognition scenes corresponding to microphones, acoustic sensors, etc. This application does not make specific limitations.
  • the multiple target sensors corresponding to the scene information include not only the target sensors required by the business directly, but also the target sensors required by the business expansion.
  • the data collected by the target sensors required by the business expansion can provide more data dimensions to supplement the data collected by the target sensors required by the business directly.
  • the target sensor required by the business directly is the camera, but the number of samples of the associated modalities collected by other sensors such as radar sensors, infrared sensors, and laser sensors is It may also provide additional data dimensions in the image recognition scenario.
  • These sensors may be target sensors for business expansion needs in the business scenario.
  • the target sensors in the image recognition scenario include not only cameras, but also radar sensors, infrared sensors, laser sensors, etc. It should be understood that the above examples are used to illustrate that in different business scenarios, the target sensors for direct business needs and the target sensors for business expansion needs are different, and examples are not given one by one here.
  • the target sensor is determined based on the scene information confirmed by the user, and multiple data are collected using the target sensor.
  • the multiple data provided to the user are data related to the business scenario confirmed by the user, thereby avoiding providing the user with data irrelevant to the business scenario, improving communication efficiency, reducing the sample storage pressure of the server 300, and avoiding waste of resources.
  • the multiple data collected using the target sensors may include multiple data collected by multiple target sensors at the same time.
  • the multiple data may be obtained after performing data alignment operation on the data collected by the multiple target sensors. It should be understood that since the acquisition frequencies of different target sensors are different, the data collected by the multiple target sensors may be subjected to data alignment processing to obtain the multiple data.
  • a data set and a corresponding search index can be established for multiple data collected by multiple target sensors within a cycle time and the collection time, and the data collected at the same time can be quickly located and filtered based on the index, so as to obtain multiple aligned data. It should be understood that when aligning the data collected by multiple target sensors, data compensation can also be appropriately performed to obtain multiple aligned data, which is not specifically limited in this application.
  • each of the above multiple data may include data collected by multiple sensors at the same time, such as a radar frame and an image frame at the same time can be regarded as one data.
  • one data corresponds to data collected by one sensor, such as an image frame, a text, etc., which is not limited in this application.
  • Step S204 the data collection device 200 uses the target sensor to collect multiple data. This step can be implemented by the collection unit 202 in the embodiment of FIG1 .
  • Step S205 The client 100 sends the collection strategy input by the user to the data collection device 200. This step can be implemented by the strategy issuing unit 102 in the embodiment of FIG1.
  • the acquisition strategy may include target sample features.
  • the acquisition strategy may include the texture features of the "fire hydrant" sample, where the texture features are used to describe the surface properties of the target in the image, such as the coarseness, density, and other features of the image texture.
  • texture features include grayscale co-occurrence matrix features, autoregressive texture model features, Tamura texture features, wavelet transform features, etc., which are not specifically limited in this application. Among them, the more similar the sample features of the data are to the target sample features, the higher the scene relevance of the data.
  • the collection strategy may include a target collection condition, where the scene relevance of the data collected under the target collection condition is high, and the scene relevance of the data collected under the non-target collection condition is low, or the scene relevance of the data collected under the non-target collection condition is not evaluated, such as the scene relevance is 0, and only the data collected under the target collection condition is input into the evaluation model for scene relevance evaluation. For example, if the user needs to collect samples collected by the target sensor when the ambient temperature is higher than 40 degrees Celsius, then the scene relevance evaluation is not performed for the samples collected by the target sensor when the ambient temperature is lower than 40 degrees Celsius.
  • the collection strategy may include a target indicator, and the data scene relevance that meets the target indicator is high, and the data scene relevance that does not meet the target indicator is low, or the data that does not meet the target indicator is not evaluated for scene relevance, and only the data that meets the target indicator is input into the evaluation model for scene relevance evaluation. For example, if the user needs a sample with a confidence level higher than a threshold value X, then the scene relevance evaluation is not performed for the data with a confidence level lower than the threshold value X. It should be understood that the above examples are for illustration purposes and are not specifically limited in this application.
  • the collection strategy may include priority information, wherein the scene relevance of data with high priority is higher than that of data with low priority.
  • data with high scene relevance is uploaded first than data with low scene relevance.
  • data with low priority can be collected after data with high priority is collected.
  • the priority of difficult samples in the collection strategy issued by the user is higher than that of non-difficult samples.
  • difficult samples can be uploaded first, and then non-difficult samples can be uploaded.
  • This application does not make specific restrictions.
  • the recognition accuracy of the business model currently trained by the user is poor, and it is necessary to collect samples with rich elements first.
  • the priority of data with rich elements is higher than that of data with single elements, and the scene relevance of data with rich elements is higher than that of data with single elements.
  • data with rich elements refers to the number of targets in the data collected by the target sensor being higher than the threshold.
  • the target here can be the target required by the user, or it can be a target not required by the user, it can be an identified target, or it can be an unknown target.
  • the target sensor includes a laser sensor and an image sensor. In the first data collected by the target sensor, the image sensor collects and identifies object A in the scene, and the laser sensor detects obstacles A, B, and C in the scene.
  • object A is a known target
  • objects B and C are unknown targets.
  • the first data is element-rich data.
  • the collection strategy may include upload logic, and the upload logic may include real-time upload, upload when resource usage is low, scheduled upload, periodic interval upload, etc., which are not specifically limited in this application.
  • the collection strategy that the user can input may include real-time upload.
  • the collection strategy that the user can input may include upload when resource usage is low.
  • the data collection device 200 can obtain local resource status, including memory, CPU, network usage, etc., and package and upload when the resource status is reduced. It should be understood that the above upload logic is used for illustration. In different application scenarios, users can flexibly set the upload logic required by the scenario, and examples are not given here one by one.
  • the user's collection strategy can be sent to the data collection device 200 in the form of an instruction.
  • the instruction may include multiple parameters, one parameter corresponding to an upload strategy, such as parameter 1 corresponding to the upload logic, parameter 2 corresponding to the priority information, parameter 3 corresponding to the target indicator, parameter 4 corresponding to the target collection condition, parameter 5 corresponding to the target sample feature, etc., which are not illustrated one by one here.
  • the evaluation unit can determine the collection strategy sent by the user according to the parameters in the instruction.
  • the upload logic can be determined according to the value of parameter 1, such as uploading when the resource occupancy is low, and the target sample feature can be determined according to the value of parameter 5, such as the texture feature of "fire hydrant", and then the scene relevance of each data in the multiple data is determined according to the collection strategy and the evaluation model.
  • parameter 1 such as uploading when the resource occupancy is low
  • parameter 5 such as the texture feature of "fire hydrant”
  • the scene relevance of each data in the multiple data is determined according to the collection strategy and the evaluation model.
  • step S205 can be omitted, and the data collection device 200 can determine the scene relevance of each data based on the evaluation model.
  • the data collection device 200 receives the collection strategy input by the user, it determines the scene relevance of each data in combination with the collection strategy and the evaluation model. This application does not make specific limitations on this.
  • Step S206 the data collection device 200 determines the scene relevance of each data. This step can be implemented by the evaluation unit 203 in the embodiment of FIG1 .
  • the scenario relevance of the data is determined based on the degree of correlation between the data and the business scenario described by the scenario information. The higher the degree of correlation, the higher the scenario relevance of the data, and vice versa.
  • the scene relevance of the data is determined based on whether the data contains a target.
  • the scene relevance of data containing a target is higher than the scene relevance of data not containing a target.
  • the target may be a target required for a business scenario.
  • the target required for the business scenario may be a pedestrian; for example, if the business scenario is vehicle recognition, the target required for the business scenario may be a vehicle.
  • the above examples are for illustration only and are not specifically limited in this application.
  • the scene relevance of the data is determined based on the number of targets contained in the data. The higher the number of targets contained in the data, the higher the scene relevance of the data, and vice versa. For example, when the business scenario is vehicle identification, the scene relevance of image data A containing two vehicles is higher than the scene relevance of image data B containing one vehicle.
  • the business scenario is vehicle identification
  • the scene relevance of image data A containing two vehicles is higher than the scene relevance of image data B containing one vehicle.
  • the scene relevance of the data is determined based on the degree of contribution of the data to model training. For example, the worse the data label confidence, the worse the sample clarity, and the worse the training effect, the lower the scene relevance of the data, and vice versa.
  • the target border of data C is blurred, or the border position is wrong and does not completely frame the target, while the target border of data D completely and clearly frames the target, then the scene relevance of data C is lower than the scene relevance of data D.
  • the scene relevance of the data is determined according to the device state when the data acquisition device 200 collects data.
  • the data acquisition device 200 is a park robot.
  • the park robot is in an abnormal posture, such as when the park robot falls or overturns, the scene relevance of the data E collected by it is lower than the scene relevance of the data F collected in a normal posture.
  • an abnormal posture such as when the park robot falls or overturns
  • the scene relevance of the data E collected by it is lower than the scene relevance of the data F collected in a normal posture.
  • the data collection device 200 can perform the following steps on any first data among the multiple data: when the first data meets the collection strategy, the first data is input into the evaluation model to obtain the scene relevance of the first data, or the first data is input into the evaluation model to obtain the first scene relevance, the second scene relevance is obtained according to the collection strategy, and the scene relevance of the first data is obtained according to the first scene relevance and the second scene relevance.
  • the acquisition of the first scene relevance and the second scene relevance can be carried out simultaneously or successively, which is not specifically limited in this application.
  • the evaluation model may be obtained by training a neural network using an evaluation sample set, wherein the evaluation sample set includes evaluation samples and labels corresponding to the evaluation samples, the evaluation samples include known data, and the labels corresponding to the evaluation samples include the scene relevance corresponding to the known data.
  • the labels of the evaluation samples may be determined according to evaluation criteria, and the evaluation criteria may include but are not limited to one or more of whether the evaluation sample contains a target, the number of targets contained, and the sample confidence, and may also include other evaluation criteria, which are not given examples one by one here.
  • the data collection device 200 can use a universal evaluation model to determine the scene relevance corresponding to each data, or determine the evaluation model corresponding to the business scene according to the scene information selected by the user. It should be understood that the evaluation criteria for the scene relevance of data in different business scenes may be different. Therefore, the evaluation model corresponding to the business scene can be used to evaluate the scene relevance of data, thereby improving the accuracy of data collection. For example, in a target recognition scenario, the more targets there are in a sample, the higher the sample scene relevance; in a target detection scenario, the higher the confidence of the target bounding box, the higher the sample scene relevance. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the above evaluation criteria may also include the device status of the data acquisition device 200.
  • the device status When the device status is poor, the scene relevance of the sample is low.
  • the device status may include but is not limited to whether the sensor of the data acquisition device 200 is damaged, whether the data acquisition device 200 is in an abnormal state, etc.
  • the abnormal state may include a serious deviation in the movement route of the campus robot.
  • Step S207 the data collection device 200 determines a sample set according to the scene relevance of each data. This step can be implemented by the uploading unit 204 in the embodiment of FIG1 .
  • the sample set may include data with a scene relevance higher than a threshold value, and the data collection device 200 uploads the data with a scene relevance higher than the threshold value to the server 300.
  • the data in the sample set is obtained by the data collection device 200 after sorting the data according to the scene relevance of the data.
  • the upload order of data with high scene relevance is earlier than that of data with low scene relevance, which can be specifically determined according to the user's collection strategy. For example, if the user's collection strategy does not set priority information, the data with a scene relevance higher than the threshold value can be uniformly uploaded to the server 300 at this time.
  • the data can be sorted according to the scene relevance of the data at this time, and the data with high scene relevance is preferentially uploaded to the server 300. It should be understood that the above examples are for illustration and are not specifically limited in this application.
  • the data acquisition device 200 may first store the sample set in a memory of the data acquisition device 200, and the memory establishes a communication connection with the server 300, so that the server 300 can obtain the sample set in the memory based on the user's reporting policy, wherein the above-mentioned memory may be a removable storage medium, such as a U disk, a mobile hard disk, a floppy disk, a CD, a memory card, etc. of the data acquisition device 200, which is not specifically limited in this application.
  • the data collection device 200 may also label the sample set to obtain a label for each data in the sample set.
  • the label may be a manually labeled label or a machine labeled label, which may be determined according to the processing capability of the data collection device 200 and is not specifically limited in this application.
  • the scene relevance of each data can be displayed to the user through the client 100.
  • the user can select data in the sample set according to business needs, and then upload the sample set to the server 300.
  • This application does not make specific limitations.
  • Step S208 the data collection device 200 uploads the sample set to the server 300. This step can be implemented by the uploading unit 204 in the embodiment of FIG1.
  • Step S209 the server 300 uses the sample set to train the model and obtain a training result. This step can be implemented by the model training unit 301 in the embodiment of FIG1 .
  • the server 300 may also label the sample set. In specific implementations, it may be manual labeling or machine labeling, which is not specifically limited in this application.
  • the server 300 can use the sample set to train the business model and obtain training results.
  • the business model is the model that the user is training, and the model can be a machine learning model, specifically, one or more of an artificial neural network model, a Bayesian network model, a decision tree model, a vector machine model, a random forest model, and an association rule model, and can also be other types of machine learning models, which are not illustrated one by one here.
  • Step S210 The server 300 determines a new acquisition strategy according to the training result. This step can be implemented by the strategy updating unit 302 in the embodiment of FIG1 .
  • the business model is a model that the user is training.
  • the training process of the model may include multiple training stages.
  • the sample collection strategies required for each training stage may be different.
  • the server 300 may store the collection strategies required for each training stage of the business model, determine the collection strategies required for the next training stage based on the current training results of the business model, and obtain an updated collection strategy.
  • the server 300 can obtain an updated collection strategy based on the collection strategy of the incremental learning stage, and send the updated collection strategy to the data acquisition device 200, so that the data acquisition device 200 can perform scene relevance evaluation on the new data collected by the target sensor according to the updated collection strategy.
  • the updated collection strategy may include priority information, and the priority information includes information on the priority collection of difficult samples.
  • a new collection strategy can also be obtained based on the training results of the business model and the data dimension of the current sample, wherein the new collection strategy is used to obtain associated data, the data dimension of the associated data is different from the data dimension of the current sample, for example, the current sample is a single model
  • the data dimension of the current sample is unimodal data, and the associated data is multimodal data.
  • the data dimension of the current sample is unimodal data, such as only video frames collected by a camera
  • the business model is a model for identifying fire hydrants.
  • the training results of the model show that the accuracy of the model prediction is low. At this time, more samples are needed.
  • the server 300 can obtain associated data based on this, such as samples collected by lasers and samples collected by radars. Associated samples can improve the prediction ability of the model. It should be understood that the above examples are for illustration only and are not specifically limited in this application.
  • Step S211 the server 300 sends a new collection strategy to the data collection device 200. This step can be implemented by the strategy updating unit 302 in the embodiment of FIG1.
  • Step S212 the client 100 sends the new collection strategy input by the user to the data collection device 200.
  • the server 300 can display the training results of the business model to the user and receive the updated collection strategy input by the user.
  • the user can also send a new collection strategy to the data collection device 200 through the strategy issuing unit 102 of the client 100, which is not specifically limited in this application.
  • the user believes that the business model has a high ability to identify samples of category A, but a poor ability to identify samples of category B, based on the training results of the business model.
  • the current business scenario just requires the business model to have the ability to identify samples of category B, and the user hopes to obtain more samples of category B to further train the business model.
  • the user can input a new acquisition strategy through the client 100.
  • the new acquisition strategy includes target sample features, i.e., texture features of category B samples, and may also include priority information, i.e., information on the priority collection of category B samples.
  • the client 100 sends the above new acquisition strategy to the data collection device 200 through the strategy issuing unit 102, so that it can preferentially collect category B data to the server 300.
  • the server 300 can obtain category B samples after marking the category B data. It should be understood that the above example is for illustration, and the user can also input a new acquisition strategy through the strategy updating unit 302 in the server 300, which is not specifically limited in this application.
  • the data acquisition device 200 is a park robot
  • the client 100 is the user's personal computer
  • the server 300 is a cloud server in the public cloud.
