Weak current system management method and device, computer equipment and storage medium

Abstract

The invention discloses a weak current system management method and device, equipment and a storage medium, and the method comprises the steps: obtaining initial data through a plurality of sensors in a weak current system; preprocessing the initial data to obtain intermediate data; extracting a first key feature from the intermediate data, wherein the first key feature is used for representing an environment state and a user behavior; obtaining a second key feature, and fusing the first key feature and the second key feature to obtain a target feature; the target features are analyzed through a machine learning algorithm, an analysis result is obtained, and the analysis result is used for representing requirements of a period of time in the future; and determining a management strategy according to the analysis result. Therefore, dynamic management can be realized according to the actual use condition of the personnel in the building and the environment change.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a management method and device for a weak current system, a computer device, and a storage medium. Background Art

[0002] In the current field of intelligent buildings, the energy management of weak current systems is an important research direction. Existing building weak current systems include subsystems such as lighting, air conditioning, elevators, and security, and these systems account for a certain proportion of building energy consumption.

[0003] However, the inventors have found through research that existing weak current systems lack real-time monitoring and intelligent regulation capabilities and cannot achieve dynamic management based on the actual usage of building occupants and environmental changes.

[0004] Therefore, there is an urgent need for a new management method for weak current systems to solve the above problems. Summary of the Invention

[0005] To overcome the above defects, a new management method for a weak current system proposed in this application has real-time monitoring and intelligent regulation capabilities and can achieve dynamic management based on the actual usage of building occupants and environmental changes.

[0006] To solve the above problems, in a first aspect, this application provides a management method for a weak current system, and the method includes: obtaining initial data through multiple sensors in the weak current system; preprocessing the initial data to obtain intermediate data; extracting a first key feature from the intermediate data, where the first key feature is used to characterize the environmental state and user behavior; obtaining a second key feature and fusing the first key feature and the second key feature to obtain a target feature; analyzing the target feature through a machine learning algorithm to obtain an analysis result, where the analysis result is used to characterize the requirements for a future period of time; and determining a management strategy according to the analysis result.

[0007] In an optional embodiment, the sensors include at least one of the following: a temperature sensor, a humidity sensor, a light sensor, a personnel detection sensor; and the method further includes: preliminarily processing the initial data through the sensors.

[0008] In an optional embodiment, the preprocessing includes at least one of the following: data cleaning, outlier detection, and normalization processing; where the mathematical formula corresponding to the normalization processing is as follows:

[0009]

[0010] where x norm represents the normalized data, x represents the original data, x minand x max respectively represent the minimum value and the maximum value in the original data.

[0011] In an optional embodiment, the analysis result includes the probability of a device failure in a future period of time, and determining a management strategy according to the analysis result includes: when the probability exceeds a threshold, triggering an early warning mechanism.

[0012] In an optional embodiment, triggering the early warning mechanism includes: generating early warning information based on the analysis result, where the early warning information includes: the failure, the impact caused by the failure, and recommended maintenance measures; and sending the early warning information to a property terminal.

[0013] In an optional embodiment, determining a management strategy according to the analysis result includes: determining a first operating parameter of the lighting system according to the analysis result, and controlling the lighting system based on the first operating parameter; where the first operating parameter includes lighting brightness, and the lighting brightness is determined according to ambient light intensity, time factors, and the usage pattern of users.

[0014] In an optional embodiment, determining a management strategy according to the analysis result further includes: determining a second operating parameter of the air conditioning system according to the analysis result, and controlling the air conditioning system based on the second operating parameter; where the second operating parameter includes air conditioning temperature, and the air conditioning temperature is determined according to indoor temperature, outdoor temperature, personnel activity intensity, and historical energy consumption data.

