Abstract: A method for controlling the power-on or power-off of an air conditioner is provided. The method includes the following steps: determining whether a variable capacity compressor of the air conditioner is in a standby single-cylinder operation state before starting the air conditioner, where the variable capacity compressor is configured to be switchable between a single-cylinder operation state and a dual-cylinder operation state; if so, starting the air conditioner; if not, switching the variable capacity compressor to the standby single-cylinder operation state, and then starting the air conditioner. With this control method, regardless of whether the air conditioner is shut down because the power supply of the unit is cut off or because a power-off signal is received, the air conditioner can be started in a single-cylinder operation state in the next startup, so that the operation state of the cylinder is always determined when starting the air conditioner.

Abstract: Disclosed are a variable capacity compressor operation mode determination method and device, a variable capacity compressor and an air conditioner. The variable capacity compressor operation mode determination method includes: detecting a current value of a compressor at present as A1 before switching an operation mode of the compressor; detecting the current value of the compressor at present as A2 after switching the operation mode of the compressor and reaching a preset time; comparing A1 and A2, determining that the switching of the operation mode of the compressor is successful when a ratio relationship between A1 and A2 satisfies a preset condition, and determining that switching of the operation mode of the compressor is failed when the ratio relationship between A1 and A2 dissatisfies a preset condition.

Abstract: The invention relates to a method and system for selecting a least-loaded route based on a naive Bayes classifier, so that the performance of a method for selecting a least-loaded route is improved. A network snapshot records historical network status information, and a naive Bayes classifier is used to predict the potential future network blocking probability if a service connection is established along a candidate route between each node pair. A network snapshot corresponds to each service request that arrives, and records the number of busy capacity units on each link.

Abstract: The disclosure discloses a method and a device for controlling cylinder switching of a compressor, a unit and an air conditioning system. The method includes: determining whether the compressor needs to perform cylinder switching; if so, adjusting current operating frequency according to a system pressure difference so that both the adjusted operating frequency and the system pressure difference meet a cylinder switching condition of the compressor; and controlling the compressor to perform cylinder switching. At the moment, the system pressure difference and the operation frequency are stable, and would not interfere the compressor's maintaining of the single-cylinder or double-cylinder operation state, which guarantees the energy efficiency of the unit where the compressor is located, and improves the use experience of a user.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: Techniques for data sharing between a data miner and a data provider are provided. A set of public parameters is downloaded from the data miner. The public parameters are data miner parameters associated with a feature set of training sample data. A set of private parameters in the data provider can be replaced with the set of public parameters. The private parameters are data provider parameters associated with the feature set of training sample data. The private parameters are updated to provide a set of update results. The private parameters are updated based on a model parameter update algorithm associated with the data provider. The update results is uploaded to the data miner.

Abstract: A method and apparatus for controlling a compressor to switch a cylinder mode and an air conditioner system. The method for controlling a compressor to switch a cylinder mode includes: determining that a compressor is required to switch from a single-cylinder operation mode to a two-cylinder operation mode; determining whether a system pressure differential is within a first preset system pressure differential interval; if not, adjusting a system control parameter such that the system pressure differential is within the first preset system pressure differential interval; and controlling the compressor to switch from the single-cylinder operation mode to the two-cylinder operation mode.

Abstract: A feature extraction is performed on transaction data to obtain a user classification feature and a transaction classification feature. A first dimension feature is constructed based on the user classification feature and the transaction classification feature. A dimension reduction processing is performed on the first dimension feature to obtain a second dimension feature. A probability that the transaction data relates to a risky transaction is determined based on a decision classification of the second dimension feature, where the decision classification is based on a pre-trained deep forest network including a plurality of levels of decision tree forest sets.

Abstract: Techniques for identifying fraudulent transactions are described. In one example method, an operation sequence and time difference information associated with a transaction are identified by a server. A probability that the transaction is a fraudulent transaction is predicted based on a result provided by a deep learning network, where the deep learning network is trained to predict fraudulent transactions based on operation sequences and time differences associated with a plurality of transaction samples, and where the deep learning network provides the result in response to input including the operation sequence and the time difference information associated with the transaction.

Abstract: This specification describes techniques for detecting abnormal data in a data set. One example method includes obtaining, by a data processing platform, a to-be-validated data group including to-be-validated data corresponding to a predetermined feature; obtaining, by the data processing platform, a comparison data group including historical data associated with the to-be-validated data group, wherein the historical and the to-be-validated data are from a same data source; performing, by the data processing platform, a two-group significance test on the to-be-validated data group and the comparison data group to generate a test result; and determining, by the data processing platform, whether there is abnormal data in the to-be-validated data group based on the test result.

Abstract: This specification describes techniques for detecting abnormal data in a data set. One example method includes obtaining, by a data processing platform, a to-be-validated data group including to-be-validated data corresponding to a predetermined feature; obtaining, by the data processing platform, a comparison data group including historical data associated with the to-be-validated data group, wherein the historical and the to-be-validated data are from a same data source; performing, by the data processing platform, a two-group significance test on the to-be-validated data group and the comparison data group to generate a test result; and determining, by the data processing platform, whether there is abnormal data in the to-be-validated data group based on the test result.