  • the user obtains the permission to remotely control the server 300 after purchasing the cloud service through the public cloud console.
  • the park robot when the park robot is performing inspection tasks, the user, who is a park property management personnel, has the following business needs: the user hopes that the park robot has AI perception skills, so that the park robot can perceive various entities in the park environment when patrolling in the park.
  • the AI perception skills here can be a deep learning model, which can perform incremental learning based on various samples collected by the park robot.
  • the data acquisition device 200 executes step S201 to report the sensor information to the client 100.
  • the client 100 can display a user interface as shown in FIG3 to the user.
  • FIG3 is an example diagram of a configuration page provided by the present application. It should be understood that FIG3 is used for illustration, and the present application does not specifically limit the user interface.
  • the configuration page 310 shown in FIG3 may include a sensor information display area 320, a scene information input area 330, and a control 340.
  • the sensor information display area 320 is used to display sensor information.
  • the sensor information may include the names and parameter information of all sensors owned by the data acquisition device 200 (that is, the campus robot in this application scenario).
  • the sensors of the campus robot may include: image sensor 1, image sensor 2, laser sensor 1, radar sensor 1, etc., wherein the parameter information of the image sensor 1 includes pixels, frame rate, signal-to-noise ratio, sensitivity, etc.
  • FIG3 exemplarily gives the sensor names and parameter information that the data acquisition device 200 may have.
  • the sensor information reported by the data acquisition device 200 may also include more types of sensors and richer parameters, which are not illustrated one by one here.
  • the client 100 may execute step S202 to receive the scene information input by the user and send it to the data acquisition device 200.
  • the data acquisition device 200 may execute step S203 to determine the target sensor corresponding to the scene information.
  • the scene information input area 330 is used for the user to input the scene information.
  • the user may input the scene information through the user interface shown in FIG3 . Assuming that the scene information selected by the user is the "target detection" scene, after the data acquisition device 200 receives the scene information fed back by the client 100 , it may determine one or more target sensors corresponding to the "target detection" scene according to the correspondence between the pre-stored scene information and the target sensor.
  • the client 100 may display to the user that the target sensor corresponding to the "target detection" scene is image sensor 1, image sensor 2, laser sensor 1, etc.
  • the data acquisition device 200 determines the target sensor, it may not feed it back to the user through the client 100, and this application does not limit this.
  • FIG. 3 exemplarily provides several optional scene information, such as voice recognition, text recognition, etc.
  • the client 100 can provide the user with more types of scene information, which are not illustrated one by one here.
  • users can also upload scene information by themselves, such as clicking the "Custom scene information” control in Figure 3 to enter custom scene information, and can also customize the target sensor corresponding to the scene information uploaded by themselves, such as clicking the "Custom target sensor” control in Figure 3 to enter the target sensor corresponding to the custom scene information.
  • users can input scene information through the API and target sensor, which is not specifically limited in this application.
  • the user can click the control 340 to save it, and the client 100 can send the scene information input by the user to the data acquisition device 200.
  • the data acquisition device 200 can execute step S204, and use the target sensor to collect multiple data.
  • the data acquisition device 200 can use image sensor 1, image sensor 2, and laser sensor to collect multiple data, and then align the multiple data collected by image sensor 1, image sensor 2, and laser sensor through data alignment operation to obtain multimodal data at the same time and in the same scene, one modality corresponds to one sensor, and the data at the same time and in the same scene are summarized into a set, and indexes are established for them.
  • step S204 in the above content, and it will not be repeated here.
  • the data acquisition device 200 can execute step S206 to determine the scene relevance of each data according to the scene information and the multiple data collected by the target sensor.
  • the data acquisition device can first determine the corresponding evaluation model according to the scene information, and then input the multiple data into the evaluation model to obtain the scene relevance of each data.
  • the evaluation model corresponding to the scene information is obtained by training the machine learning model using the evaluation sample set.
  • the evaluation samples in the evaluation sample set and the corresponding labels are determined according to the evaluation criteria, which include but are not limited to whether the evaluation sample contains the target, the number of targets contained, the sample confidence, the status information of the data acquisition device, etc., and may also include other evaluation criteria.
  • the evaluation criteria include but are not limited to whether the evaluation sample contains the target, the number of targets contained, the sample confidence, the status information of the data acquisition device, etc., and may also include other evaluation criteria.
  • the evaluation criteria include but are not limited to whether the evaluation sample contains the target, the number of targets contained, the sample confidence, the status information of the data acquisition device, etc.
  • the evaluation criteria may include the confidence of the laser data collected by the laser sensor, the confidence of the image data collected by the video sensor, the number of targets contained in the laser data and the image data, the matching degree of the same target between the laser data and the image data, the fusion speed of the data acquisition device 200 and the abnormal state recognition results of the angular velocity of the inertial measurement unit (IMU), etc. It should be understood that the above evaluation criteria are used for illustration and are not specifically limited in this application.
  • the user may also input a collection strategy through the client 100 according to business needs.
  • the client 100 may execute step S205 to send the collection strategy input by the user to the data collection device 200, so that when the data collection device 200 executes step S206, it can integrate the collection strategy input by the user and the scene information input in step S202, and confirm the scene relevance of the data collected by the target sensor in multiple dimensions, so that the sample set determined in step S207 not only meets the user's business scenario, but also meets the user's business needs, thereby improving the user's usage experience.
  • FIG. 4 is an example diagram of another configuration page provided by the present application.
  • the configuration page 410 may include a collection strategy input area 420 and a control 430.
  • the client 100 may display multiple types of collection strategies available for selection to the user, such as target sample features, target collection conditions, priority information, upload logic, etc.
  • the client 100 may display a variety of selectable targets to the user, such as fire hydrants, bushes, electric meters, etc.
  • the client 100 may send it as a collection strategy to the data acquisition device 200 based on the sample features of the pre-stored fire hydrant, such as the texture features of the fire hydrant. For another example, after the user selects the priority information, the client 100 may provide the user with a variety of targets, and the user may set the priority level for the target according to the business needs. For example, in FIG. 4, the fire hydrant is set to priority level 1, indicating that the user hopes that the data acquisition device can upload the sample data of the fire hydrant first. For another example, the user can also choose an upload strategy, such as uploading during off-peak hours. The user can schedule uploading at off-peak times, such as 3 a.m. Beijing time, or when resource usage is low. It should be understood that Figure 4 is used for illustration and this application does not make any specific limitations.
  • the user can also customize the collection strategy, or customize each type of collection strategy, such as customizing the target sample characteristics, or customizing the priority information, or customize a new type of collection strategy, which is not specifically limited in this application.
  • the user can customize the collection strategy by writing an API. After the user selects or customizes the collection strategy, the collection strategy can be saved through the control 430, so that the client 100 sends the collection strategy to the data acquisition device 200.
  • the data acquisition device 200 can perform steps S206 and S207 according to the acquisition strategy and scene information to determine the scene relevance of each of the multiple data collected by the target sensor, thereby determining a sample set.
  • the data acquisition device can retrieve the collected laser data and image data according to the target sample feature of the "fire hydrant" in the acquisition strategy, that is, the laser texture feature and image texture feature of the fire hydrant, wherein the greater the similarity between the laser data and the laser texture feature, the higher the scene relevance of the laser data, and vice versa, the lower the scene relevance.
  • the greater the similarity between the image data and the image texture feature, the higher the scene relevance of the image data, and vice versa the lower the scene relevance.
  • the similarity here can refer to the feature matching degree.
  • the sample set is determined.
  • the sample set can be obtained by arranging the scene relevance of all data in order from large to small. For example, if the number of the sample set is 1 million data, then after sorting the data from large to small according to the scene relevance, the data from the 1st to the 1 millionth are used as the data in the sample set.
  • the above examples are for illustration, and the present application is not specifically limited.
  • the data collection device 200 may execute step S208 to upload the sample set, and according to the upload strategy in the collection strategy, ie, the off-peak upload strategy, upload the sample set to the server 300 at an off-peak time defined by the user, such as 3:00 Beijing time.
  • the upload strategy in the collection strategy ie, the off-peak upload strategy
  • the server uses the collection strategy of the target sample features (fire hydrants) specified by the user to train the model for a period of time.
  • the incremental learning has achieved the expected recognition effect on fire hydrants.
  • the user can issue a new collection strategy. For example, the user hopes that the model can recognize more types of samples.
  • the user can change the target sample features in the collection strategy to "shrubs" or "electricity meters”.
  • the client can execute step S212 to issue a new collection strategy to the data acquisition device 200.
  • the data acquisition device can determine a new sample set according to the new collection strategy, and upload the sample set that meets the user's new collection strategy to the server.
  • the above scheme can flexibly change the user's collection strategy, quickly match the sample set required by the user, minimize the resource waste caused by data collection and uploading, and at the same time ensure the training effect of the sample set required by the user and improve the user's experience.
  • the present application provides a data collection method, in which a data collection device can obtain scene information input by a user, wherein the scene information includes information about the business scenario corresponding to the model that the user needs to train, and then determine a target sensor in at least one sensor based on the scene information, use the target sensor to perform data collection to obtain multiple data, and then determine the scene relevance of each of the multiple data based on the scene information, and send a sample set for training the above-mentioned model to the user, wherein the sample set is determined based on the scene relevance of each data, so that the data in the sample set is data related to the business scenario, and uploading the data in the sample set to the server can avoid data irrelevant to the business scenario from being uploaded to the server, thereby reducing the pressure of data transmission and storage and improving model training efficiency.
  • FIG5 is a schematic diagram of the structure of a computing device provided in the present application.
  • the computing device 500 is the client 100, the data acquisition device 200 or the server 300 in the embodiments of FIG1 to FIG4.
  • the computing device 500 includes a processor 501, a storage unit 502, a storage medium 503 and a communication interface 504, wherein the processor 501, the storage unit 502, the storage medium 503 and the communication interface 504 communicate through a bus 505, and also communicate through other means such as wireless transmission.
  • the processor 501 is composed of at least one general-purpose processor, such as a CPU, an NPU, or a combination of a CPU and a hardware chip.
  • the hardware chip is an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof.
  • the PLD is a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.
  • the processor 501 executes various types of digital storage instructions, such as software or firmware programs stored in the storage unit 502, which enables the computing device 500 to provide a wide variety of services.
  • the processor 501 includes one or more CPUs, such as CPU0 and CPU1 shown in FIG. 5 .
  • the computing device 500 also includes multiple processors, such as the processor 501 and the processor 506 shown in FIG5 .
  • processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU).
  • the processor here refers to one or more devices, circuits, and/or processing cores for processing data (such as computer program instructions).
  • the storage unit 502 is used to store program codes, and is controlled by the processor 501 to execute the processing steps of the program running device, client or server in any of the embodiments in Figures 1 to 5.
  • the program code includes one or more software units.
  • the one or more software units are the scene confirmation unit and the policy delivery unit in the embodiment of FIG1, wherein the scene confirmation unit is used to obtain the scene information input by the user and send it to the data acquisition device 200, and the policy delivery unit is used to obtain the acquisition policy input by the user and send it to the data acquisition device 200.
  • the scene confirmation unit is used to execute step S202 and its optional steps in the embodiment of FIG2, and the policy delivery unit is used to execute step S205 and its optional steps in FIG2, which will not be described in detail here.
  • the one or more software units are the reporting capability unit, the collection unit, the evaluation unit, and the upload unit in the embodiment of FIG. 1, wherein the reporting capability unit is used to send the sensor information of the data collection device 200 to the client, the collection unit is used to determine the target sensor according to the scene information input by the user, and use the target sensor to collect multiple data, the evaluation unit is used to determine the scene relevance of multiple data according to the scene information and the collection strategy input by the user, and the upload unit is used to determine the sample set according to the scene relevance of multiple data, and upload the sample set to the server.
  • the reporting capability unit is used to execute step S201 and its optional steps in the embodiment of FIG.
  • the collection unit is used to execute step S203 and step S204 and its optional steps in the embodiment of FIG. 2
  • the evaluation unit is used to execute step S206 and its optional steps in FIG. 2
  • the upload unit is used to execute step S207 and step S208 and its optional steps in FIG. 2, which will not be repeated here.
  • the one or more software units are the model training unit and the strategy updating unit in the embodiment of FIG. 1 , wherein the model training unit is used to receive the sample set sent by the data acquisition device, use the sample set to train the model, and obtain the training result, and the strategy updating unit is used to determine the new acquisition strategy according to the training result, and send it to the data acquisition device to obtain a new sample, wherein the model training unit is used to perform step S209 and its optional steps in the embodiment of FIG. 2 , and the strategy updating unit is used to Execute step S210, step S211 and optional steps in FIG2 , which will not be described in detail here.
  • the storage unit 502 includes a read-only memory and a random access memory, and provides instructions and data to the processor 501.
  • the storage unit 502 also includes a non-volatile random access memory.
  • the storage unit 502 is a volatile memory or a non-volatile memory, or includes both volatile and non-volatile memories.
  • the non-volatile memory is a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.
  • the volatile memory is a random access memory (RAM), which is used as an external cache.
  • RAM synchronous RAM
  • DDR SDRAM double data rate SDRAM
  • ESDRAM enhanced SDRAM
  • SLDRAM synchronous link RAM
  • DR RAM direct RAM bus RAM
  • hard disk hard disk
  • USB universal serial bus
  • flash flash
  • SD card secure digital memory card
  • memory stick etc.
  • the hard disk is a hard disk drive (HDD), a solid state disk (SSD), a mechanical hard disk (HDD), etc., and this application does not make specific limitations.
  • the storage medium 503 is a carrier for storing data, such as a hard disk, a USB flash drive (universal serial bus), a flash memory, a secure digital memory card (SD card), a memory stick, etc.
  • the hard disk can be a hard disk drive (HDD), a solid state disk (SSD), a mechanical hard disk (HDD), etc., and this application does not make any specific limitations.
  • the communication interface 504 is a wired interface (such as an Ethernet interface), an internal interface (such as a high-speed serial computer expansion bus (Peripheral Component Interconnect express, PCIe) bus interface), a wired interface (such as an Ethernet interface) or a wireless interface (such as a cellular network interface or a wireless local area network interface) for communicating with other servers or units.
  • a wired interface such as an Ethernet interface
  • an internal interface such as a high-speed serial computer expansion bus (Peripheral Component Interconnect express, PCIe) bus interface
  • PCIe Peripheral Component Interconnect express
  • a wired interface such as an Ethernet interface
  • a wireless interface such as a cellular network interface or a wireless local area network interface
  • the bus 505 is a Peripheral Component Interconnect Express (PCIe) bus, an extended industry standard architecture (EISA) bus, a unified bus (Ubus or UB), a compute express link (CXL), a cache coherent interconnect for accelerators (CCIX), etc.
  • PCIe Peripheral Component Interconnect Express
  • EISA extended industry standard architecture
  • Ubus or UB unified bus
  • CXL compute express link
  • CCIX cache coherent interconnect for accelerators
  • the bus 505 is divided into an address bus, a data bus, a control bus, etc.
  • the bus 505 includes not only a data bus but also a power bus, a control bus, and a status signal bus, etc. However, for the sake of clarity, various buses are labeled as bus 505 in the figure.
  • FIG. 5 is only a possible implementation of the embodiment of the present application.
  • the computing device 500 also includes more or fewer components, which is not limited here.
  • the contents not shown or described in the embodiment of the present application refer to the relevant description in the embodiments of FIG. 1 to FIG. 4 above, which will not be repeated here.
  • the embodiment of the present application provides a computer cluster, including multiple computing devices 500, and there is a communication connection between the multiple computing devices.
  • each computing device 500 can be the client 100, data acquisition device 200 and server 300 in the above content.
  • the client 100, data acquisition device 200 and server 300 can be deployed on different computing devices in the computer cluster, or partially deployed on the same computing device and partially deployed on other computing devices, which is not specifically limited in this application.
  • An embodiment of the present application provides a computer storage medium, including: instructions are stored in the computer storage medium; when the instructions are executed on a computing device, the computing device executes the data collection method described in Figures 1 to 5 above.