[0015] In a second aspect, the present application further provides a management device for a weak current system, where the device includes: a data acquisition module for acquiring initial data through multiple sensors in the weak current system; a preprocessing module for preprocessing the initial data to obtain intermediate data; a feature extraction module for extracting a first key feature from the intermediate data, where the first key feature is used to characterize the environmental state and user behavior; a feature fusion module for acquiring a second key feature and fusing the first key feature and the second key feature to obtain a target feature; an analysis module for analyzing the target feature through a machine learning algorithm to obtain an analysis result, where the analysis result is used to characterize the requirements in a future period of time; and a management module for determining a management strategy according to the analysis result.

[0016] In a third aspect, the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the program can implement the steps of any one of the above methods when executed by the processor.

[0017] Fourthly, the present application also provides a storage medium for storing a computer program, and the program, when executed by a computer or a processor, implements the steps of the method described in any one of the above.

[0018] Compared with the prior art, the technical solution of the embodiment of the present application has the following beneficial effects:

[0019] The management method of the weak current system in the embodiment of the present application can collect data in real time through multiple sensors deployed inside the weak current system, preprocess the initial data according to a unified preprocessing logic, extract the features in the data collected this time after preprocessing, and fuse them with the features of historical data to more comprehensively analyze the current environmental state and user behavior of the weak current system. Then, use machine learning algorithms to deeply analyze the fused data to predict the needs of the weak current system in the next period of time, and based on this, determine the control strategies for each subsystem in the weak current system. Thus, an automatic management solution for the weak current system can be realized, and dynamic management can be achieved according to the actual usage of the personnel in the building and environmental changes.

[0020] Furthermore, through the early warning and remote management mechanism provided by the present application, not only can potential fault risks be detected in advance, but also it can help property management personnel make more accurate maintenance decisions, thereby improving the operation and maintenance efficiency of intelligent buildings and reducing the losses caused by faults.

[0021] Furthermore, through the implementation of the intelligent regulation strategy provided by the present application, the present application can ensure that the operation of the building weak current system is both energy-saving and efficient, and can meet the actual needs of users, thereby improving the energy use efficiency while also enhancing the living and working experience of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic flowchart of a management method for a weak current system according to an embodiment of the present application;

[0023] Figure 2 is a schematic structural diagram of a management device for a weak current system according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] As described in the background art, the weak current system in current intelligent buildings lacks real-time monitoring and intelligent regulation capabilities and cannot achieve dynamic management according to the actual usage of the personnel in the building and environmental changes.

[0026] In addition, the inventors have also found through research that the low-voltage power systems in existing intelligent buildings lack effective data analysis and fault warning mechanisms, resulting in energy waste and low operation and maintenance efficiency. Existing technologies such as sensor network-based monitoring systems and rule-based control systems often operate independently, lacking integration and intelligence levels, and unable to achieve efficient management. At the same time, the technology has not fully considered user usage habits and comfort requirements, resulting in conflicts between energy-saving measures and user needs.

[0027] To solve the above problems, the present application provides a new management method for low-voltage power systems, which can be applied to intelligent buildings. It can monitor the operating status of the low-voltage power system in the building in real time and intelligently manage energy distribution. The present application aims to solve the problems of low energy management efficiency, poor comfort, and lack of warning mechanisms in the prior art, and achieve the following goals: reducing energy waste in the building's low-voltage power system; improving energy use efficiency; enhancing the comfort of living and working; providing remote data analysis and fault warning functions for convenient property management and maintenance.

[0028] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes the implementation manners and effects of the management method and device for low-voltage power systems, computer equipment, and storage medium proposed according to the present invention in detail with reference to the accompanying drawings and preferred embodiments.

[0029] In the first aspect, please refer to Figure 1 , the present application provides a management method for a low-voltage power system. The low-voltage power system may refer to the low-voltage power system in an intelligent building, and the low-voltage power system may include subsystems such as lighting, air conditioning, elevators, and security.

[0030] The management method of the low-voltage power system may include steps S101 to S106, and each step is described in detail as follows.

