Intelligent building Internet of Things equipment data acquisition method, system, equipment and storage medium

Abstract

The invention discloses an intelligent construction Internet of Things equipment data acquisition method, system and equipment and a storage medium, and relates to the technical field of data acquisition and transmission, and the intelligent construction Internet of Things equipment data acquisition method comprises the steps: utilizing a dynamic generation connector, matching a communication protocol of the equipment, establishing communication connection with the equipment, and utilizing an intelligent data interaction strategy to acquire data of the equipment; the method comprises the following steps: acquiring data through a dynamically generated connector, checking whether the acquired data meets a standard or not by using an anomaly detection algorithm, inputting the data into an adaptive data format converter for standard format conversion, dynamically packaging the converted data, executing distributed foreground analysis at the edge of equipment, and carrying out distributed foreground analysis on the edge of the equipment. And performing real-time processing on the packaged service data, executing background analysis at a server side, further processing the analyzed data, and forwarding the data to an Internet of Things platform. According to the invention, the problems of low flexibility and adaptability of a device communication protocol, inaccurate data acquisition and untimely data processing in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition and transmission technology, and in particular to a data acquisition method, system, device and storage medium for intelligent building Internet of Things equipment. Background Art

[0002] With the rapid development of Internet of Things technology, more and more devices are connected to the network, generating a large amount of data. This data is essential for the management, monitoring and optimization of equipment operation. However, the existing data collection service methods have some shortcomings in practical applications, which may affect the efficiency and accuracy of data collection in some cases.

[0003] Existing data collection service methods usually rely on static connectors, which preset the communication protocol of the device when they are designed. However, with the increase in the types of devices and the update of communication protocols, static connectors can hardly meet the needs of all devices. In addition, static connectors need to be redesigned and configured when the device is updated or replaced, which not only increases the maintenance cost but also reduces the flexibility of the system. Existing data collection service methods also have shortcomings in data acquisition and processing. Traditional data collection methods often use a simple polling mechanism, which will lead to low communication efficiency when the data volume is large, and is not suitable for scenarios with high real-time requirements. Summary of the invention

[0004] In view of the above-mentioned existing problems, the present invention provides a data collection method and system for intelligent building Internet of Things equipment, so as to solve the problems in the prior art of low flexibility and adaptability of equipment communication protocols, low efficiency of data collection, inaccurate collected data and untimely data processing.

[0005] In order to solve the above technical problems, a data collection method for intelligent construction IoT equipment is proposed, including: Use dynamically generated connectors to match the communication protocol of the device and establish a communication connection with the device. Use intelligent data interaction strategies to obtain data from the device through dynamically generated connectors. Use anomaly detection algorithms to check whether the acquired data meets the standards, eliminate or correct data that does not meet the standards, input data that meets the standards into the adaptive data format converter for standard format conversion, dynamically encapsulate the converted data, and communicate the processed data bidirectionally between the device and the data acquisition service. Perform distributed front-end analysis at the edge of the device, process the encapsulated service data in real time, and respond to the data quickly. Perform background analysis on the server side, further process the data after the front-end analysis, and forward it to the IoT middle platform.

[0006] As a preferred solution of the data collection method for intelligent building Internet of Things devices described in the present invention, wherein: the establishment of a communication connection includes using a dynamically generated connector to match the communication protocol of the device and establish a communication connection with the device.

[0007] The communication protocol of the matching device includes protocol identification, connector template selection, connector parameter configuration and connector instantiation.

[0008] The protocol identification includes receiving a signal sent by a device, querying a device configuration file, and performing protocol identification using a protocol identification algorithm.

[0009] The connector template selection includes selecting a connector template from a connector template library according to the identified protocol type; the connector parameter configuration includes configuring the port number, transmission rate and verification method of the connector according to the specific parameters of the device and the protocol requirements; the connector instantiation includes instantiating the connector using the selected connector template and the selected configuration parameters.

[0010] As a preferred solution of the method for collecting data of intelligent building IoT devices described in the present invention, the data acquisition includes acquiring data from the device through a dynamically generated connector using an intelligent data interaction strategy.

[0011] Intelligent data interaction strategies include adaptive polling and subscription mechanisms, data priority and flow control, data compression and encryption, device status management, heartbeat mechanism and connection maintenance; acquiring data from the device includes initializing data interaction strategies, determining data interaction modes, configuring data collection parameters, executing data collection, data verification and cleaning, and data caching and preprocessing.

[0012] The acquired equipment includes sensor data, physical quantity data, environmental data, electrical data, equipment status data and user interaction data.