  • the above embodiments are implemented in whole or in part by software, hardware, firmware or any other combination.
  • the above embodiments are implemented in whole or in part in the form of a computer program product.
  • the computer program product includes at least one computer instruction.
  • the computer program instruction is loaded or executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part.
  • the computer is a general-purpose computer, a special-purpose computer, a computer network, or other programming device.
  • the computer instructions are stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • the computer instructions are transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.
  • the computer-readable storage medium is any medium that can be accessed by a computer or a data storage node such as a server or data center that contains at least one medium set.
  • the medium is a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a high-density digital video disc (DVD), or a semiconductor medium.
  • the semiconductor medium is an SSD.

Abstract

本申请提供了一种数据采集方法、装置及相关设备，该方法包括以下步骤：数据采集装置获取用户输入的场景信息，其中，场景信息包括用户所需训练的模型对应的业务场景信息，根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，根据场景信息确定多个数据中每个数据的场景相关度，向用户发送用于训练模型的样本集，其中，样本集是根据每个数据的场景相关度确定的，使得样本集中的数据是与业务场景相关的数据，将样本集中的数据上传至服务器，可以避免与业务场景无关的数据被上传至服务器，从而减轻数据传输和存储的压力，提高模型训练效率。

Description

一种数据采集方法、装置及相关设备
本申请要求在2022年10月27日提交中国国家知识产权局、申请号为202211330232.4的中国专利申请的优先权，发明名称为“一种数据采集方法、装置及相关设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及人工智能(artificial intelligence,AI)领域，尤其涉及一种数据采集方法、装置及相关设备。
背景技术
随着科学技术的不断发展，AI模型在视频图像、语音识别、自然语言处理等相关领域得到了广泛应用。AI模型通常需要使用大量的样本对其进行训练，通常情况下，传感器会将采集到的原始数据统一上传至服务器中，再对服务器中的原始数据进行标注后获得样本才，然后使用样本训练AI模型。
但是，传感器采集到的原始数据可能会存在大量与业务场景无关的数据，这些数据也会被上传至服务器，造成数据传输和存储的压力，降低模型训练效率。
发明内容
本申请提供了一种数据采集方法、装置及相关设备，用于解决传感器采集到的原始数据与业务场景无关，导致数据传输和存储的压力大，模型训练效率低的问题。
第一方面，提供了一种数据采集方法，该方法包括以下步骤：数据采集装置获取用户输入的场景信息，其中，场景信息包括用户所需训练的模型对应的业务场景信息，根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，目标传感器的数量为一个或者多个，根据场景信息确定多个数据中每个数据的场景相关度，每个数据的场景相关度用于描述每个数据与业务场景之间的相关程度，向用户发送用于训练模型的样本集，其中，样本集是根据每个数据的场景相关度确定的。
上述实现方式，数据采集装置可以获取用户输入的场景信息，然后根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，再根据场景信息确定多个数据中每个数据的场景相关度，向用户发送用于训练上述模型的样本集，其中，样本集是根据每个数据的场景相关度确定的，使得样本集中的数据是与业务场景相关的数据，将样本集中的数据上传至服务器，可以避免与业务场景无关的数据被上传至服务器，从而减轻数据传输和存储的压力，提高模型训练效率。
在一可能的实现方式中，在数据采集装置获取用户输入的场景信息之前，该方法还包括以下步骤：数据采集装置向用户提供传感器信息，其中，传感器信息包括数据采集装置拥有的至少一个传感器的参数信息，传感器信息用于供用户确定场景信息。
可选地，传感器信息可包括数据采集装置中传感器的参数信息，比如分辨率、灵敏度、线性范围、稳定性、精度、频率响应特性、采样频率等等，本申请不作具体限定。应理解，不同传感器的参数信息类型可能不同，本申请不作具体限定。
可选地，传感器信息还可包括其他用于确认业务场景的相关信息，举例来说，传感器信息还可包括数据采集装置的设备信息，比如数据采集装置的设备型号、设备功能、资源状态、设备状态等等，其中，资源状态可包括但不限于内存资源状态、中央处理器(central processing unit,CPU)资源状态、网络占用等等，设备状态可包括但不限于数据采集装置的传感器是否发生损坏，数据采集装置是否处于异常状态等等。再举例来说，传感器信息还可包括数据采集装置和用户所持有的客户端之间的通信网络的状态信息，比如带宽、网速等等，应理解，上述举例用于说明，本申请不作具体限定。
具体实现中，数据采集装置可以在首次与用户所持有的客户端建立通信连接后，向客户端上报传感器信息，或者，在接收到客户端发起的上报请求之后，向客户端上报传感器信息，或者，在数据采集装置的传感器信息发生更新后，将更新后的传感器信息上报给客户端，本申请不作具体限定。
可选地，用户可以根据客户端显示的传感器信息，输入用户所需的业务场景的信息，如果客户端显示该数据采集装置没有业务场景所需的传感器信息，用户可以选择其他能够满足业务场景需求的其他数据采 集装置，从而避免数据采集装置无法采集到用户的业务场景相关的数据，降低模型训练效率。
上述实现方式，在用户输入场景信息之前，将数据采集装置的传感器信息上报给用户，使得用户可以根据数据采集装置的传感器信息确定输入的场景信息，避免出现数据采集装置无法满足用户需求这一情况的发生，提高用户的使用体验。
在一可能的实现方式中，场景信息可以是用户所需的业务场景的信息，比如业务场景的名称、编号、缩写等等，本申请不作具体限定。用户所需的业务场景可包括但不限于图像识别、语音识别、文字识别、图像检测等等，本申请不作具体限定。
具体实现中，客户端可以根据传感器信息确定该传感器信息可实现的一个或者多个场景，然后向用户展示的上述一个或者多个场景后，获取用户从中选择的场景信息。或者，用户也可以根据客户端展示的传感器信息，自行输入场景信息，本申请不作具体限定。具体实现中，客户端可以通过可视化界面向用户展示多个场景信息以供选择，也可以通过应用程序接口(application programming interface,API)输入场景信息，本申请不作具体限定。
上述实现方式，通过获取用户输入的场景信息，可以使得最后确定的样本集中的数据是与业务场景相关的数据，从而避免与业务场景无关的数据被上传至服务器，减轻数据传输和存储的压力，提高模型训练效率。
在一可能的实现方式中，数据采集装置可以存储有场景信息和目标传感器之间的对应关系，获取用户输入的场景信息之后，可以根据对应关系确定该场景信息对应的目标传感器，比如图像识别场景对应图像传感器，语音识别场景对应麦克风、声敏传感器等等，本申请不作具体限定。
需要说明的，根据场景信息对应的多个目标传感器中，不仅包括业务直接需求的目标传感器，还可包括业务扩展需求的目标传感器，其中，业务扩展需求的目标传感器所采集到的数据，能够为业务直接需求的目标传感器采集到的数据提供更多数据维度的补充。举例来说，图像识别场景中，业务直接需求的目标传感器是摄像头，但是雷达传感器、红外传感器、激光传感器等其他传感器所采集到的关联模态的样本数据也可能在该图像识别场景中提供数据维度的补充，这些传感器可以是该业务场景下存在业务扩展需求的目标传感器，那么图像识别场景下的目标传感器不仅包括摄像头，还可包括雷达传感器、红外传感器、激光传感器等，应理解，上述举例用于说明，在不同的业务场景中，业务直接需求的目标传感器和业务扩展需求的目标传感器不同，这里不一一举例说明。
上述实现方式，根据用户确认的场景信息确定目标传感器，使用目标传感器采集多个数据，这样提供给用户的多个数据是与用户所确认的业务场景相关的数据，从而避免向用户提供与业务场景无关的数据，提高通信效率，同时减轻样本存储压力，避免资源的浪费。
在一可能的实现方式中，在目标传感器的数量为多个时，使用目标传感器采集的多个数据可包括同一时刻下多个目标传感器采集的多个数据。具体地，可以对多个目标传感器采集到的数据进行数据对齐操作后，获得上述多个数据。应理解，由于不同目标传感器的采集频率存在差异，因此可以对多个目标传感器采集的数据进行数据对齐处理，获得上述多个数据。
具体实现中，可以为多个目标传感器在周期时间内采集到的多个数据以及采集时间建立数据集合和对应的查找索引，基于索引快速定位和筛选出同一时刻下采集的数据，从而获得对齐后的多个数据。应理解，对多个目标传感器采集到的数据进行对齐处理时，也可以适当进行数据补偿，以获得对齐后的多个数据，本申请不作具体限定。
需要说明的，对于多传感器融合场景来说，上述多个数据中的每个数据可以包括同一时刻下的多个传感器采集的数据，比如同一时刻下的雷达帧和图像帧可以作为一个数据。对于单一传感器场景来说，一个数据对应一个传感器采集的数据，比如一个图像帧，一个文本等等，本申请不作限定。
上述实现方式，通过将多个目标传感器采集的多个数据进行对齐操作，使得最后样本集中的数据是同一时刻下中多个目标传感器采集的数据，提高数据的准确性，避免由于数据误差导致模型训练效果差的问题。
在一可能的实现方式中，数据的场景相关度是根据数据与场景信息所描述的业务场景之间的相关程度确定的，相关程度越高，数据的场景相关度越高，反之则越低。
可选地，数据的场景相关度是根据数据是否包含目标确定的，包含目标的数据的场景相关度高于不包含目标的数据的场景相关度，该目标可以是业务场景所需的目标，比如业务场景是行人识别时，业务场景所需的目标可以是行人；比如业务场景是车辆识别，业务场景所需的目标可以是车辆，上述举例用于说明， 本申请不作具体限定。
可选地，数据的场景相关度是根据数据包含的目标数量确定的，数据包含的目标数量越高，数据的场景相关度越高，反之则越低，比如业务场景是车辆识别时，包含2个车辆的图像数据A的场景相关度高于包含1车辆的图像数据B的场景相关度，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度是根据数据对模型训练时的贡献程度决定的，比如数据的标签置信度越差、样本清晰度越差、训练效果越差的数据的场景相关度越底，反之则越高，举例来说，数据C的目标边框模糊不清，或者边框位置错误没有将目标完整框住，数据D的目标边框完整、清晰的框住了目标，那么数据C的场景相关度低于数据D的场景相关度，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度是根据数据采集装置200采集数据时的设备状态决定的，设备状态越差，数据采集装置所采集的数据的场景相关度越低，反之则越高。举例来说，数据采集装置200为园区机器人，园区机器人处于非正常姿态时，比如园区机器人摔倒、倾覆等状态时，其采集的数据E的场景相关度低于正常姿态下采集的数据F的场景相关度，上述举例用于说明，本申请不作具体限定。
可选地，可以通过评估模型确定每个数据的场景相关度，其中，评估模型可以是使用评估样本集对神经网络进行训练后获得的，其中，评估样本集包括评估样本和评估样本对应的标签，评估样本包括已知数据，评估样本对应的标签包括已知数据对应的场景相关度。其中，评估样本的标签可以是根据评估标准确定的，评估标准可包括但不限于评估样本是否包含目标、包含目标数量、样本置信度中的一个或者多个，还可以包括其他评估标准，这里不一一举例说明。
可选地，上述评估标准还可包括数据采集装置的设备状态，设备状态差时，样本的场景相关度较低，设备状态可包括但不限于数据采集装置的传感器是否发生损坏，数据采集装置是否处于异常状态等等。举例来说，数据采集装置是园区机器人，那么异常状态可以包括园区机器人的运动路线发生严重偏移。应理解，上述举例用于说明，本申请不作具体限定。
需要说明的，数据采集装置可以使用通用的评估模型确定每个数据对应的场景相关度，或者，根据用户选择的场景信息确定与业务场景对应的评估模型，应理解，不同的业务场景下数据的场景相关度的评估标准可能存在差异，因此可以使用业务场景对应的评估模型进行数据的场景相关度评估，从而提高数据的场景相关度的获取的准确性。举例来说，目标识别场景下，样本中目标数量越多，样本场景相关度越高，目标检测场景下，目标边界框的置信度越高，样本场景相关度越高，应理解，上述举例用于说明，本申请不作具体限定。
上述实现方式中，通过确定每个数据的场景相关度，并以此来确定样本集，可以使得样本集更加符合用户所需的业务场景，从而避免向用户提供与业务场景无关的数据，提高通信效率，同时减轻样本存储压力，避免资源的浪费。
在一可能的实现方式中，根据场景信息确定多个数据中每个数据的场景相关度之前，该方法还包括以下步骤：获取用户输入的采集策略，其中，采集策略包括模型训练时所需的样本的目标样本特征。
具体实现中，数据采集装置也可以结合客户下发的采集策略进一步确定数据的场景相关度，数据采集装置可以对多个数据中任意一个第一数据执行以下步骤：在第一数据符合采集策略的情况下，将第一数据输入评估模型获得第一数据的场景相关度，或者，将第一数据输入评估模型获得第一场景相关度，根据采集策略获得第二场景相关度，根据第一场景相关度和第二场景相关度获得第一数据的场景相关度。其中，第一场景相关度和第二场景相关度的获取可以是同时进行的，也可以是先后进行的，本申请不作具体限定。
举例来说，以目标检测场景为例，假设用户需要优先采集“消防栓”样本图像，那么采集策略可以包括“消防栓”样本的纹理特征，其中，纹理特征用于描述图像中目标的表面性质，例如图像纹理的粗细、稠密等特征。常见的纹理特征有灰度共生矩阵特征、自回归纹理模型特征、Tamura纹理特征、小波变换特征等，本申请不作具体限定。其中，数据的样本特征与目标样本特征越相似，该数据的场景相关度越高。
可选地，采集策略包括目标采集条件，其中，样本集中数据的是在目标采集条件下采集的数据，在目标采集条件下采集到的数据的场景相关度高，非目标采集条件下采集到的数据的场景相关度低，或者，非目标采集条件下采集到的数据不进行场景相关度评估，比如场景相关度为0，只将目标采集条件下采集到的数据输入评估模型进行场景相关度评估。举例来说，用户需求采集环境温度高于40摄氏度时目标传感器采集的样本，那么对于环境温度低于40摄氏度时目标传感器采集的样本不进行场景相关度评估。
可选地，采集策略可以包括目标指标，满足目标指标的数据场景相关度高，不满足目标指标的数据场景相关度低，或者，不满足目标指标的数据不进行场景相关度评估，只将满足目标指标的数据输入评估模 型进行场景相关度评估。举例来说，用户需求置信度高于阈值X的样本，那么对于置信度低于阈值X的数据不进行场景相关度评估。应理解，上述举例用于说明，本申请不作具体限定。