Abstract: This specification describes techniques for detecting abnormal data in a data set. One example method includes obtaining, by a data processing platform, a to-be-validated data group including to-be-validated data corresponding to a predetermined feature; obtaining, by the data processing platform, a comparison data group including historical data associated with the to-be-validated data group, wherein the historical and the to-be-validated data are from a same data source; performing, by the data processing platform, a two-group significance test on the to-be-validated data group and the comparison data group to generate a test result; and determining, by the data processing platform, whether there is abnormal data in the to-be-validated data group based on the test result.

Abstract: A method and a device for controlling a capacity change of a compressor, and a smart home appliance. The method includes: determining whether a capacity of the compressor is required to be changed; if yes, determining an intermediate frequency; changing the capacity of the compressor while maintaining the operating frequency of the compressor at the intermediate frequency; and the maintaining the operating frequency of the compressor at the intermediate frequency reduces a sudden change in output of the compressor after the changing the capacity.

Abstract: Techniques for identifying fraudulent transactions are described. In one example method, an operation sequence and time difference information associated with a transaction are identified by a server. A probability that the transaction is a fraudulent transaction is predicted based on a result provided by a deep learning network, where the deep learning network is trained to predict fraudulent transactions based on operation sequences and time differences associated with a plurality of transaction samples, and where the deep learning network provides the result in response to input including the operation sequence and the time difference information associated with the transaction.

Abstract: The present disclosure discloses a networking method device and terminal for an air conditioning unit. The networking method includes: detecting an outside ambient temperature after a target networking indoor unit is started; determining a target operating mode of the target networking indoor unit according to the outside ambient temperature; detecting a system pressure value of a multi-split air conditioning system after an operating duration of the target networking indoor unit in the target operating mode is greater than a preset operating duration; and determining whether the target networking indoor unit belongs to a target networking system according to the system pressure value.

Abstract: An index anomaly detection method includes: acquiring data of each of monitoring points, contained in a period of time, of a monitored index; extracting a mean value and a variance of the data of the monitoring points using a Gaussian model; calculating, according to the mean value and the variance of the data of the monitoring points, probabilities of occurrence of the data of the monitoring points, respectively; calculating, according to the respectively calculated probabilities, joint probabilities of occurrence of the data of the monitoring points contained in respective windows divided from the period of time; and detecting, according to the joint probabilities corresponding to the respective windows, whether the monitored index is abnormal.

Abstract: The present specification discloses a method and an apparatus for training a transaction feature generation model, and a method and an apparatus for generating a transaction feature. The method for generating a transaction feature can include the following: obtaining a target dataset, where the target dataset includes some pieces of transaction data; obtaining some original features of the transaction data and determining one or more combination methods for the original features; determining a feature vector of a new feature that is obtained by combining the original features based on each combination method; inputting the feature vector into a trained transaction feature generation model, and outputting a prediction result of the new feature; and selecting some new features whose prediction results meet a specified condition as transaction features generated for the target dataset.

Abstract: Implementations of the present specification provide a method for determining a computer-executed ensemble model. The method includes: obtaining a current ensemble model and a plurality of untrained candidate submodels; integrating each of the plurality of candidate submodels into the current ensemble model to obtain a plurality of first candidate ensemble models; training at least the plurality of first candidate ensemble models to obtain a plurality of second candidate ensemble models after this training; performing performance evaluation on each of the plurality of second candidate ensemble models to obtain corresponding performance evaluation results; determining, based on the performance evaluation results, an optimal candidate ensemble model with optimal performance from the plurality of second candidate ensemble models; and updating the current ensemble model with the optimal candidate ensemble model if the performance of the optimal candidate ensemble model satisfies a predetermined condition.

Abstract: Embodiments of the specification provide a URL abnormal field location method. One exemplary method comprising: obtaining a plurality of URL samples comprising a plurality of abnormal URL samples and a plurality of normal URL samples; for each of the plurality of URL samples, obtaining a plurality of feature vectors representing the plurality of fields of the URL sample; assigning a plurality of training labels to the plurality of feature vectors of each of the plurality of URL samples; obtaining, based on a classifier, a plurality of predicted labels for the plurality of feature vectors of each of the plurality of URL samples; updating the plurality of training labels based on the plurality of predicted labels; training the classifier with the plurality of updated training labels; and deploying the trained classifier to identify an abnormal field in a URL.

Abstract: The application discloses an optical network planning method for asymmetric traffic transmission over a multi-core fiber optical network and a network using the same. The method comprises: acquiring an asymmetric traffic demand over a multi-core fiber optical network to obtain a target service; establishing a corresponding route depending on the target service, and selecting cores in a multi-core fiber and allocating corresponding frequency slots in an interleaving and counter-propagating manner to each link along the route to optimize optical network planning and design. With the method provided by the application, through selecting cores in a multi-core fiber and allocating corresponding frequency slots in an interleaving and counter-propagating manner to each link along the route, the inter-core crosstalk is suppressed and network capacity efficiency is increased, thereby optimizing optical network planning and design for traffic transmission over the multi-core fiber optical network.