[0031] Step S101, obtain initial data through a plurality of sensors in the low-voltage power system.

[0032] In an optional embodiment of step S101, the sensors include at least one of the following: temperature sensors, humidity sensors, light sensors, personnel detection sensors, and so on.

[0033] Specifically, a series of sensors can be deployed in key areas of the intelligent building to establish a comprehensive sensor network for precisely monitoring the internal environmental parameters of the intelligent building. These sensors can cover temperature sensors, humidity sensors, light sensors, and occupancy sensors, etc. Among them, the temperature sensor is responsible for capturing and transmitting the indoor temperature data in real time, the humidity sensor is used to capture the humidity data in real time, the light sensor is used to monitor the light intensity inside and outside the room, and the occupancy sensor is used to sense whether there are people activities in a specific area. The arrangement of these sensors follows a certain strategy to ensure the comprehensiveness and accuracy of data collection, thus providing reliable basic information for subsequent data analysis and equipment status monitoring.

[0034] The following are the key details in the deployment process: The selection of sensors needs to be determined according to the specific environment and requirements of the intelligent building to ensure that it can adapt to different environmental conditions; The installation positions of the sensors are carefully designed to cover the monitoring area to the greatest extent while avoiding mutual interference; The data transmission between the sensors and the central processing unit uses wireless communication technology to reduce the wiring complexity and improve the flexibility of the system.

[0035] In a specific example, the sensors in the intelligent building can be arranged according to the following steps:

[0036] Step 1, according to the building floor plan and functional area planning, determine the distribution map of the sensors; Step 2, install temperature and / or humidity sensors in each functional area, such as meeting rooms, offices, and corridors; Step 3, install light sensors near the windows to monitor the change of natural light; Step 4, install occupancy sensors in high-frequency personnel activity areas, such as the entrance hall and rest areas; Step 5, after the installation is completed, debug the sensors to ensure that they work properly and can accurately transmit data.

[0037] Step S102, preprocess the initial data to obtain intermediate data.

[0038] In an optional embodiment of step S102, the preprocessing includes at least one of the following: data cleaning, outlier detection, and normalization processing. Data cleaning aims to remove useless data; Outlier detection is used to identify and correct possible data errors; And normalization processing is to adjust data in different ranges to a unified scale for subsequent analysis.

[0039] Among them, the mathematical formula corresponding to the normalization processing is as follows:

[0040]

[0041] Among them, x norm represents the normalized data, x represents the original data, x min and xmax respectively represent the minimum value and the maximum value in the original data.

[0042] In the first specific embodiment of step S102, the preprocessing operation is performed by each sensor to obtain intermediate data. Then, each sensor sends the intermediate data obtained by itself to the central processing unit, and the subsequent steps are executed by the central processing unit. Thus, each sensor can preprocess the initial data collected by itself based on the overall management strategy of the weak current system, which can reduce the data sending pressure of the sensor to the central processing unit.

[0043] Furthermore, multiple threads can be established at each sensor end. Some threads are responsible for data collection, some threads are responsible for preprocessing, and the remaining threads are responsible for data transmission. Thus, distributed operations of data collection, data processing, and data transmission can be realized.

[0044] In the second specific embodiment of step S102, after each sensor collects the initial data, the initial data is directly sent to the central processing unit, and the central processing unit performs the preprocessing operation to obtain intermediate data. Thus, it can be ensured that the central processing unit obtains initial data with more information, which is convenient for the central processing unit to extract different information from the initial data according to different requirements.

[0045] Furthermore, before each sensor sends the initial data to the central processing unit after collecting the initial data, each sensor can also perform a preliminary verification on the initial data. For example, the sensor can perform preliminary data cleaning and formatting operations on the initial data to ensure the accuracy and transmission efficiency of the data.