可选地，采集策略包括数据的优先级信息，其中，优先级高的数据的场景相关度高于优先级低的数据的场景相关度，数据上传时，场景相关度高的数据比场景相关度低的数据优先上传。换句话说，优先级得低的数据可以在优先级高的数据采集完之后，再进行采集，举例来说，用户需求难例样本进行模型训练，那么用户下发的采集策略中难例样本的优先级高于非难例样本，此时可以先上传难例样本，然后再上传非难例样本，本申请不作具体限定。再举例来说，用户当前训练的业务模型的识别精度较差，需要优先采集元素丰富的样本，那么用户下发的采集策略中元素丰富的数据的优先级高于元素单一的数据，那么元素丰富的数据的场景相关度高于元素单一的数据的场景相关度。其中，元素丰富的数据指的是目标传感器采集到的数据中的目标数量高于阈值，这里的目标可以是用户所需的目标，也可以不是用户所需的目标，可以是识别出的目标，也可以是未知目标，本申请不作具体限定。举例来说，目标传感器包括激光传感器和图像传感器，目标传感器采集到的第一数据中，图像传感器采集并识别出场景中的物体A，激光传感器探测出场景中存在障碍物A、B以及C，也就是说，物体A是已知目标，物体B和C是未知目标，那么第一数据属于元素丰富的数据，应理解，上述举例用于说明，在不同的应用场景下，用户可以灵活设置场景所需的优先级信息，这里不一一举例说明。
可选地，采集策略可包括上传逻辑，上传逻辑包括实时上传、资源占用低时上传、定时上传、周期间隔上传中的任一种，根据上传逻辑将样本集上传至服务器，其中，服务器用于供用户训练模型。上传逻辑可包括实时上传、资源占用低时上传、定时上传、周期间隔上传等等，本申请不作具体限定。举例来说，对于业务模型迭代要求及时的场景，用户可以输入的采集策略可包括实时上传，对于业务模型要求优先保证使用者体验的场景，用户输入的采集策略可以包括资源占用较低时上传，具体实现中，数据采集装置可以获取本地资源状态，包括内存、CPU、网络占用等，在资源状态降低的情况下进行打包和上传。应理解，上述上传逻辑用于举例说明，在不同的应用场景下，用户可以灵活设置场景所需的上传逻辑，这里不一一举例说明。
在一实施例中，用户的采集策略可以以指令的形式下发至数据采集装置，该指令可包括多个参数，一个参数对应一种上传策略，比如参数1对应上传逻辑，参数2对应优先级信息，参数3对应目标指标，参数4对应目标采集条件，参数5对应目标样本特征等等，这里不一一举例说明。携带有采集策略的指令下发至数据采集装置之后，评估单元可以根据指令中的参数确定用户下发的采集策略，比如指令包括参数1和参数5，那么可根据参数1的值确定上传逻辑，比如资源占用低时上传，根据参数5的值确定目标样本特征，比如“消防栓”的纹理特征，然后根据采集策略和评估模型确定多个数据中每个数据的场景相关度。应理解，上述举例用于说明，本申请不作具体限定。
上述实现方式，通过获取用户的采集策略，根据采集策略来确定训练模型使用的样本集，可以使得样本集更加符合用户所需的业务场景，从而避免向用户提供与业务场景无关的数据，提高通信效率，同时减轻样本存储压力，避免资源的浪费。
在一可能的实现方式中，样本集可以包括场景相关度高于阈值的数据，数据采集装置将场景相关度高于阈值的数据上传至服务器，或者，样本集中的数据是数据采集装置按照数据的场景相关度对数据进行排序后获得的，场景相关度高的数据的上传顺序早于场景相关度低的数据，具体可根据用户的采集策略确定。
可选地，数据采集装置可以先将样本集存储于数据采集装置的存储器中，该存储器与服务器建立通信连接，使得服务器可以基于用户的上报策略获取存储器中的样本集，其中，上述存储器可以是可移动存储介质，比如数据采集装置的U盘、移动硬盘、软盘、光盘、存储卡等等，本申请不作具体限定。
可选地，数据采集装置也可以对样本集进行标注，获得样本集中每个数据的标签，该标签可以是人工标注的标签也可以是机器标注的标签，具体可根据数据采集装置的处理能力决定，本申请不作具体限定，
可选地，数据采集装置确定多个数据中每个数据的场景相关度之后，可以通过客户端向用户显示每个数据的场景相关度，用户可以根据业务需求选择样本集中的数据，然后将样本集上传至服务器，本申请不作具体限定。
上述实现方式，根据数据的场景相关度确定样本集，使得样本集中的数据都是与用户所需的业务场景相关的数据，从而避免向用户提供与业务场景无关的数据，提高通信效率，同时减轻样本存储压力，避免资源的浪费。
在一可能的实现方式中，向用户发送用于训练模型的样本集之后，获取的新的采集策略，根据新的采 集策略确定新的样本集，其中，新的采集策略是服务器使用样本集对模型训练后确定的，或者，新的采集策略是用户输入的。
举例来说，业务模型下一个训练阶段为增量学习阶段，在增量学习阶段需求更多难例样本，此时服务器可以根据增量学习阶段的采集策略获得更新后的采集策略，将更新后的采集策略发送给数据采集装置，使得数据采集装置可以根据更新后的采集策略对目标传感器采集到的新数据进行场景相关度评估。其中，更新后的采集策略可以包括优先级信息，该优先级信息包括优先采集难例样本的信息。应理解，上述举例用于说明本申请不作具体限定。
需要说明的，也可以根据业务模型的训练结果和当前样本的数据维度，获取新的采集策略，其中，新的采集策略用于获取关联数据，该关联数据的数据维度与当前样本的数据维度不同，比如当前样本是单模态数据，关联数据是多模态数据。举例来说，当前样本的数据维度是单模态的数据，比如只有摄像头采集的视频帧，业务模型为识别消防栓的模型，模型的训练结果显示模型预测的准确率较低，此时需要更多的样本，服务器可以基于此获取关联数据，比如激光采集的样本以及雷达采集的样本，关联样本可以提高模型的预测能力。应理解，上述举例用于说明，本申请不作具体限定。
可选地，服务器可以向用户显示业务模型的训练结果，接收用户输入的更新后的采集策略。当然，用户也可以通过客户端向数据采集装置发送新的采集策略，本申请不作具体限定。
举例来说，业务模型是训练完毕后，用户根据业务模型的训练结果，认为业务模型对A类别的样本识别能力很高，但是对B类别的样本识别能力较差，而当前业务场景正好需求业务模型拥有对B类别的样本识别能力，用户希望可以获取更多B类别的样本对业务模型进行进一步的训练。此时用户可以通过客户端输入新的采集策略，新的采集策略中包括目标样本特征，即B类别样本的纹理特征，还可包括优先级信息，即B类别样本优先采集的信息，然后客户端100可以将上述新的采集策略发送给数据采集装置，使其优先采集B类别的数据至服务器，服务器对B类别的数据进行标注后即可获得B类别的样本。应理解，上述举例用于说明，本申请不作具体限定。
上述实现方式，根据训练结果更新采集策略，再使用新的采集策略获取新的样本集对模型进行进一步的训练，不断使用与业务场景相关的样本集对模型进行训练，解决了模型的样本集需求会随着时间不断变化的问题，提高用户的使用体验。
第二方面，提供了一种数据采集装置，该数据采集装置包括采集单元，用于获取用户输入的场景信息，其中，场景信息包括用户所需训练的模型对应的业务场景信息，采集单元，用于根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，目标传感器的数量为一个或者多个，评估单元，用于根据场景信息确定多个数据中每个数据的场景相关度，上传单元，用于向用户发送用于训练模型的样本集，其中，样本集是根据每个数据的场景相关度确定的。
上述实现方式，数据采集装置可以获取用户输入的场景信息，然后根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，再根据场景信息确定多个数据中每个数据的场景相关度，向用户发送用于训练上述模型的样本集，其中，样本集是根据每个数据的场景相关度确定的，使得样本集中的数据是与业务场景相关的数据，将样本集中的数据上传至服务器，可以避免与业务场景无关的数据被上传至服务器，从而减轻数据传输和存储的压力，提高模型训练效率。
在一可能的实现方式中，样本集中的数据的场景相关度高于阈值，或者，样本集中的数据是根据多个数据的场景相关度对多个数据进行排序后获得的。
在一可能的实现方式中，装置包括上报能力单元，上报能力单元用于在数据采集装置获取用户输入的场景信息之前，向用户提供传感器信息，其中，传感器信息包括数据采集装置拥有的至少一个传感器的参数信息，传感器信息用于供用户确定场景信息。
在一可能的实现方式中，采集单元还用于获取用户输入的采集策略，其中，采集策略包括模型训练时所需的样本的目标样本特征，评估单元，用于根据场景信息确定每个数据的第一场景相关度，根据采集策略确定每个数据的第二场景相关度，根据第一场景相关度和第二场景相关度确定每个数据的场景相关度，其中，每个数据的数据特征与目标样本特征之间的相似度越高，数据的第二场景相关度越高。
在一可能的实现方式中，采集策略包括目标采集条件，其中，样本集中数据的是在目标采集条件下采集的数据。
在一可能的实现方式中，采集策略包括数据的优先级信息，其中，优先级高的数据的场景相关度高于优先级低的数据的场景相关度。
在一可能的实现方式中，采集策略包括上传逻辑，上传逻辑包括实时上传、资源占用低时上传、定时上传、周期间隔上传中的任一种，上传单元用于根据上传逻辑将样本集上传至服务器，其中，服务器用于供用户训练模型。
在一可能的实现方式中，评估单元，用于确定场景信息对应的评估模型，使用评估模型对数据进行场景相关度评估，确定数据的第一场景相关度，其中，不同场景信息对应不同的评估模型。
在一可能的实现方式中，采集单元还用于获取的新的采集策略，根据新的采集策略确定新的样本集，其中，新的采集策略是服务器使用样本集对模型训练后确定的，或者，新的采集策略是用户输入的。
第三方面，提供了一种计算设备，该计算设备包括处理器和存储器，存储器用于存储代码，处理器用于执行代码实现如第一方面描述的方法。
第四方面，提供了一种计算机可读存储介质，该计算机可读存储介质包括指令，当指令在计算设备上运行时，使得计算设备实现如第一方面描述的方法。
第五方面，提供了一种计算机程序产品，该计算机程序产品包括代码，该代码在计算机上运行时使得计算机实现如第一方面描述的方法。
本申请在上述各方面提供的实现方式的基础上，还可以进行进一步组合以提供更多实现方式。
附图说明
图1是本申请提供的一种数据采集系统的架构图；
图2是本申请提供的一种数据采集方法的流程示意图；
图3是本申请提供的一种配置页面的示例图；
图4是本申请提供的另一种配置页面的示例图；
图5是本申请提供的一种计算设备的结构示意图。
具体实施方式
AI是利用数字计算机或者数字计算机控制的计算设备模拟、延伸和扩展人的智能，感知环境、获取知识并使用知识获得最佳结果的理论、方法、技术及应用系统。人工智能的应用场景十分广泛，比如人脸识别、车辆识别、行人重识别、数据处理应用、机器人领域、自动驾驶等等。AI模型是一种实现AI的数学方法集合。可以使用大量的样本对AI模型进行训练来使训练完成的AI模型获得预测的能力。
通常情况下，传感器会将采集到的原始数据统一上传至服务器中，再对服务器中的原始数据进行处理后获得的样本才可以用来训练AI模型，这里的数据处理可包括但不限于数据清洗、样本标注、数据筛选等等，数据处理是一个费力费时的秘籍劳动过程，需要昂贵的人力成本才能构建一个可用的数据集。
因此，如何使用尽可能少的高质量样本来训练模型是当前AI技术重点突破的课题之一。但是，传感器采集到的原始数据可能会存在大量的无场景相关度数据，比如重复数据、不清晰的无法使用的数据或者与业务场景无关的数据等等，这些无场景相关度数据也会被上传至服务器，造成数据传输和存储的压力，降低模型训练效率。即使提前预定好数据采集的规则，该也需要技术人员消耗大量的时间精力去制定采集规则，不仅存在人为误差，难以规模化，每次采集数据都要人工定制规则，而且采集规则的更新也容易出现不及时的情况，尤其对于多传感器场景来说，人工定制采集规则更加困难，比如机器人、自动驾驶场景下，人工制定采集规则时很容易出现采集规则无法覆盖全部传感器导致高场景相关度样本遗漏的情况。
综上可知，传感器采集到的原始数据可能会存在大量与业务场景无关的数据，这些数据会被上传至服务器，容易造成数据传输和存储的压力，降低模型训练效率。
为了解决传感器采集到的原始数据存在大量与业务场景无关的数据，导致数据传输和存储压力大，模型训练效率低的问题，本申请提供了一种数据采集系统，该系统中的数据采集装置可以获取用户输入的场景信息，该场景信息包括用户所需训练的模型对应的业务场景的信息，然后根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，再根据场景信息确定多个数据中每个数据的场景相关度，向用户发送用于训练上述模型的样本集，其中，样本集是根据每个数据的场景相关度确定的，使得样本集中的数据是与业务场景相关的数据，将样本集中的数据上传至服务器，可以避免与业务场景无关的数据被上传至服务器，从而减轻数据传输和存储的压力，提高模型训练效率。
如图1所示，图1是本申请提供的一种数据采集系统的架构图，如图1所示，该数据采集系统可包括 客户端100、数据采集装置200以及服务器300，其中，客户端100、数据采集装置200以及服务器300之间存在通信连接，具体可以是有线网络或者无线网络，本申请不作具体限定。客户端100、数据采集装置200以及服务器300的数量可以是一个或者多个，本申请不作具体限定。
客户端100可以是与用户交互的计算设备、终端设备或者边缘计算设备，该计算设备可包括但不限于物理服务器、虚拟机、容器、芯片等，其中，物理服务器可包括但不限于ARM服务器、X86服务器等；虚拟机指的是网络功能虚拟化(network functions virtualization,NFV)技术实现的、通过软件模拟的具有完整硬件系统功能的、运行在一个完全隔离环境中的完整计算机系统；容器指的是一组受到资源限制，彼此间相互隔离的进程；终端设备可以是智能手机、掌上处理设备、平板电脑、移动笔记本、增强现实(augmented reality,AR)设备、虚拟现实(virtual reality,VR)设备、一体化掌机、穿戴设备、车载设备、智能会议设备、智能广告设备、智能家电等等，此处不作具体限定。边缘计算设备指的是与数据采集装置200距离较近的、拥有计算能力的计算设备，边缘计算设备可以对数据采集装置200所采集的数据进行预处理，然后再将预处理后的数据发送至远端进行存储，从而节省通信带宽，减少数据流量，还可以提高数据的安全性。
数据采集装置200可以是一个或者多个传感器的计算设备、终端设备、机器人或者车辆，其中，一个或者多个传感器可以包括图像传感器，具体可以是用于面部图像识别、眼睛虹膜识别的摄像头，比如语音传感器，具体可以是用于语音识别的麦克风阵列，还可以包括其他能够感受规定的被测量(物理量、化学量、状态量以及机械量)，并按一定规律转换成可用输出信号的多种信息获取传感器，比如，激光传感器、光敏传感器、气敏传感器、雷达传感器、化学传感器、压敏传感器、温敏传感器、流体传感器、加速度传感器、陀螺仪传感器、地磁传感器、红外线传感器、心电传感器、脉压传感器等等。应理解，上述提及的传感器用于示例性说明，并不能构成具体限定。
服务器300可以是计算设备，比如裸金属服务器(bare metal server,BMS)、虚拟机、容器、边缘计算设备等。其中，BMS指的是通用的物理服务器，例如，ARM服务器或者X86服务器，虚拟机和容器的描述可参考前述内容，这里不重复赘述。服务器300还可以是用于存储数据的服务器，比如存储阵列，本申请不作具体限定。服务器300还可以是多个上述计算设备组成的服务器集群，比如集中式服务器集群或者分布式服务器集群，本申请不作具体限定。
可选地，客户端100可以部署于服务器300中，或者，客户端100可以部署于数据采集装置200中，或者，数据采集装置200和服务器300可以是同一个计算设备，或者，客户端100、数据采集装置200以及服务器300是同一个计算设备，或者，客户端100、数据采集装置200以及服务器300在不同的计算设备上，本申请不作具体限定。
可选地，服务器300也可以部署于公有云中，客户端100是该公有云的控制台(console)或者API，本申请不作具体限定。举例来说，数据采集装置200是园区机器人，客户端100可以是web化的管理系统，服务器300是公有云中的对象存储服务(Object Storage Service,OBS)设备，用户400可以通过客户端100购买公有云的OBS存储服务，园区机器人采集的数据可以上传至公有云中进行存储，应理解，上述举例用于说明，本申请不作具体限定。