[0013] As a preferred solution of the data collection method for intelligent building Internet of Things equipment described in the present invention, the anomaly detection algorithm includes using the anomaly detection algorithm to check whether the acquired data meets the standards, eliminating or correcting the data that does not meet the standards, and inputting the data that meets the standards into an adaptive data format converter for standard format conversion.

[0014] The anomaly detection algorithm formula is expressed as: ; Where D(x) is the data anomaly detection value, μ is the average value of the data set, is the variance of the data set, x is the data point to be detected, is the sample mean of the data set, s is the sample standard deviation of the data set, k is the anomaly detection threshold, is a step function, λ is the learning rate of the adaptive data format converter, τ is the threshold of the adaptive data format converter, n is the number of samples in the data set, is the i-th sample in the data set.

[0015] The range of D(x) is [0,1], and the threshold of D(x) is set to 0.7. When , it means the test data is abnormal and needs to be eliminated or corrected; when , it means the detection data is normal and can be input into the adaptive data format converter for standard format conversion.

[0016] As a preferred solution of the data collection method for intelligent building Internet of Things equipment described in the present invention, the format conversion includes inputting data that meets the standard into an adaptive data format converter for standard format conversion.

[0017] The adaptive data format converter includes selecting a conversion method according to the distribution characteristics of the data, introducing a machine learning algorithm to automatically adjust the conversion parameters, and performing data format conversion; wherein the optimized format conversion formula is expressed as: ; in, is the data after format conversion. For input data, is the mean of the data, is the standard deviation of the data, is the error function.

[0018] As a preferred solution of the data collection method for intelligent building Internet of Things equipment described in the present invention, the dynamic encapsulation includes designing the data type, timestamp, data source identifier and data check code of the data encapsulation, dynamically selecting the encapsulation strategy according to the data type and characteristics, encapsulating the data using the encapsulation formula, establishing a communication connection between the device and the data acquisition service, establishing a two-way communication channel using the TCP / IP communication protocol, sending the encapsulated data packet to the data acquisition service, receiving feedback and instructions from the data acquisition service, implementing a data compression algorithm to reduce the amount of transmitted data, and optimizing the communication process using a communication optimization algorithm.

[0019] As a preferred solution of the data collection method for intelligent building Internet of Things equipment described in the present invention, the distributed front-end analysis includes receiving the encapsulated service data packet at the device edge, decapsulating and decompressing the data, and processing the decompressed data in real time using a fast response formula.

[0020] The background analysis includes receiving the data processed by the foreground analysis at the edge of the device on the server side, integrating the data at the edges of different devices, processing it using the server processing formula, applying machine learning algorithms to conduct in-depth analysis of the data, and forwarding the processed data to the IoT middle station.

[0021] The server processing formula is: ; in, The data is processed by the server. For the server-side processing function, This is the result after real-time processing.

[0022] The machine learning algorithm analyzes data by using a linear regression model to predict and analyze data, and trains the model using the least squares method; the linear regression model formula is: ; in, is the prediction function, are model parameters, As a feature.

[0023] The least squares formula is: ; Where m is the number of samples, is a single model parameter, is the prediction function of the jth feature, is the true value of the jth sample, is the function after training.

[0024] Another object of the present invention is to provide a data acquisition system for intelligent building Internet of Things equipment. The present invention optimizes the efficiency of data acquisition services and realizes timeliness and high efficiency of communication. The system of the present invention solves the problems of low flexibility and adaptability of equipment communication protocols, low efficiency of data acquisition, inaccurate collected data and untimely data processing through communication connection, data acquisition and anomaly detection, and adopts exception handling and dynamic encapsulation and parsing strategies, realizes unified access management of different types of equipment, and improves the overall scalability and security of the system.

[0025] As a preferred solution of the intelligent building Internet of Things equipment data acquisition system described in the present invention, it is characterized by including a communication connection module, a data acquisition module, an anomaly detection module, a format conversion module, a dynamic packaging module and a parsing module.

[0026] The communication connection module is used to utilize the communication protocol of the dynamically generated connector matching device to perform protocol identification, connector template selection, connector parameter configuration and connector instantiation, and establish a communication connection with the device.

[0027] The data acquisition module is used to use intelligent data interaction strategies to execute adaptive polling and subscription mechanisms, data priority and flow control, data compression and encryption strategies, and initialize data interaction strategies, determine data interaction modes, configure data collection parameters, perform data collection, data verification and cleaning, and data caching and preprocessing.

[0028] The anomaly detection module is used to check whether the acquired data meets the standards and to remove or correct them.

[0029] The format conversion module is used to input data that meets the standard into the adaptive data format converter for standard format conversion.