[0046] It should be noted that each sensor can perform real-time collection of environmental data at a predetermined time interval. Once the data is captured, the sensor immediately performs preliminary data cleaning and formatting operations to ensure the accuracy and transmission efficiency of the data. Subsequently, the data is securely encapsulated in a data packet and quickly transmitted to the central processing unit through a selected wireless communication protocol (such as Wi-Fi or ZigBee). During the data transmission process, encryption and integrity verification measures can be implemented to ensure the secure transmission of the data in the wireless network.

[0047] In a specific example, the data collection and transmission of each sensor can include the following steps:

[0048] Step 1, each sensor is programmed to work at a specific sampling frequency; for example, the temperature sensor and the humidity sensor can be set to collect data once per minute, while the light sensor and the occupancy sensor may adjust the sampling frequency as needed. Step 2, the initially collected data is first preliminarily verified inside the sensor, such as data cleaning and formatting, to ensure that the transmitted data is accurate and useful. Step 3, each sensor uses relevant encryption algorithms to encrypt and package the initially verified data to obtain an encrypted data packet; Step 4, each sensor sends the encrypted data packet to the central processing unit through a wireless communication network, such as Wi-Fi, Bluetooth, or ZigBee, etc. Step 5, after receiving the data packet, the central processing unit decrypts it to obtain the initially verified data. Step 6, the central processing unit preprocesses this data and stores the preprocessed data.

[0049] Step S103, extract the first key feature from the intermediate data, and the first key feature is used to characterize the environmental state and user behavior.

[0050] Among them, the first key feature is the feature corresponding to the environmental state and user behavior of the intelligent building reflected by the intermediate data.

[0051] In a specific embodiment, the first key feature may include whether there is someone in the target area, the indoor temperature, outdoor temperature, and indoor humidity of the target area, and the light intensity of the target area, etc. within a certain period of time.

[0052] Step S104, obtain the second key feature, and fuse the first key feature and the second key feature to obtain the target feature.

[0053] Among them, the second key feature is the feature corresponding to the environmental state and user behavior of the intelligent building reflected by the historical data. The historical data is the intermediate data obtained from each sensor in a previous period of time.

[0054] In order to analyze the current intermediate data more accurately, the system will fuse the first key feature in the intermediate data with the second key feature in the historical data.

[0055] In addition, it can also be analyzed in combination with the user's usage habits. The acquisition of the user's usage habits is obtained by long-term monitoring of the user's interaction behavior with the building's weak current system and using data mining techniques for analysis.

[0056] In a specific embodiment of step S104, after the central processing unit receives the intermediate data each time, according to the preset feature extraction logic, it extracts the features corresponding to the environmental state and user behavior of the intelligent building reflected in the intermediate data, and stores these features and their corresponding time tags in the database. When the central processing unit needs to obtain the features within a certain period of time, it can obtain the corresponding features from the database according to the time tags as the second key features.

[0057] Step S105, analyze the target features through a machine learning algorithm to obtain an analysis result, and the analysis result is used to characterize the requirements for a future period of time.

[0058] Among them, the machine learning algorithm is a machine learning algorithm based on time series analysis, such as Long Short-Term Memory Network (LSTM). This is because the initial data and intermediate data are usually time series data, and LSTM is suitable for dealing with and predicting the long-term dependence problems in time series data, so as to better analyze the target features, and thus predict the requirements of each area (such as offices, etc.) in the intelligent building for a future period of time according to the target features, and obtain the analysis result accordingly.

[0059] Among them, the core formula of the LSTM algorithm can be seen in the following representation:

[0060] f(t) = σ(W f ·[h t―1 , x t + b f )

[0061] f(t): is the output of the forget gate at time step t. σ: activation function, usually the Sigmoid function, whose role is to output a value between 0 and 1, representing the proportion of forgetting. W f is the weight matrix of the forget gate. [h t―1 , x t is the concatenated vector containing the previous hidden state h t―1 and the current input x t . b f is the bias term of the forget gate.