进一步地，客户端100、数据采集装置200以及服务器300可以划分为多个单元模块，示例性地，如图1所示，客户端100可包括场景确认单元101以及策略下发单元102。数据采集装置200可包括上报能力单元201、采集单元202、评估单元203以及上传单元204。服务器300可包括模型训练单元301以及策略更新单元302。应理解，图1中的划分方式用于举例说明，客户端100、数据采集装置200以及服务器300还可以包括更多或者更少的单元模块，本申请不作具体限定。
数据采集装置200中的上报能力单元201用于获取数据采集装置200中的传感器信息，并将其上报给客户端100。其中，传感器信息可包括数据采集装置200中传感器的参数信息，比如分辨率、灵敏度、线性范围、稳定性、精度、频率响应特性、采样频率等等，本申请不作具体限定。应理解，不同传感器的参数信息类型可能不同，本申请不作具体限定。
可选地，传感器信息还可包括其他用于确认业务场景的相关信息，举例来说，传感器信息还可包括数据采集装置200的设备信息，比如数据采集装置200的设备型号、设备功能、资源状态、设备状态等等，其中，资源状态可包括但不限于内存、CPU、网络占用等等，设备状态可包括但不限于数据采集装置200的传感器是否发生损坏，数据采集装置200是否处于异常状态等等。再举例来说，传感器信息还可包括数据采集装置200和客户端100之间的通信网络的状态信息，比如带宽、网速等等，应理解，上述举例用于说明，本申请不作具体限定。
具体实现中，上报能力单元201可以在首次于客户端100建立通信连接后，向客户端100上报传感器信息，或者，在接收到客户端100发起的上报请求之后，向客户端100上报传感器信息，或者，在数据采集装置200的传感器信息发生更新后，将更新后的传感器信息上报给客户端，本申请不作具体限定。
客户端100中的场景确认单元101用于接收上报能力单元201上报的传感器信息，向用户400显示该传感器信息后，接收用户400输入的场景信息，并将场景信息返回给数据采集装置200。
具体实现中，场景信息可以是用户所需的业务场景的信息，比如业务场景的名称、编号、缩写等等，本申请不作具体限定。用户所需的业务场景可包括但不限于图像识别、语音识别、文字识别、图像检测等等，本申请不作具体限定。举例来说，用户的业务场景是图像识别时，用户可以输入场景信息为编号01，用户的业务场景是语音识别时，用户可以输入场景信息为编号02，用户的业务场景是文字识别时，用户可以输入场景信息为编号03，以此类推，应理解，上述举例用于说明，本申请不作具体限定。
可选地，用户可以根据客户端100显示的传感器信息，输入用户所需的业务场景的信息，如果客户端100显示该数据采集装置200没有业务场景所需的传感器信息，用户可以选择其他能够满足业务场景需求的其他数据采集装置200，从而避免数据采集装置200无法采集到用户的业务场景相关的数据，降低模型训练效率。
具体实现中，客户端100可以根据传感器信息确定该传感器信息可实现的一个或者多个场景，然后向用户展示的上述一个或者多个场景后，获取用户从中选择的场景信息。或者，用户也可以根据客户端100展示的传感器信息，自行输入场景信息，本申请不作具体限定。具体实现中，客户端100可以通过可视化界面向用户展示多个场景信息以供选择，也可以通过应用程序界面(application program interface,API)输入场景信息，本申请不作具体限定。
数据采集装置200中的采集单元202用于接收客户端100发送的场景信息，根据场景信息确定目标传感器，使用目标传感器采集多个数据，其中，目标传感器的数量可以是一个或者多个。
具体实现中，数据采集装置200可以存储有场景信息和目标传感器之间的对应关系，比如图像识别场景对应图像传感器，语音识别场景对应麦克风、声敏传感器等等，本申请不作具体限定。
需要说明的，根据场景信息对应的多个目标传感器中，不仅包括业务直接需求的目标传感器，还可包括业务扩展需求的目标传感器，其中，业务扩展需求的目标传感器所采集到的数据，能够为业务直接需求的目标传感器采集到的数据提供更多数据维度的补充。举例来说，图像识别场景中，业务直接需求的目标传感器是摄像头，但是雷达传感器、红外传感器、激光传感器等其他传感器所采集到的关联模态的样本数据也可能在该图像识别场景中提供数据维度的补充，这些传感器可以是该业务场景下存在业务扩展需求的目标传感器，那么图像识别场景下的目标传感器不仅包括摄像头，还可包括雷达传感器、红外传感器、激光传感器等，应理解，上述举例用于说明，在不同的业务场景中，业务直接需求的目标传感器和业务扩展需求的目标传感器不同，这里不一一举例说明。
可以理解的，根据用户确认的场景信息确定目标传感器，使用目标传感器采集多个数据，这样提供给用户的多个数据是与用户所确认的业务场景相关的数据，从而避免向用户提供与业务场景无关的数据，提高通信效率，减轻服务器300的样本存储压力，避免资源的浪费。
可选地，在目标传感器的数量为多个时，采集单元202使用目标传感器采集的多个数据可包括同一时刻下多个目标传感器采集的多个数据。具体地，可以对多个目标传感器采集到的数据进行数据对齐操作后，获得上述多个数据。应理解，由于不同目标传感器的采集频率存在差异，因此可以对多个目标传感器采集的数据进行数据对齐处理，获得上述多个数据。
具体实现中，可以为多个目标传感器在周期时间内采集到的多个数据以及采集时间建立数据集合和对应的查找索引，基于索引快速定位和筛选出同一时刻下采集的数据，从而获得对齐后的多个数据。比如图像传感器在00:01、00:03、00:05采集了3个图像帧，雷达传感器在00:01、00:02、00:03、00:04、00:05、00:06采集了6个雷达帧，那么数据对齐后，可以获得3组对齐的多个数据，第1组数据包括00:01时刻下的雷达帧和图像帧，第2组数据包括00:03时刻下的雷达帧和图像帧，第3组数据包括00:01时刻下的雷达帧和图像帧。应理解，上述举例用于说明，本申请不作具体限定。
应理解，对多个目标传感器采集到的数据进行对齐处理时，也可以适当进行数据补偿，以获得对齐后的多个数据，举例来说，图像传感器在00:01、00:03、00:05采集了3个图像帧，雷达传感器在00:02、00:04、00:06采集了3个雷达帧，那么可以根据00:01、00:03、00:05采集的图像帧确定00:02、00:04、00:06的补偿帧，从而获得对齐后的多个数据，比如00:02时刻下的雷达帧和图像帧，这里不一一展开说明。应理解， 上述举例用于说明，本申请不作具体限定。
需要说明的，对于多传感器融合场景来说，上述多个数据中的每个数据可以包括同一时刻下的多个传感器采集的数据，比如同一时刻下的雷达帧和图像帧可以作为一个数据。对于单一传感器场景来说，一个数据对应一个传感器采集的数据，比如一个图像帧，一个文本等等，本申请不作限定。
数据采集装置200的评估单元203可以对采集单元202采集的多个数据进行场景相关度评估，获得多个数据对应的场景相关度。
在一可能的实现方式中，数据的场景相关度是根据数据与场景信息所描述的业务场景之间的相关程度确定的，相关程度越高，数据的场景相关度越高，反之则越低。
可选地，数据的场景相关度是根据数据是否包含目标确定的，包含目标的数据的场景相关度高于不包含目标的数据的场景相关度，该目标可以是业务场景所需的目标，比如业务场景是行人识别时，业务场景所需的目标可以是行人；比如业务场景是车辆识别，业务场景所需的目标可以是车辆，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度还可以是根据数据包含的目标数量确定的，数据包含的目标数量越高，数据的场景相关度越高，反之则越低，比如业务场景是车辆识别时，包含2个车辆的图像数据A的场景相关度高于包含1车辆的图像数据B的场景相关度，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度还可以根据数据对模型训练时的贡献程度决定的，数据对模型训练时的贡献程度越高，数据的场景相关度越高，反之则越低，比如标签置信度越差、样本清晰度越差、训练效果越差的数据的场景相关度越底，反之则越高，举例来说，数据C的目标边框模糊不清，或者边框位置错误，或者目标边框没有框住完整目标，数据D的目标边框完整、清晰的框住了目标，那么数据C的场景相关度低于数据D的场景相关度，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度还可以是根据数据采集装置200采集数据时的设备状态决定的，设备状态越差，数据采集装置所采集的数据的场景相关度越低，反之则越高。举例来说，数据采集装置200为园区机器人，园区机器人处于非正常姿态时，比如园区机器人摔倒、倾覆等状态时，其采集的数据E的场景相关度低于正常姿态下采集的数据F的场景相关度，上述举例用于说明，本申请不作具体限定。
具体实现中，评估单元203可以将多个数据输入评估模型，获得多个数据中每个数据对应的场景相关度，其中，评估模型可以是使用评估样本集对神经网络进行训练后获得的，其中，评估样本集包括评估样本和评估样本对应的标签，评估样本包括已知数据，评估样本对应的标签包括已知数据对应的场景相关度。其中，评估样本的标签可以是根据评估标准确定的，评估标准可包括但不限于评估样本是否包含目标、包含目标数量、样本置信度中的一个或者多个，还可以包括其他评估标准，这里不一一举例说明。
需要说明的，评估单元203可以使用通用的评估模型确定每个数据对应的场景相关度，或者，根据用户选择的场景信息确定与业务场景对应的评估模型，应理解，不同的业务场景下数据的场景相关度的评估标准可能存在差异，因此可以使用业务场景对应的评估模型进行数据的场景相关度评估，从而提高数据的场景相关度的获取的准确性。举例来说，目标识别场景下，样本中目标数量越多，样本场景相关度越高，目标检测场景下，目标边界框的置信度越高，样本场景相关度越高，应理解，上述举例用于说明，本申请不作具体限定。
在一实施例中，上述评估标准还可包括数据采集装置200的设备状态，设备状态差时，样本的场景相关度较低，设备状态可包括但不限于数据采集装置200的传感器是否发生损坏，数据采集装置200是否处于异常状态等等。举例来说，数据采集装置200是园区机器人，那么异常状态可以包括园区机器人的运动路线发生严重偏移。应理解，上述举例用于说明，本申请不作具体限定。
举例来说，目标检测场景下的评估标准可包括样本的置信度、样本中的目标数量、样本采集时的设备状态是否异常等等，如果样本是多模态数据，比如摄像头采集的图像数据和激光传感器采集的激光数据，那么该评估标准还可包括图像数据与激光数据之间的目标匹配度，应理解，上述举例用于说明，本申请不作具体限定。
在一实施例中，客户端100的策略下发单元102可以获取用户下发的采集策略，并将其发送给数据采集装置200，数据采集装置200的评估单元203可以结合用户下发的采集策略进一步确定第一数据的场景相关度。
具体实现中，评估单元203可以在第一数据符合采集策略的情况下，将第一数据输入评估模型获得第一数据的场景相关度，或者，评估单元203可以将第一数据输入评估模型获得第一场景相关度，根据采集 策略获得第二场景相关度，根据第一场景相关度和第二场景相关度获得第一数据的场景相关度。其中，第一场景相关度和第二场景相关度的获取可以是同时进行的，也可以是先后进行的，本申请不作具体限定。
可选地，采集策略可以包括目标样本特征，以目标检测场景为例，假设用户需要优先采集“消防栓”样本图像，那么采集策略可以包括“消防栓”样本的纹理特征，其中，纹理特征用于描述图像中目标的表面性质，例如图像纹理的粗细、稠密等特征。常见的纹理特征有灰度共生矩阵特征、自回归纹理模型特征、Tamura纹理特征、小波变换特征等，本申请不作具体限定。
具体实现中，评估单元203接收到携带有目标样本特征的采集策略之后，可以根据第一数据的特征与目标样本特征之间的相似度的大小，确定第一数据的场景相关度。比如相似度越大，场景相关度越高，相似度越低，场景相关度越低，最后根据相似度确定第一数据的第二场景相关度，根据评估模型的输出值确定第一数据的第一场景相关度，然后根据第一场景相关度和第二场景相关度获得第一数据的场景相关度。或者，评估单元203可以在确定第一数据的特征与目标样本特征之间的相似度高于阈值标准的情况下，将第一数据输入评估模型获得第一数据的场景相关度，其中，上述阈值标准可以是用户输入的采集策略中携带的，也可以是数据采集装置200预设的阈值标准，本申请不作具体限定。
可选地，采集策略可以包括目标采集条件，在目标采集条件下采集到的数据的场景相关度高，非目标采集条件下采集到的数据的场景相关度低，或者，非目标采集条件下采集到的数据不进行场景相关度评估，比如场景相关度为0，只将目标采集条件下采集到的数据输入评估模型进行场景相关度评估。举例来说，用户需求采集环境温度高于40摄氏度时目标传感器采集的样本，那么对于环境温度低于40摄氏度时目标传感器采集的样本不进行场景相关度评估。
可选地，采集策略可以包括目标指标，满足目标指标的数据场景相关度高，不满足目标指标的数据场景相关度低，或者，不满足目标指标的数据不进行场景相关度评估，只将满足目标指标的数据输入评估模型进行场景相关度评估。举例来说，用户需求置信度高于阈值X的样本，那么对于置信度低于阈值X的数据不进行场景相关度评估。应理解，上述举例用于说明，本申请不作具体限定。
可选地，采集策略可包括优先级信息，其中，优先级高的数据的场景相关度高于优先级低的数据，数据上传时，场景相关度高的数据比场景相关度低的数据优先上传。换句话说，优先级得低的数据可以在优先级高的数据采集完之后，再进行采集，举例来说，用户需求难例样本进行模型训练，那么用户下发的采集策略中难例样本的优先级高于非难例样本，此时可以先上传难例样本，然后再上传非难例样本，本申请不作具体限定。再举例来说，用户当前训练的业务模型的识别精度较差，需要优先采集元素丰富的样本，那么用户下发的采集策略中元素丰富的数据的优先级高于元素单一的数据，那么元素丰富的数据的场景相关度高于元素单一的数据的场景相关度。其中，元素丰富的数据指的是目标传感器采集到的数据中的目标数量高于阈值，这里的目标可以是用户所需的目标，也可以不是用户所需的目标，可以是识别出的目标，也可以是未知目标，本申请不作具体限定。举例来说，目标传感器包括激光传感器和图像传感器，目标传感器采集到的第一数据中，图像传感器采集并识别出场景中的物体A，激光传感器探测出场景中存在障碍物A、B以及C，也就是说，物体A是已知目标，物体B和C是未知目标，那么第一数据属于元素丰富的数据，应理解，上述举例用于说明，在不同的应用场景下，用户可以灵活设置场景所需的优先级信息，这里不一一举例说明。
可选地，采集策略可包括上传逻辑，上传逻辑可包括实时上传、资源占用低时上传、定时上传、周期间隔上传等等，本申请不作具体限定。举例来说，对于业务模型迭代要求及时的场景，用户可以输入的采集策略可包括实时上传，对于业务模型要求优先保证使用者体验的场景，用户输入的采集策略可以包括资源占用较低时上传，具体实现中，数据采集装置200可以获取本地资源状态，包括内存、CPU、网络占用等，在资源状态降低的情况下进行打包和上传。应理解，上述上传逻辑用于举例说明，在不同的应用场景下，用户可以灵活设置场景所需的上传逻辑，这里不一一举例说明。
在一实施例中，用户的采集策略可以以指令的形式下发至评估单元203，该指令可包括多个参数，一个参数对应一种上传策略，比如参数1对应上传逻辑，参数2对应优先级信息，参数3对应目标指标，参数4对应目标采集条件，参数5对应目标样本特征等等，这里不一一举例说明。携带有采集策略的指令下发至评估单元203之后，评估单元可以根据指令中的参数确定用户下发的采集策略，比如指令包括参数1和参数5，那么可根据参数1的值确定上传逻辑，比如资源占用低时上传，根据参数5的值确定目标样本特征，比如“消防栓”的纹理特征，然后根据采集策略和评估模型确定多个数据中每个数据的场景相关度。应理解，上述举例用于说明，本申请不作具体限定。
需要说明的，用户也可以不输入采集策略，数据采集装置200可以根据评估模型，确定每个数据的场景相关度，当数据采集装置200接收到用户输入的采集策略时，再结合采集策略和评估模型确定每个数据的场景相关度，本申请不对此进行具体限定。
上传单元204可以根据多个数据中每个数据的场景相关度，确定样本集，将样本集上传至服务器300。