[0062] i(t) = σ(W i ·[h t―1 , x t + b i )

[0063] i(t): is the output of the input gate at time step t. W i is the weight matrix of the input gate. b i is the bias term of the input gate.

[0064]

[0065] is a candidate memory cell at time step t. The tanh hyperbolic tangent function, which serves as an activation function, outputs values between -1 and 1. W C is the weight matrix of the memory cell. b C is the bias term of the memory cell.

[0066]

[0067] C t is the actual memory cell state at time step t. C t―1 is the memory cell state at the previous time step t-1. This formula indicates that the new memory cell state is the forgotten part of the old state plus the input part of the new candidate state.

[0068] o(t) = σ(W o ·[h t―1 , x t + b o )

[0069] o(t) is the output of the output gate at time step t. W o is the weight matrix of the output gate. b o is the bias term of the output gate.

[0070] h t = o(t) · tanh(C t )

[0071] h t is the hidden state output at time step t, which is also the final output of the LSTM cell. This formula shows that the hidden state is calculated based on the activation value of the output gate and the memory cell state.

[0072] Among them, f(t), i(t), and o(t) respectively represent the activation functions of the forget gate, input gate, and output gate, and C t respectively represent the candidate memory unit and the current memory unit, h t represents the hidden state.

[0073] The above formulas describe the state update process of each gate and memory cell in the LSTM (Long Short-Term Memory) cell, which is the key mechanism for LSTM to learn and remember long-term dependencies.

[0074] Step S106, determine the management strategy according to the analysis result.

[0075] Among them, the management strategy may include strategies for controlling each subsystem (such as air conditioning system, lighting system, security system, etc.) in the intelligent building.

[0076] Based on Figure 1 the provided management method, it is possible to collect data in real time through multiple sensors deployed within the low-voltage power system, preprocess the initial data according to a unified preprocessing logic, extract the features in the currently collected data after preprocessing, and fuse them with the features of historical data to more comprehensively analyze the current environmental state and user behavior of the low-voltage power system. Then, use machine learning algorithms to deeply analyze the fused data to predict the requirements of the low-voltage power system in the future for a period of time, and based on this, determine the control strategies for each subsystem within the low-voltage power system. Thus, an automatic management solution for the low-voltage power system can be realized, and dynamic management can be achieved according to the actual usage situation of the personnel in the building and environmental changes.

[0077] In an optional embodiment, the analysis result includes the probability of a device failure within a future period of time. Step S106 of determining the management strategy according to the analysis result may include: when the probability exceeds the threshold, triggering an early warning mechanism.

[0078] Furthermore, a prediction model can be constructed based on the analysis result and historical data to identify possible failure modes in the low-voltage power system. This model takes into account various factors, such as the running time, maintenance history, environmental conditions, etc. of each device in the low-voltage power system, to improve the accuracy of prediction.

[0079] The following is an example of a mathematical model for early warning analysis:

[0080] P(failure|feature vector) = f(feature vector)

[0081] where P(failure|feature vector) represents the probability of a failure occurring under the given feature vector condition, and f(feature vector) is a function that calculates the failure probability based on the feature vector. The feature vector is obtained by vectorizing each factor.

[0082] In a specific example, after the machine learning algorithm obtains the analysis result, the analysis result is input into the prediction model, and the prediction model will output the probability of a device failure within a future period of time. If the predicted failure probability exceeds the preset threshold, the system will trigger an early warning mechanism.

[0083] In an optional embodiment, the triggering of the early warning mechanism includes: the failure, the impact caused by the failure, and the recommended maintenance measures; and sending the early warning information to the property terminal.

[0084] That is, after the system triggers the early warning mechanism, it will include the failure predicted by the analysis result, the possible impact of this failure, and the recommended maintenance measures to solve this failure.

[0085] Optionally, the system runs the prediction model every day and monitors the status of each device. Among them, the warning information for the intelligent building can be sent to the property management personnel via email, text message or other communication methods, and the warning information can be accompanied by detailed fault analysis and maintenance suggestions.

[0086] Furthermore, after receiving the warning information, the property management personnel can take corresponding preventive measures or arrange maintenance work according to the provided suggestions.

[0087] Furthermore, the system can also track the maintenance activities after the warning in order to continuously optimize the prediction model.

[0088] Through this warning and remote management mechanism, not only can potential fault risks be detected in advance, but it can also help the property management personnel make more accurate maintenance decisions, thereby improving the operation and maintenance efficiency of the intelligent building and reducing the losses caused by faults.

[0089] In an optional embodiment, Figure 1 The determining of the management strategy according to the analysis result in step S106 may include: determining the first operating parameter of the lighting system according to the analysis result, and controlling the lighting system based on the first operating parameter; wherein, the first operating parameter includes the lighting brightness, and the lighting brightness is determined according to the ambient light intensity, time factor and the user's usage pattern.

[0090] Among them, the first operating parameter is a parameter used to adjust the operating state of the lighting system, which includes but is not limited to the lighting brightness, and may also include parameters such as the lighting duration, the type of lighting source (such as warm yellow light, white light, etc.).

[0091] The following provides an example of adjusting the lighting system. When the ambient light is lower than the preset threshold, the system will automatically increase the lighting brightness; on the contrary, when the ambient light is sufficient, the system will reduce the lighting brightness or turn off some lamps.

[0092] The corresponding control logic can be expressed by the following formula:

[0093] L = f(I, T, U); where L represents the lighting brightness, I represents the ambient light intensity, T represents the time factor (such as day or night), and U represents the user's usage pattern.

[0094] In an optional embodiment, Figure 1 The determining of the management strategy according to the analysis result in step S106 may include: determining the second operating parameter of the air conditioning system according to the analysis result, and controlling the air conditioning system based on the second operating parameter; wherein, the second operating parameter includes the air conditioning temperature, and the air conditioning temperature is determined according to the indoor temperature, outdoor temperature, personnel activity intensity, historical energy consumption data.

[0095] Among them, the second operating parameter is a parameter used to adjust the operating state of the air conditioning system, which includes but is not limited to the air conditioning temperature, and may also include parameters such as the intensity of cold / hot air, whether to turn on ventilation, and so on.

[0096] The following provides an example of adjusting an air conditioning system. The system will intelligently adjust the air conditioning temperature according to the indoor-outdoor temperature difference, personnel activity conditions, and historical energy consumption data. The goal is to minimize energy consumption while ensuring user comfort.

[0097] The mathematical model of the regulation strategy can be expressed as:

[0098] T set = g(T in , T out , P, H)

[0099] Among them, T set represents the set air conditioning temperature, T in represents the indoor temperature, T out represents the outdoor temperature, P represents the personnel activity intensity, and H represents the historical energy consumption data.

[0100] In a specific embodiment, after obtaining the analysis result, the optimal operating parameters (i.e., the first operating parameter and the second operating parameter) of the lighting system and the air conditioning system can be calculated according to the preset regulation logic. Further, corresponding control instructions can be sent to the lighting system and the air conditioning system according to the operating parameters. For example, for the lighting system, an instruction to adjust the brightness will be sent to the lighting controller; for the air conditioning system, a new temperature setting value will be sent to the air conditioning controller. After receiving the instructions, the controllers of the lighting system and the air conditioning system will adjust the operating states of the corresponding devices. In addition, the effects after adjusting the lighting system and the air conditioning system will be continuously monitored, and fine-tuning will be performed according to the feedback to ensure the energy-saving effect and user comfort.

[0101] By implementing this intelligent regulation strategy, the present application can ensure that the operation of the building's weak current system is both energy-saving and efficient, and can meet the actual needs of users, thereby improving the energy use efficiency while also enhancing the user's living and working experience.