可选地，样本集可以包括场景相关度高于阈值的数据，上传单元204将场景相关度高于阈值的数据上传至服务器300，或者，样本集中的数据是上传单元204按照数据的场景相关度对数据进行排序后获得的，场景相关度高的数据的上传顺序早于场景相关度低的数据，具体可根据用户的采集策略确定。举例来说，若用户的采集策略中没有设置优先级信息，此时可以将场景相关度高于阈值的数据统一上传至服务器300，若用户采集策略中设置有优先级信息，此时可以按照数据的场景相关度对数据进行排序，场景相关度高的数据被优先上传至服务器300。应理解，上述举例用于说明，本申请不作具体限定。
可选地，上传单元204可以先将样本集存储于数据采集装置200的存储器中，该存储器与服务器300建立通信连接，使得服务器300可以基于用户的上报策略获取存储器中的样本集，其中，上述存储器可以是可移动存储介质，比如数据采集装置200的U盘、移动硬盘、软盘、光盘、存储卡等等，本申请不作具体限定。
可选地，上传单元204也可以对样本集进行标注，获得样本集中每个数据的标签，该标签可以是人工标注的标签也可以是机器标注的标签，具体可根据数据采集装置200的处理能力决定，本申请不作具体限定，
在一实施例中，评估单元203确定多个数据中每个数据的场景相关度之后，上传单元204可以通过客户端100向用户显示每个数据的场景相关度，用户可以根据业务需求选择样本集中的数据，然后将样本集上传至服务器300，本申请不作具体限定。
服务器300的模型训练单元301可以接收数据采集装置200上报的样本集。使用上述样本集对业务模型进行训练，获得训练结果。具体实现中，上述业务模型是用户正在训练的模型，模型可以是机器学习模型，具体可以是人工神经网络模型、贝叶斯网络模型、决策树模型、向量机模型、随机森林模型、关联规则模型中的一种或者多种，还可以是其他类型的机器学习模型，这里不一一举例说明。
可选地，上传单元204没有对样本集进行标注，模型训练单元301也可以对样本集进行标注。具体实现中，可以是人工标注也可以是机器标注，本申请不作具体限定。
服务器300的策略更新单元302可以根据业务模型的训练结果确定更新后的采集策略。
具体实现中，业务模型是用户正在训练的模型，该模型的训练过程可以包括多个训练阶段，每个训练阶段所需的样本的采集策略可能存在差异，策略更新单元302可以存储业务模型每个训练阶段所需的采集策略，根据业务模型当前的训练结果，确定下一个训练阶段所需要的采集策略，获得更新后的采集策略。
举例来说，业务模型下一个训练阶段为增量学习阶段，在增量学习阶段需求更多难例样本，此时策略更新单元302可以根据增量学习阶段的采集策略获得更新后的采集策略，将更新后的采集策略发送给数据采集装置200，使得数据采集装置200可以根据更新后的采集策略对目标传感器采集到的新数据进行场景相关度评估。其中，更新后的采集策略可以包括优先级信息，该优先级信息包括优先采集难例样本的信息。应理解，上述举例用于说明本申请不作具体限定。
需要说明的，也可以根据业务模型的训练结果和当前样本的数据维度，获取新的采集策略，其中，新的采集策略用于获取关联数据，该关联数据的数据维度与当前样本的数据维度不同，比如当前样本是单模态数据，关联数据是多模态数据。举例来说，当前样本的数据维度是单模态的数据，比如只有摄像头采集的视频帧，业务模型为识别消防栓的模型，模型的训练结果显示模型预测的准确率较低，此时需要更多的样本，策略更新单元302可以基于此获取关联数据，比如激光采集的样本以及雷达采集的样本，关联样本可以提高模型的预测能力。应理解，上述举例用于说明，本申请不作具体限定。
可选地，策略更新单元302可以向用户显示业务模型的训练结果，接收用户输入的更新后的采集策略。当然，用户也可以通过客户端100的策略下发单元102向数据采集装置200发送新的采集策略，本申请不作具体限定。
举例来说，业务模型是训练完毕后，用户根据业务模型的训练结果，认为业务模型对A类别的样本识别能力很高，但是对B类别的样本识别能力较差，而当前业务场景正好需求业务模型拥有对B类别的样本识别能力，用户希望可以获取更多B类别的样本对业务模型进行进一步的训练。此时用户可以通过客户端100输入新的采集策略，新的采集策略中包括目标样本特征，即B类别样本的纹理特征，还可包括优先级 信息，即B类别样本优先采集的信息，然后客户端100可以通过策略下发单元102将上述新的采集策略发送给数据采集装置200，使其优先采集B类别的数据至服务器300，服务器300对B类别的数据进行标注后即可获得B类别的样本。应理解，上述举例用于说明，用户也可以通过服务器300中的策略更新单元302输入新的采集策略，本申请不作具体限定。
综上可知，本申请提供了一种数据采集系统，该系统中的数据采集装置可以获取用户输入的场景信息，该场景信息包括用户所需训练的模型对应的业务场景的信息，然后根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，再根据场景信息确定多个数据中每个数据的场景相关度，向用户发送用于训练上述模型的样本集，其中，样本集是根据每个数据的场景相关度确定的，使得样本集中的数据是与业务场景相关的数据，将样本集中的数据上传至服务器，可以避免与业务场景无关的数据被上传至服务器，从而减轻数据传输和存储的压力，提高模型训练效率。
图2是本申请提供的一种数据采集方法的流程示意图，该方法可应用于如图1所示的数据采集系统中，如图2所示，该方法可包括以下步骤。
步骤S201：数据采集装置200向客户端100上报传感器信息。该步骤可以由图1实施例中的上报能力单元201实现。其中，数据采集装置200和客户端100的描述可参考图1实施例，这里不重复赘述。
可选地，传感器信息可包括数据采集装置200中传感器的参数信息，比如分辨率、灵敏度、线性范围、稳定性、精度、频率响应特性、采样频率等等，本申请不作具体限定。应理解，不同传感器的参数信息类型可能不同，本申请不作具体限定。
可选地，传感器信息还可包括其他用于确认业务场景的相关信息，举例来说，传感器信息还可包括数据采集装置200的设备信息，比如数据采集装置200的设备型号、设备功能、资源状态、设备状态等等，其中，资源状态可包括但不限于内存、CPU、网络占用等等，设备状态可包括但不限于数据采集装置200的传感器是否发生损坏，数据采集装置200是否处于异常状态等等。再举例来说，传感器信息还可包括数据采集装置200和客户端100之间的通信网络的状态信息，比如带宽、网速等等，应理解，上述举例用于说明，本申请不作具体限定。
具体实现中，数据采集装置200可以在首次于客户端100建立通信连接后，向客户端100上报传感器信息，或者，在接收到客户端100发起的上报请求之后，向客户端100上报传感器信息，或者，在数据采集装置200的传感器信息发生更新后，将更新后的传感器信息上报给客户端，本申请不作具体限定。
步骤S202：客户端100向数据采集装置200发送用户输入的场景信息。该步骤可以由图1实施例中的场景确认单元101实现。
具体实现中，场景信息可以是用户所需的业务场景的信息，比如业务场景的名称、编号、缩写等等，本申请不作具体限定。用户所需的业务场景可包括但不限于图像识别、语音识别、文字识别、图像检测等等，本申请不作具体限定。
可选地，用户可以根据客户端100显示的传感器信息，输入用户所需的业务场景的信息，如果客户端100显示该数据采集装置200没有业务场景所需的传感器信息，用户可以选择其他能够满足业务场景需求的其他数据采集装置200，从而避免数据采集装置200无法采集到用户的业务场景相关的数据，降低模型训练效率。
具体实现中，客户端100可以根据传感器信息确定该传感器信息可实现的一个或者多个场景，然后向用户展示的上述一个或者多个场景后，获取用户从中选择的场景信息。或者，用户也可以根据客户端100展示的传感器信息，自行输入场景信息，本申请不作具体限定。具体实现中，客户端100可以通过可视化界面向用户展示多个场景信息以供选择，也可以通过API输入场景信息，本申请不作具体限定。
步骤S203：数据采集装置200根据场景信息确定目标传感器。该步骤可以由图1实施例中的采集单元202实现。其中，目标传感器的数量可以是一个或者多个。
具体实现中，数据采集装置200可以存储有场景信息和目标传感器之间的对应关系，比如图像识别场景对应图像传感器，语音识别场景对应麦克风、声敏传感器等等，本申请不作具体限定。
需要说明的，根据场景信息对应的多个目标传感器中，不仅包括业务直接需求的目标传感器，还可包括业务扩展需求的目标传感器，其中，业务扩展需求的目标传感器所采集到的数据，能够为业务直接需求的目标传感器采集到的数据提供更多数据维度的补充。举例来说，图像识别场景中，业务直接需求的目标传感器是摄像头，但是雷达传感器、红外传感器、激光传感器等其他传感器所采集到的关联模态的样本数 据也可能在该图像识别场景中提供数据维度的补充，这些传感器可以是该业务场景下存在业务扩展需求的目标传感器，那么图像识别场景下的目标传感器不仅包括摄像头，还可包括雷达传感器、红外传感器、激光传感器等，应理解，上述举例用于说明，在不同的业务场景中，业务直接需求的目标传感器和业务扩展需求的目标传感器不同，这里不一一举例说明。
可以理解的，根据用户确认的场景信息确定目标传感器，使用目标传感器采集多个数据，这样提供给用户的多个数据是与用户所确认的业务场景相关的数据，从而避免向用户提供与业务场景无关的数据，提高通信效率，减轻服务器300的样本存储压力，避免资源的浪费。
可选地，在目标传感器的数量为多个时，使用目标传感器采集的多个数据可包括同一时刻下多个目标传感器采集的多个数据。具体地，可以对多个目标传感器采集到的数据进行数据对齐操作后，获得上述多个数据。应理解，由于不同目标传感器的采集频率存在差异，因此可以对多个目标传感器采集的数据进行数据对齐处理，获得上述多个数据。
具体实现中，可以为多个目标传感器在周期时间内采集到的多个数据以及采集时间建立数据集合和对应的查找索引，基于索引快速定位和筛选出同一时刻下采集的数据，从而获得对齐后的多个数据。应理解，对多个目标传感器采集到的数据进行对齐处理时，也可以适当进行数据补偿，以获得对齐后的多个数据，本申请不作具体限定。
需要说明的，对于多传感器融合场景来说，上述多个数据中的每个数据可以包括同一时刻下的多个传感器采集的数据，比如同一时刻下的雷达帧和图像帧可以作为一个数据。对于单一传感器场景来说，一个数据对应一个传感器采集的数据，比如一个图像帧，一个文本等等，本申请不作限定。
步骤S204：数据采集装置200使用目标传感器采集多个数据。该步骤可以由图1实施例中的采集单元202实现。
步骤S205：客户端100向数据采集装置200发送用户输入的采集策略。该步骤可以由图1实施例中的策略下发单元102实现。
可选地，采集策略可以包括目标样本特征，以目标检测场景为例，假设用户需要优先采集“消防栓”样本图像，那么采集策略可以包括“消防栓”样本的纹理特征，其中，纹理特征用于描述图像中目标的表面性质，例如图像纹理的粗细、稠密等特征。常见的纹理特征有灰度共生矩阵特征、自回归纹理模型特征、Tamura纹理特征、小波变换特征等，本申请不作具体限定。其中，数据的样本特征与目标样本特征越相似，该数据的场景相关度越高。
可选地，采集策略可以包括目标采集条件，在目标采集条件下采集到的数据的场景相关度高，非目标采集条件下采集到的数据的场景相关度低，或者，非目标采集条件下采集到的数据不进行场景相关度评估，比如场景相关度为0，只将目标采集条件下采集到的数据输入评估模型进行场景相关度评估。举例来说，用户需求采集环境温度高于40摄氏度时目标传感器采集的样本，那么对于环境温度低于40摄氏度时目标传感器采集的样本不进行场景相关度评估。
可选地，采集策略可以包括目标指标，满足目标指标的数据场景相关度高，不满足目标指标的数据场景相关度低，或者，不满足目标指标的数据不进行场景相关度评估，只将满足目标指标的数据输入评估模型进行场景相关度评估。举例来说，用户需求置信度高于阈值X的样本，那么对于置信度低于阈值X的数据不进行场景相关度评估。应理解，上述举例用于说明，本申请不作具体限定。
可选地，采集策略可包括优先级信息，其中，优先级高的数据的场景相关度高于优先级低的数据，数据上传时，场景相关度高的数据比场景相关度低的数据优先上传。换句话说，优先级得低的数据可以在优先级高的数据采集完之后，再进行采集，举例来说，用户需求难例样本进行模型训练，那么用户下发的采集策略中难例样本的优先级高于非难例样本，此时可以先上传难例样本，然后再上传非难例样本，本申请不作具体限定。再举例来说，用户当前训练的业务模型的识别精度较差，需要优先采集元素丰富的样本，那么用户下发的采集策略中元素丰富的数据的优先级高于元素单一的数据，那么元素丰富的数据的场景相关度高于元素单一的数据的场景相关度。其中，元素丰富的数据指的是目标传感器采集到的数据中的目标数量高于阈值，这里的目标可以是用户所需的目标，也可以不是用户所需的目标，可以是识别出的目标，也可以是未知目标，本申请不作具体限定。举例来说，目标传感器包括激光传感器和图像传感器，目标传感器采集到的第一数据中，图像传感器采集并识别出场景中的物体A，激光传感器探测出场景中存在障碍物A、B以及C，也就是说，物体A是已知目标，物体B和C是未知目标，那么第一数据属于元素丰富的数据，应理解，上述举例用于说明，在不同的应用场景下，用户可以灵活设置场景所需的优先级信息，这 里不一一举例说明。
可选地，采集策略可包括上传逻辑，上传逻辑可包括实时上传、资源占用低时上传、定时上传、周期间隔上传等等，本申请不作具体限定。举例来说，对于业务模型迭代要求及时的场景，用户可以输入的采集策略可包括实时上传，对于业务模型要求优先保证使用者体验的场景，用户输入的采集策略可以包括资源占用较低时上传，具体实现中，数据采集装置200可以获取本地资源状态，包括内存、CPU、网络占用等，在资源状态降低的情况下进行打包和上传。应理解，上述上传逻辑用于举例说明，在不同的应用场景下，用户可以灵活设置场景所需的上传逻辑，这里不一一举例说明。
在一实施例中，用户的采集策略可以以指令的形式下发至数据采集装置200，该指令可包括多个参数，一个参数对应一种上传策略，比如参数1对应上传逻辑，参数2对应优先级信息，参数3对应目标指标，参数4对应目标采集条件，参数5对应目标样本特征等等，这里不一一举例说明。携带有采集策略的指令下发至数据采集装置200之后，评估单元可以根据指令中的参数确定用户下发的采集策略，比如指令包括参数1和参数5，那么可根据参数1的值确定上传逻辑，比如资源占用低时上传，根据参数5的值确定目标样本特征，比如“消防栓”的纹理特征，然后根据采集策略和评估模型确定多个数据中每个数据的场景相关度。应理解，上述举例用于说明，本申请不作具体限定。
需要说明的，用户也可以不输入采集策略，也就是说，步骤S205可以省略，数据采集装置200可以根据评估模型，确定每个数据的场景相关度，当数据采集装置200接收到用户输入的采集策略时，再结合采集策略和评估模型确定每个数据的场景相关度，本申请不对此进行具体限定。
步骤S206：数据采集装置200确定每个数据的场景相关度。该步骤可以由图1实施例中的评估单元203实现。
在一可能的实现方式中，数据的场景相关度是根据数据与场景信息所描述的业务场景之间的相关程度确定的，相关程度越高，数据的场景相关度越高，反之则越低。
可选地，数据的场景相关度是根据数据是否包含目标确定的，包含目标的数据的场景相关度高于不包含目标的数据的场景相关度，该目标可以是业务场景所需的目标，比如业务场景是行人识别时，业务场景所需的目标可以是行人；比如业务场景是车辆识别，业务场景所需的目标可以是车辆，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度是根据数据包含的目标数量确定的，数据包含的目标数量越高，数据的场景相关度越高，反之则越低，比如业务场景是车辆识别时，包含2个车辆的图像数据A的场景相关度高于包含1车辆的图像数据B的场景相关度，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度是根据数据对模型训练时的贡献程度决定的，比如数据的标签置信度越差、样本清晰度越差、训练效果越差的数据的场景相关度越底，反之则越高，举例来说，数据C的目标边框模糊不清，或者边框位置错误没有将目标完整框住，数据D的目标边框完整、清晰的框住了目标，那么数据C的场景相关度低于数据D的场景相关度，上述举例用于说明，本申请不作具体限定。