[0102] In the second aspect of the present application, please refer to Figure 2 , and there is also provided a management device 20 for a weak current system, including:

[0103] A data acquisition module 201 for acquiring initial data through multiple sensors in the weak current system;

[0104] A preprocessing module 202 for preprocessing the initial data to obtain intermediate data;

[0105] The feature extraction module 203 is configured to extract first key features from the intermediate data, and the first key features are used to characterize the environmental state and user behavior.

[0106] The feature fusion module 204 is configured to obtain second key features, and fuse the first key features and the second key features to obtain target features.

[0107] The analysis module 205 is configured to analyze the target features through a machine learning algorithm to obtain an analysis result, and the analysis result is used to characterize the requirements for a future period of time.

[0108] The management module 206 is configured to determine a management strategy according to the analysis result.

[0109] In an optional embodiment, the sensors include at least one of the following: a temperature sensor, a humidity sensor, a light sensor, a personnel detection sensor; the management device 20 of the low-voltage power system may further include: a preliminary processing module, configured to perform preliminary processing on the initial data through the sensors.

[0110] In an optional embodiment, the preprocessing includes at least one of the following: data cleaning, outlier detection, and normalization processing; wherein, the mathematical formula corresponding to the normalization processing is as follows:

[0111]

[0112] wherein, x norm represents the normalized data, x represents the original data, x min and x max respectively represent the minimum value and the maximum value in the original data.

[0113] In an optional embodiment, the analysis result includes the probability of a device failure in a future period of time, and the management module 206 may further be configured to: when the probability exceeds a threshold, trigger an early warning mechanism.

[0114] In an optional embodiment, the triggering of the early warning mechanism includes: generating early warning information based on the analysis result, where the early warning information includes: the failure, the impact caused by the failure, and recommended maintenance measures; and sending the early warning information to the property terminal.

[0115] In an optional embodiment, the management module 206 may further be configured to: determine first operating parameters of the lighting system according to the analysis result, and control the lighting system based on the first operating parameters; wherein, the first operating parameters include lighting brightness, and the lighting brightness is determined according to the environmental light intensity, time factors, and the user's usage pattern.

[0116] In an optional embodiment, the management module 206 may further be configured to: determine a second operating parameter of the air-conditioning system according to the analysis result, and control the air-conditioning system based on the second operating parameter; wherein, the second operating parameter includes an air-conditioning temperature, and the air-conditioning temperature is determined according to the indoor temperature, outdoor temperature, personnel activity intensity, and historical energy consumption data.

[0117] For more content about the working principle and working mode of the management device 20 of the weak current system, reference may be made to the relevant description of Figure 1 the management method of the weak current system, which will not be elaborated here.

[0118] The embodiment of the present application further provides a storage medium, specifically a computer-readable storage medium, for storing a computer program, and the program executes the steps of any one of the above-mentioned management methods of the weak current system by a computer or a processor. The computer-readable storage medium may include a non-volatile memory or a non-transitory memory, and may also include an optical disc, a mechanical hard disk, a solid-state drive, etc.

[0119] The embodiment of the present application further provides a device, and the device may include a computer device, which includes a memory and a processor. The memory stores a computer program, and the program can implement the steps of any one of the above-mentioned management methods of the weak current system when executed by the processor.

[0120] In the embodiment of the present application, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiment of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the embodiment of the present application can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0121] In the embodiment of the present application, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., or may also be a volatile memory, such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present application may also be a circuit or any other device capable of implementing a storage function, for storing a computer program and / or data.

[0122] In the management method of the weak current system provided by the embodiments of the present application, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable devices. The computer program can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as an SSD), etc.

[0123] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

[0124] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0125] In the above embodiments, the descriptions of the various embodiments have their own emphases, and any plurality of embodiments can be combined and used. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware processor, or executed and completed by the combination of the hardware and software units in the processor. The software unit can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor executes the instructions in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0127] In the embodiments of the present application, the processor of the above device may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0128] The embodiments of the present application also provide a computer program product. The above computer program product includes a non-transitory computer-readable storage medium storing a computer program. The above computer program is operable to cause a computer to execute some or all of the steps of any of the methods described in the above method embodiments. This computer program product may be a software installation package.

[0129] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0130] The units described as separate components above may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0131] In addition, in each embodiment of the present application, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0132] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a TRP, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned memory includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0133] It should be understood that the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship.

[0134] The term "a plurality of" that appears in the embodiments of the present application refers to two or more.

[0135] The descriptions such as first and second that appear in the embodiments of the present application are only for schematic and distinguishing the described objects, without an order, and do not represent a special limitation on the number of devices in the embodiments of the present application, and cannot constitute any limitation to the embodiments of the present application.

[0136] The term "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not make any limitation on this.

[0137] The above has introduced the embodiments of the present application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A management method for a weak current system, characterized in that: The method comprises: Acquiring initial data through a plurality of sensors in the weak current system; Preprocessing the initial data to obtain intermediate data; Extracting a first key feature from the intermediate data, where the first key feature is used to characterize an environment state and a user behavior; Acquire a second key feature, and fuse the first key feature with the second key feature to obtain a target feature; Analyze the target features through a machine learning algorithm to obtain an analysis result, where the analysis result is used to characterize demand for a period of time in the future; A management strategy is determined based on the analysis results.

2. The method according to claim 1, characterized in that The sensor includes at least one of the following: a temperature sensor, a humidity sensor, a light sensor, and a person detection sensor; The method further includes: performing preliminary processing on the initial data by the sensor.

3. The method according to claim 1, characterized in that The preprocessing includes at least one of the following: data cleaning, outlier detection and normalization processing; The mathematical formula corresponding to the normalization process is as follows: Among them, x norm represents the normalized data, x represents the original data, and x min and x max Represent the minimum and maximum values ​​in the original data respectively.

4. The method according to claim 1, characterized in that: The analysis result includes the probability of equipment failure within a future period of time, and the management strategy is determined according to the analysis result, including: When the probability exceeds a threshold, an early warning mechanism is triggered.

5. The method according to claim 4, characterized in that The trigger warning mechanism includes: generating warning information based on the analysis result, the warning information including: the fault, the impact caused by the fault and recommended maintenance measures; The warning information is sent to the property terminal.

6. The method according to claim 1, characterized in that Determining the management strategy according to the analysis result includes: determining a first operating parameter of the lighting system according to the analysis result, and controlling the lighting system based on the first operating parameter; The first operating parameter includes lighting brightness, and the lighting brightness is determined according to ambient light intensity, time factors and user usage patterns.

7. The method according to claim 1 or 6, characterized in that: Determining the management strategy according to the analysis result also includes: determining a second operating parameter of the air conditioning system according to the analysis result, and controlling the air conditioning system based on the second operating parameter; Among them, the second operating parameter includes air-conditioning temperature, and the air-conditioning temperature is determined according to indoor temperature, outdoor temperature, personnel activity intensity, and historical energy consumption data.

8. A management device for a weak current system, characterized in that: The device comprises: A data acquisition module, used to acquire initial data through multiple sensors in the weak current system; A preprocessing module, used for preprocessing the initial data to obtain intermediate data; A feature extraction module, used to extract a first key feature from the intermediate data, where the first key feature is used to characterize an environment state and a user behavior; A feature fusion module, used to obtain a second key feature, and fuse the first key feature with the second key feature to obtain a target feature; An analysis module is used to analyze the target features through a machine learning algorithm to obtain analysis results, and the analysis results are used to characterize the demand for a period of time in the future; A management module is used to determine a management strategy according to the analysis result.

9. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, which, when executed by the processor, can implement the steps of the method according to any one of claims 1 to 7.

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

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