可选地，数据的场景相关度是根据数据采集装置200采集数据时的设备状态决定的，设备状态越差，数据采集装置所采集的数据的场景相关度越低，反之则越高。举例来说，数据采集装置200为园区机器人，园区机器人处于非正常姿态时，比如园区机器人摔倒、倾覆等状态时，其采集的数据E的场景相关度低于正常姿态下采集的数据F的场景相关度，上述举例用于说明，本申请不作具体限定。
具体实现中，数据采集装置200可以对多个数据中任意一个第一数据执行以下步骤：在第一数据符合采集策略的情况下，将第一数据输入评估模型获得第一数据的场景相关度，或者，将第一数据输入评估模型获得第一场景相关度，根据采集策略获得第二场景相关度，根据第一场景相关度和第二场景相关度获得第一数据的场景相关度。其中，第一场景相关度和第二场景相关度的获取可以是同时进行的，也可以是先后进行的，本申请不作具体限定。
具体实现中，评估模型可以是使用评估样本集对神经网络进行训练后获得的，其中，评估样本集包括评估样本和评估样本对应的标签，评估样本包括已知数据，评估样本对应的标签包括已知数据对应的场景相关度。其中，评估样本的标签可以是根据评估标准确定的，评估标准可包括但不限于评估样本是否包含目标、包含目标数量、样本置信度中的一个或者多个，还可以包括其他评估标准，这里不一一举例说明。
需要说明的，数据采集装置200可以使用通用的评估模型确定每个数据对应的场景相关度，或者，根据用户选择的场景信息确定与业务场景对应的评估模型，应理解，不同的业务场景下数据的场景相关度的评估标准可能存在差异，因此可以使用业务场景对应的评估模型进行数据的场景相关度评估，从而提高数 据的场景相关度的获取的准确性。举例来说，目标识别场景下，样本中目标数量越多，样本场景相关度越高，目标检测场景下，目标边界框的置信度越高，样本场景相关度越高，应理解，上述举例用于说明，本申请不作具体限定。
在一实施例中，上述评估标准还可包括数据采集装置200的设备状态，设备状态差时，样本的场景相关度较低，设备状态可包括但不限于数据采集装置200的传感器是否发生损坏，数据采集装置200是否处于异常状态等等。举例来说，数据采集装置200是园区机器人，那么异常状态可以包括园区机器人的运动路线发生严重偏移。
步骤S207：数据采集装置200根据每个数据的场景相关度确定样本集。该步骤可以由图1实施例中的上传单元204实现。
可选地，样本集可以包括场景相关度高于阈值的数据，数据采集装置200将场景相关度高于阈值的数据上传至服务器300，或者，样本集中的数据是数据采集装置200按照数据的场景相关度对数据进行排序后获得的，场景相关度高的数据的上传顺序早于场景相关度低的数据，具体可根据用户的采集策略确定。举例来说，若用户的采集策略中没有设置优先级信息，此时可以将场景相关度高于阈值的数据统一上传至服务器300，若用户采集策略中设置有优先级信息，此时可以按照数据的场景相关度对数据进行排序，场景相关度高的数据被优先上传至服务器300。应理解，上述举例用于说明，本申请不作具体限定。
可选地，数据采集装置200可以先将样本集存储于数据采集装置200的存储器中，该存储器与服务器300建立通信连接，使得服务器300可以基于用户的上报策略获取存储器中的样本集，其中，上述存储器可以是可移动存储介质，比如数据采集装置200的U盘、移动硬盘、软盘、光盘、存储卡等等，本申请不作具体限定。
可选地，数据采集装置200也可以对样本集进行标注，获得样本集中每个数据的标签，该标签可以是人工标注的标签也可以是机器标注的标签，具体可根据数据采集装置200的处理能力决定，本申请不作具体限定，
在一实施例中，数据采集装置200确定多个数据中每个数据的场景相关度之后，可以通过客户端100向用户显示每个数据的场景相关度，用户可以根据业务需求选择样本集中的数据，然后将样本集上传至服务器300，本申请不作具体限定。
步骤S208：数据采集装置200上传样本集至服务器300。该步骤可以由图1实施例中的上传单元204实现。
步骤S209：服务器300使用样本集训练模型，获得训练结果。该步骤可以由图1实施例中的模型训练单元301实现。
可选地，若服务器300接收到的样本集是未标注的样本集，也就是说，数据采集装置200没有对样本集进行标注，服务器300也可以对样本集进行标注。具体实现中，可以是人工标注也可以是机器标注，本申请不作具体限定。
服务器300可以使用上述样本集对业务模型进行训练，获得训练结果。具体实现中，上述业务模型是用户正在训练的模型，模型可以是机器学习模型，具体可以是人工神经网络模型、贝叶斯网络模型、决策树模型、向量机模型、随机森林模型、关联规则模型中的一种或者多种，还可以是其他类型的机器学习模型，这里不一一举例说明。
步骤S210：服务器300根据训练结果确定新的采集策略。该步骤可以由图1实施例中的策略更新单元302实现。
具体实现中，业务模型是用户正在训练的模型，该模型的训练过程可以包括多个训练阶段，每个训练阶段所需的样本的采集策略可能存在差异，服务器300可以存储业务模型每个训练阶段所需的采集策略，根据业务模型当前的训练结果，确定下一个训练阶段所需要的采集策略，获得更新后的采集策略。
举例来说，业务模型下一个训练阶段为增量学习阶段，在增量学习阶段需求更多难例样本，此时服务器300可以根据增量学习阶段的采集策略获得更新后的采集策略，将更新后的采集策略发送给数据采集装置200，使得数据采集装置200可以根据更新后的采集策略对目标传感器采集到的新数据进行场景相关度评估。其中，更新后的采集策略可以包括优先级信息，该优先级信息包括优先采集难例样本的信息。应理解，上述举例用于说明本申请不作具体限定。
需要说明的，也可以根据业务模型的训练结果和当前样本的数据维度，获取新的采集策略，其中，新的采集策略用于获取关联数据，该关联数据的数据维度与当前样本的数据维度不同，比如当前样本是单模 态数据，关联数据是多模态数据。举例来说，当前样本的数据维度是单模态的数据，比如只有摄像头采集的视频帧，业务模型为识别消防栓的模型，模型的训练结果显示模型预测的准确率较低，此时需要更多的样本，服务器300可以基于此获取关联数据，比如激光采集的样本以及雷达采集的样本，关联样本可以提高模型的预测能力。应理解，上述举例用于说明，本申请不作具体限定。
步骤S211：服务器300向数据采集装置200发送新的采集策略。该步骤可以由图1实施例中的策略更新单元302实现。
步骤S212：客户端100向数据采集装置200发送用户输入的新的采集策略。
可选地，服务器300可以向用户显示业务模型的训练结果，接收用户输入的更新后的采集策略。当然，用户也可以通过客户端100的策略下发单元102向数据采集装置200发送新的采集策略，本申请不作具体限定。
举例来说，业务模型是训练完毕后，用户根据业务模型的训练结果，认为业务模型对A类别的样本识别能力很高，但是对B类别的样本识别能力较差，而当前业务场景正好需求业务模型拥有对B类别的样本识别能力，用户希望可以获取更多B类别的样本对业务模型进行进一步的训练。此时用户可以通过客户端100输入新的采集策略，新的采集策略中包括目标样本特征，即B类别样本的纹理特征，还可包括优先级信息，即B类别样本优先采集的信息，然后客户端100可以通过策略下发单元102将上述新的采集策略发送给数据采集装置200，使其优先采集B类别的数据至服务器300，服务器300对B类别的数据进行标注后即可获得B类别的样本。应理解，上述举例用于说明，用户也可以通过服务器300中的策略更新单元302输入新的采集策略，本申请不作具体限定。
为了使本申请的有益效果能够被更好地理解，下面集合具体的应用场景对上述步骤S201～步骤S212进行举例说明。
假设数据采集装置200为园区机器人，客户端100为用户的个人计算机，服务器300是公有云中的云服务器，用户通过公有云的控制台购买了云服务后获得远程控制服务器300的权限。该应用场景下，园区机器人在执行巡检任务中，作为园区物管人员的用户有以下业务需求：用户希望园区机器人拥有AI感知技能，使得园区机器人在园区内巡逻时，可以感知园区内环境中的各种实体。这里的AI感知技能可以是一个深度学习模型，该深度学习模型可根据园区机器人采集的各种样本进行增量学习。
首先，数据采集装置200执行步骤S201，将传感器信息上报给客户端100，客户端100可以向用户显示如图3所示的用户界面，图3是本申请提供的一种配置页面的示例图，应理解，图3用于举例说明，本申请不对用户界面进行具体限定。示例性地，图3所示的配置页面310可包括传感器信息显示区域320、场景信息输入区域330以及控件340。
示例性地，如图3所示，传感器信息显示区域320用于显示传感器信息，传感器信息可包括数据采集装置200(本应用场景下即为园区机器人)所拥有的全部传感器的名称以及参数信息，比如园区机器人的传感器可包括：图像传感器1、图像传感器2、激光传感器1、雷达传感器1等等，其中，图像传感器1的参数信息包括像素、帧率、信噪比、感光度等等，应理解，图3示例性的给出了数据采集装置200可能拥有的传感器名称和参数信息，实际应用场景中，数据采集装置200上报的传感器信息还可以包括更多类型的传感器以及更丰富的参数，这里不一一举例说明。
接着，客户端100可以执行步骤S202，接收用户输入的场景信息，并将其发送给数据采集装置200，数据采集装置200可以执行步骤S203，确定场景信息对应的目标传感器。示例性地，如图3所示，场景信息输入区域330用于供用户输入场景信息，用户可以通过图3所示的用户界面输入场景信息，假设用户选择的场景信息为“目标检测”场景，数据采集装置200接收到客户端100反馈的场景信息之后，可以根据预先存储的场景信息与目标传感器之间的对应关系，确定“目标检测”场景对应的一个或者多个目标传感器，示例性地，如图3所示，客户端100可以向用户显示“目标检测”场景对应的目标传感器为图像传感器1、图像传感器2、激光传感器1等等。当然，数据采集装置200确定目标传感器之后也可以不用通过客户端100将其反馈给用户，本申请不对此进行限定。应理解，图3示例性的给出了几种可供选择的场景信息，比如语音识别、文字识别等等，具体实现中，客户端100可以向用户提供更多类型的场景信息，这里不一一举例说明。
需要说明的，用户也可以自行上传场景信息，比如点击图3中的“自定义场景信息”控件，输入自定义的场景信息，还可以对自行上传的场景信息对应的目标传感器进行自定义，比如点击图3中的“自定义目标传感器”控件，输入该自定义场景信息对应的目标传感器。具体实现中，用户可通过API输入场景信息 和目标传感器，本申请不作具体限定。
应理解，用户通过选择或者自定义场景信息之后，可以点击控件340进行保存，客户端100可以将用户输入的场景信息发送给数据采集装置200。
进一步地，数据采集装置200可以执行步骤S204，使用目标传感器采集多个数据，在图3所示的应场景下，数据采集装置200可以使用图像传感器1、图像传感器2以及激光传感器采集多个数据，然后通过数据对齐操作对图像传感器1、图像传感器2以及激光传感器采集的多个数据进行对齐，获得同一时刻、同一场景下的多模态数据，一个模态对应一个传感器，将这些同一时刻、同一场景下的数据归纳成为集合，并且为其建立索引。具体可参考前述内容中关于步骤S204的描述，这里不重复展开赘述。
进一步地，数据采集装置200可以执行步骤S206，根据场景信息和目标传感器采集的多个数据，确定每个数据的场景相关度，具体实现中，数据采集装置可以先根据场景信息确定对应的评估模型，然后将多个数据输入评估模型，获得每个数据的场景相关度。其中，该场景信息对应的评估模型是使用评估样本集对机器学习模型进行训练后获得的，该评估样本集中的评估样本以及对应的标签是根据评估标准确定的，该评估标准包括但不限于评估样本是否包含目标、包含目标数量、样本置信度、数据采集装置的状态信息等等，还可以包括其他评估标准，具体可参考前述步骤S206中的描述，这里不一一举例说明。
在图3所示的应用场景中，评估标准可包括激光传感器采集的激光数据的置信度、视频传感器采集的图像数据的置信度、激光数据和图像数据中包含的目标数量、激光数据和图像数据之间同一个目标的匹配度、数据采集装置200的融合速度和惯性测量单元(inertial measurement unit,IMU)角速度的异常状态识别结果等等，应理解，上述评估标准用于举例说明，本申请不作具体限定。
可选地，用户也可以根据业务需求，通过客户端100输入采集策略，客户端100可以执行步骤S205，将用户输入的采集策略发送给数据采集装置200，使得数据采集装置200执行步骤S206时，可以综合用户输入的采集策略以及步骤S202输入的场景信息，多维度确认目标传感器采集的数据的场景相关度，使得步骤S207确定的样本集不仅符合用户的业务场景，而且符合用户的业务需求，提高用户的使用体验。
示例性的，如图4所示，图4是本申请提供的另一种配置页面的示例图，如图4所示该配置页面410可包括采集策略输入区域420以及控件430。由图4可知，客户端100可以向用户显示可供选择的多个采集策略类型，比如目标样本特征、目标采集条件、优先级信息、上传逻辑等等。示例性地，用户选择目标样本特征之后，客户端100可以向用户显示多种可供选择的目标，比如消防栓、灌木、电表等等，如果用户选择了消防栓作为目标，那么客户端100可以根据预先存储的消防栓的样本特征，比如消防栓的纹理特征，将其作为采集策略发送给数据采集装置200。再举例来说，用户选择优先级信息之后，客户端100可以向用户提供多种目标，用户可以根据业务需求为目标设置优先等级，比如图4中设置消防栓为优先等级1，表示用户希望数据采集装置可以优先上传消防栓的样本数据。再举例来说，用户还可以选择上传策略，比如闲时上传，用户可以定时闲时时刻，比如北京时间凌晨3点，或者，资源占用较低时上传，应理解，图4用于举例说明，本申请不作具体限定。
需要说明的，用户也可以自定义采集策略，或者，自定义每种类型的采集策略，比如自定义目标样本特征，或者自定义优先级信息，也可以自定义新的采集策略类型，本申请不作具体限定。具体实现中，用户自定义采集策略时可以通过编写API实现。用户选择或者自定义采集策略之后，可以通过控件430保存采集策略，使得客户端100将采集策略发送给数据采集装置200。
进一步地，数据采集装置200可以根据采集策略和场景信息，执行步骤S206和步骤S207，确定目标传感器所采集的多个数据中每个数据的场景相关度，从而确定样本集。确定数据场景相关度时，数据采集装置可以根据采集策略中“消防栓”的目标样本特征，即消防栓的激光纹理特征以及图像纹理特征，对采集的激光数据和图像数据进行检索，其中，激光数据与激光纹理特征之间的相似度越大，该激光数据的场景相关度越高，相反则场景相关度越低，同理，图像数据与图像纹理特征之间的相似度越大，该图像数据的场景相关度越高，相反则场景相关度越低，这里的相似度可以指的是特征匹配度。根据采集的数据的场景相关度，确定样本集，这里的样本集可以是将所有数据的场景相关度按照从大到小的顺序进行排列后获得的，比如样本集的数量为100万个数据，那么按数据的场景相关度从大到小排序后，从第1个到第100万个数据作为样本集中的数据。上述举例用于说明，本申请不作具体限定。
进一步地，数据采集装置200可执行步骤S208上传样本集，根据采集策略中的上传策略，即闲时上传策略，在用户定义的闲时时刻上传样本集至服务器300，比如北京时间3点。
最后，服务器使用用户指定的目标样本特征(消防栓)的采集策略对模型进行训练一段时间之后，根 据模型的训练结果，比如增量学习对消防栓的识别效果达到预期，此时用户可以下发新的采集策略，比如用户希望模型能够识别更多类型的样本，那么用户可以更改采集策略中目标样本特征为“灌木”或者“电表”，客户端可以执行步骤S212下发新的采集策略至数据采集装置200，数据采集装置接收到新的采集策略之后，可以根据新的采集策略确定新的样本集，将符合用户新的采集策略的样本集上传至服务器。
可以理解的，上述方案可以灵活变更用户的采集策略，快速匹配用户所需的样本集，尽可能减少数据采集和上传带来的资源浪费，同时保证了用户所需的样本集的训练效果，提高用户的使用体验。
综上可知，本申请提供了一种数据采集方法，数据采集装置可以获取用户输入的场景信息，该场景信息包括用户所需训练的模型对应的业务场景的信息，然后根据场景信息确定至少一个传感器中的目标传感器，使用目标传感器进行数据采集获得多个数据，再根据场景信息确定多个数据中每个数据的场景相关度，向用户发送用于训练上述模型的样本集，其中，样本集是根据每个数据的场景相关度确定的，使得样本集中的数据是与业务场景相关的数据，将样本集中的数据上传至服务器，可以避免与业务场景无关的数据被上传至服务器，从而减轻数据传输和存储的压力，提高模型训练效率。
图5是本申请提供的一种计算设备的结构示意图，该计算设备500是图1至图4实施例中的客户端100、数据采集装置200或者服务器300。
进一步地，计算设备500包括处理器501、存储单元502、存储介质503和通信接口504，其中，处理器501、存储单元502、存储介质503和通信接口504通过总线505进行通信，也通过无线传输等其他手段实现通信。
处理器501由至少一个通用处理器构成，例如CPU、NPU或者CPU和硬件芯片的组合。上述硬件芯片是专用集成电路(Application-Specific Integrated Circuit，ASIC)、编程逻辑器件(Programmable Logic Device，PLD)或其组合。上述PLD是复杂编程逻辑器件(Complex Programmable Logic Device，CPLD)、现场编程逻辑门阵列(Field-Programmable Gate Array，FPGA)、通用阵列逻辑(Generic Array Logic，GAL)或其任意组合。处理器501执行各种类型的数字存储指令，例如存储在存储单元502中的软件或者固件程序，它能使计算设备500提供较宽的多种服务。
具体实现中，作为一种实施例，处理器501包括一个或多个CPU，例如图5中所示的CPU0和CPU1。
在具体实现中，作为一种实施例，计算设备500也包括多个处理器，例如图5中所示的处理器501和处理器506。这些处理器中的每一个可以是一个单核处理器(single-CPU)，也可以是一个多核处理器(multi-CPU)。这里的处理器指一个或多个设备、电路、和/或用于处理数据(例如计算机程序指令)的处理核。
存储单元502用于存储程序代码，并由处理器501来控制执行，以执行上述图1-图5中任一实施例中程序运行装置、客户端或者服务端的处理步骤。程序代码中包括一个或多个软件单元。
在计算设备500为前述内容中的客户端100时，上述一个或多个软件单元是图1实施例中的场景确认单元和策略下发单元，其中，场景确认单元用于获取用户输入的场景信息，并将其发送给数据采集装置200，策略下发单元用于获取用户输入的采集策略，并将其发送给数据采集装置200。其中，场景确认单元用于执行图2实施例中的步骤S202及其可选步骤，策略下发单元用于执行图2中的步骤S205及其可选步骤，此处不再赘述。
在计算设备500为前述内容中的数据采集装置200时，上述一个或多个软件单元是图1实施例中的上报能力单元、采集单元、评估单元以及上传单元，其中，上报能力单元用于向客户端发送数据采集装置200的传感器信息，采集单元用于根据用户输入的场景信息确定目标传感器，使用目标传感器采集多个数据，评估单元用于根据场景信息和用户输入的采集策略确定多个数据的场景相关度，上传单元用于根据多个数据的场景相关度确定样本集，将样本集上传至服务器。其中，上报能力单元用于执行图2实施例中的步骤S201及其可选步骤，采集单元用于中行图2实施例中的步骤S203和步骤S204及其可选步骤，评估单元用于执行图2中的步骤S206及其可选步骤，上传单元用于执行图2中的步骤S207和步骤S208及其可选步骤，此处不再赘述。
在计算设备500为前述内容中的服务器300时，上述一个或多个软件单元是图1实施例中的模型训练单元以及策略更新单元，其中，模型训练单元用于接收数据采集装置发送的样本集，使用样本集对模型进行训练，获得训练结果，策略更新单元用于根据训练结果确定新的采策略，并将其发送给数据采集装置获得新的样本，其中，模型训练单元用于中行图2实施例中的步骤S209及其可选步骤，策略更新单元用于 执行图2中的步骤S210、步骤S211及其可选步骤，此处不再赘述。
存储单元502包括只读存储器和随机存取存储器，并向处理器501提供指令和数据。存储单元502还包括非易失性随机存取存储器。存储单元502是易失性存储器或非易失性存储器，或包括易失性和非易失性存储器两者。其中，非易失性存储器是只读存储器(read-only memory，ROM)、编程只读存储器(programmable ROM，PROM)、擦除编程只读存储器(erasable PROM，EPROM)、电擦除编程只读存储器(electrically EPROM，EEPROM)或闪存。易失性存储器是随机存取存储器(random access memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM用，例如静态随机存取存储器(static RAM，SRAM)、动态随机存取存储器(DRAM)、同步动态随机存取存储器(synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(double data date SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器(direct rambus RAM，DR RAM)。还是硬盘(hard disk)、U盘(universal serial bus，USB)、闪存(flash)、SD卡(secure digital memory Card,SD card)、记忆棒等等，硬盘是硬盘驱动器(hard disk drive,HDD)、固态硬盘(solid state disk,SSD)、机械硬盘(mechanical hard disk,HDD)等，本申请不作具体限定。
存储介质503是存储数据的载体，比如硬盘(hard disk)、U盘(universal serial bus，USB)、闪存(flash)、SD卡(secure digital memory Card,SD card)、记忆棒等等，硬盘可以是硬盘驱动器(hard disk drive,HDD)、固态硬盘(solid state disk,SSD)、机械硬盘(mechanical hard disk,HDD)等，本申请不作具体限定。
通信接口504为有线接口(例如以太网接口)，为内部接口(例如高速串行计算机扩展总线(Peripheral Component Interconnect express,PCIe)总线接口)、有线接口(例如以太网接口)或无线接口(例如蜂窝网络接口或使用无线局域网接口)，用于与其他服务器或单元进行通信。
总线505是快捷外围部件互联标准(Peripheral Component Interconnect Express，PCIe)总线，或扩展工业标准结构(extended industry standard architecture，EISA)总线、统一总线(unified bus，Ubus或UB)、计算机快速链接(compute express link，CXL)、缓存一致互联协议(cache coherent interconnect for accelerators，CCIX)等。总线505分为地址总线、数据总线、控制总线等。
总线505除包括数据总线之外，还包括电源总线、控制总线和状态信号总线等。但是为了清楚说明起见，在图中将各种总线都标为总线505。
需要说明的，图5仅仅是本申请实施例的一种可能的实现方式，实际应用中，计算设备500还包括更多或更少的部件，这里不作限制。关于本申请实施例中未示出或未描述的内容，参见前述图1-图4实施例中的相关阐述，这里不再赘述。
本申请实施例提供一种计算机集群，包括多个计算设备500，多个计算设备之间存在通信连接。其中，每个计算设备500可以是前述内容中的客户端100、数据采集装置200以及服务器300，具体实现中，客户端100、数据采集装置200以及服务器300可以分别部署于计算机集群中的不同的计算设备上，也可以部分部署于同一个计算设备，部分部署于其他计算设备上，本申请不作具体限定。
本申请实施例提供一种计算机存储介质，包括：该计算机存储介质中存储有指令；当该指令在计算设备上运行时，使得该计算设备执行上述图1至图5描述的数据采集方法。
上述实施例，全部或部分地通过软件、硬件、固件或其他任意组合来实现。当使用软件实现时，上述实施例全部或部分地以计算机程序产品的形式实现。计算机程序产品包括至少一个计算机指令。在计算机上加载或执行计算机程序指令时，全部或部分地产生按照本发明实施例的流程或功能。计算机为通用计算机、专用计算机、计算机网络、或者其他编程装置。计算机指令存储在计算机读存储介质中，或者从一个计算机读存储介质向另一个计算机读存储介质传输，例如，计算机指令从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(digital subscriber line,DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。计算机读存储介质是计算机能够存取的任何用介质或者是包含至少一个用介质集合的服务器、数据中心等数据存储节点。用介质是磁性介质(例如，软盘、硬盘、磁带)、光介质(例如，高密度数字视频光盘(digital video disc,DVD)、或者半导体介质。半导体介质是SSD。
以上，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，轻易想到各种等效的修复或替换，这些修复或替换都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。

Claims (20)

  1. 一种数据采集方法，其特征在于，所述方法应用于数据采集装置，所述方法包括：
    所述数据采集装置获取用户输入的场景信息，其中，所述场景信息包括所述用户所需训练的模型对应的业务场景信息；
    根据所述场景信息确定至少一个传感器中的目标传感器，使用所述目标传感器进行数据采集获得多个数据，所述目标传感器的数量为一个或者多个；
    根据所述场景信息确定所述多个数据中每个数据的场景相关度，所述每个数据的场景相关度用于描述所述每个数据与所述业务场景之间的相关程度；
    向所述用户发送用于训练所述模型的样本集，其中，所述样本集是根据所述每个数据的场景相关度确定的。
  2. 根据权利要求1所述的方法，其特征在于，所述样本集中的数据的场景相关度高于阈值，或者，所述样本集中的数据是根据所述多个数据的场景相关度对所述多个数据进行排序后获得的。
  3. 根据权利要求1或2所述的方法，其特征在于，在所述数据采集装置获取用户输入的场景信息之前，所述方法还包括：
    所述数据采集装置向所述用户提供传感器信息，其中，所述传感器信息包括所述数据采集装置拥有的至少一个传感器的参数信息，所述传感器信息用于供所述用户确定所述场景信息。
  4. 根据权利要求1至3任一权利要求所述的方法，其特征在于，根据所述场景信息确定所述多个数据中每个数据的场景相关度之前，所述方法还包括：
    获取所述用户输入的采集策略，其中，所述采集策略包括所述模型训练时所需的样本的目标样本特征；
    所述根据所述场景信息确定所述多个数据中每个数据的场景相关度包括：
    根据所述场景信息确定所述每个数据的第一场景相关度，根据所述采集策略确定所述每个数据的第二场景相关度，根据所述第一场景相关度和所述第二场景相关度确定所述每个数据的场景相关度，其中，所述每个数据的数据特征与所述目标样本特征之间的相似度越高，所述数据的第二场景相关度越高。
  5. 根据权利要求4所述的方法，其特征在于，所述采集策略包括目标采集条件，其中，所述样本集中数据的是在所述目标采集条件下采集的数据。
  6. 根据权利要求4或5所述的方法，其特征在于，所述采集策略包括数据的优先级信息，其中，优先级高的数据的场景相关度高于优先级低的数据的场景相关度。
  7. 根据权利要求4至6任一权利要求所述的方法，其特征在于，所述采集策略包括上传逻辑，所述上传逻辑包括实时上传、资源占用低时上传、定时上传、周期间隔上传中的任一种，所述方法还包括：根据所述上传逻辑将所述样本集上传至服务器，其中，所述服务器用于供所述用户训练所述模型。
  8. 根据权利要求4至7任一权利要求所述的方法，其特征在于，所述根据所述场景信息确定所述每个数据的第一场景相关度包括：
    确定所述场景信息对应的评估模型，使用所述评估模型对所述数据进行场景相关度评估，确定所述数据的第一场景相关度，其中，不同场景信息对应不同的评估模型。
  9. 根据权利要求1至8任一权利要求所述的方法，其特征在于，向所述用户发送用于训练所述模型的样本集之后，所述方法还包括：
    获取的新的采集策略，根据新的采集策略确定新的样本集，其中，所述新的采集策略是所述服务器使用所述样本集对所述模型训练后确定的，或者，所述新的采集策略是所述用户输入的。
  10. 一种数据采集装置，其特征在于，所述装置包括：
    采集单元，用于获取用户输入的场景信息，其中，所述场景信息包括所述用户所需训练的模型对应的业务场景信息；
    所述采集单元，用于根据所述场景信息确定所述至少一个传感器中的目标传感器，使用所述目标传感器进行数据采集获得多个数据，所述目标传感器的数量为一个或者多个；
    评估单元，用于根据所述场景信息确定所述多个数据中每个数据的场景相关度；
    上传单元，用于向所述用户发送用于训练所述模型的样本集，其中，所述样本集是根据所述每个数据的场景相关度确定的。
  11. 根据权利要求10所述的装置，其特征在于，所述样本集中的数据的场景相关度高于阈值，或者，所述样本集中的数据是根据所述多个数据的场景相关度对所述多个数据进行排序后获得的。
  12. 根据权利要求10或11所述的装置，其特征在于，所述装置包括上报能力单元，所述上报能力单元用于在所述数据采集装置获取用户输入的场景信息之前，向所述用户提供传感器信息，其中，所述传感器信息包括所述数据采集装置拥有的至少一个传感器的参数信息，所述传感器信息用于供所述用户确定所述场景信息。
  13. 根据权利要求10至12任一权利要求所述的装置，其特征在于，
    所述采集单元还用于获取所述用户输入的采集策略，其中，所述采集策略包括所述模型训练时所需的样本的目标样本特征；
    所述评估单元，用于根据所述场景信息确定所述每个数据的第一场景相关度，根据所述采集策略确定所述每个数据的第二场景相关度，根据所述第一场景相关度和所述第二场景相关度确定所述每个数据的场景相关度，其中，所述每个数据的数据特征与所述目标样本特征之间的相似度越高，所述数据的第二场景相关度越高。
  14. 根据权利要求13所述的装置，其特征在于，所述采集策略包括目标采集条件，其中，所述样本集中数据的是在所述目标采集条件下采集的数据。
  15. 根据权利要求13或14所述的装置，其特征在于，所述采集策略包括数据的优先级信息，其中，优先级高的数据的场景相关度高于优先级低的数据的场景相关度。
  16. 根据权利要求13至15任一权利要求所述的装置，其特征在于，所述采集策略包括上传逻辑，所述上传逻辑包括实时上传、资源占用低时上传、定时上传、周期间隔上传中的任一种；
    所述上传单元用于根据所述上传逻辑将所述样本集上传至服务器，其中，所述服务器用于供所述用户训练所述模型。
  17. 根据权利要求13至16任一权利要求所述的装置，其特征在于，所述评估单元，用于确定所述场景信息对应的评估模型，使用所述评估模型对所述数据进行场景相关度评估，确定所述数据的第一场景相关度，其中，不同场景信息对应不同的评估模型。
  18. 根据权利要求10至17任一权利要求所述的装置，其特征在于，所述采集单元还用于获取的新的采集策略，根据新的采集策略确定新的样本集，其中，所述新的采集策略是所述服务器使用所述样本集对所述模型训练后确定的，或者，所述新的采集策略是所述用户输入的。
  19. 一种计算设备，其特征在于，所述计算设备包括处理器和存储器，所述存储器用于存储代码，所述处理器用于执行所述代码实现如权利要求1至9任一权利要求所述的方法。
  20. 一种计算机可读存储介质，其特征在于，包括指令，当所述指令在计算设备上运行时，使得所述计算设备执行如权利要求1至9任一权利要求所述的方法。
PCT/CN2023/123300 2022-10-27 2023-10-08 一种数据采集方法、装置及相关设备 WO2024088031A1 (zh)

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