[0030] The dynamic encapsulation module is used to design the data type, time stamp, data source identifier and data check code of the data encapsulation, and perform encapsulation.

[0031] The analysis module is used to perform distributed front-end analysis and back-end analysis, and forward the analyzed data to the IoT middle station.

[0032] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in the method for data collection of intelligent building Internet of Things devices are implemented.

[0033] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method described in the method for data collection of intelligent building Internet of Things devices are implemented.

[0034] Beneficial effects of the present invention: The present invention utilizes dynamically generated connectors, and the invention can dynamically match and configure connectors according to the communication protocol of the device, thereby improving the flexibility and adaptability of the system. This step achieves the purpose of establishing a stable communication connection with different devices, so that the data acquisition service can be widely used in various devices without being limited to a specific communication protocol; through intelligent data interaction strategies, the present invention can obtain data from the device through dynamically generated connectors, improve the efficiency and accuracy of data acquisition, achieve efficient data acquisition and transmission, and ensure the real-time and integrity of the data; through the anomaly detection algorithm, the acquired data is checked to see if it meets the standards, and the data that does not meet the standards is eliminated or corrected, and the data that meets the standards is input into the adaptive data format converter for standard format conversion, and the converted data is dynamically packaged, achieving strict screening and efficient conversion of the data. This step ensures the quality and consistency of the data and improves the accuracy and real-time performance of data processing. It performs distributed front-end analysis at the device edge, processes the encapsulated service data in real time, and responds to the data quickly. It performs background analysis on the server side, further processes the data after the front-end analysis, and forwards it to the IoT middle station, achieving rapid response and efficient processing of data. Data can be transmitted to the IoT middle station in a timely and accurate manner, providing a reliable data foundation for subsequent data analysis and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0036] Figure 1 An overall flow chart of a method for collecting data from smart building IoT devices provided by one embodiment of the present invention.

[0037] Figure 2 A system solution flow chart of a smart building IoT equipment data acquisition system provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0041] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0042] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0043] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a method for collecting data of intelligent building IoT devices, including: S1: Use dynamically generated connectors to match the communication protocol of the device and establish a communication connection with the device. Use intelligent data interaction strategies to obtain data from the device through the dynamically generated connector.

[0045] The establishing of the communication connection includes using a dynamically generated connector, matching the communication protocol of the device, and establishing a communication connection with the device.

[0046] It should be noted that the communication protocol of the matching device includes protocol identification, connector template selection, connector parameter configuration and connector instantiation.

[0047] The protocol identification includes receiving a signal sent by a device, querying a device configuration file, and performing protocol identification using a protocol identification algorithm, wherein the protocol identification algorithm is expressed as: The device signal analysis formula is: P=F(Signal), where P is the protocol type, F is the signal analysis function, and Signal is the device signal.

[0048] The configuration file parsing formula is: P=Parse(Config), where P is the protocol type, Parse is the configuration file parsing function, and Config is the device configuration information.

[0049] Furthermore, the connector template selection includes selecting a connector template from a connector template library according to the identified protocol type; the connector parameter configuration includes configuring the connector port number, transmission rate and verification method according to the specific parameters of the device and the protocol requirements; the connector instantiation includes instantiating the connector using the selected connector template and the selected configuration parameters.

[0050] The data acquisition includes acquiring data from the device through a dynamically generated connector using an intelligent data interaction strategy.

[0051] Furthermore, the intelligent data interaction strategy includes adaptive polling and subscription mechanism, data priority and flow control, data compression and encryption, device status management, heartbeat mechanism and connection maintenance; the acquisition of data from the device includes initializing data interaction strategy, determining data interaction mode, configuring data collection parameters, executing data collection, data verification and cleaning, data caching and preprocessing; the data verification includes format verification, integrity verification, consistency verification and accuracy verification; the consistency verification refers to checking the logical consistency of data in time and space.

[0052] It should be further explained that the acquired equipment includes sensor data, physical quantity data, environmental data, electrical data, equipment status data and user interaction data; the sensor data includes temperature data, humidity data, pressure data, light data, sound data and vibration data; the physical quantity data includes position data, acceleration data, rotation speed data, power data and angle data; the environmental data includes air quality data, water quality data and soil data; the electrical data includes voltage data, current data, power data and electric energy data; the equipment status data includes operating status data, fault status data and maintenance status data; the user interaction data includes operation data and interaction data.

[0053] S2: Use anomaly detection algorithms to check whether the acquired data meets the standards, remove or correct the data that does not meet the standards, input the data that meets the standards into the adaptive data format converter for standard format conversion, dynamically encapsulate the converted data, and communicate the processed data bidirectionally between the device and the data acquisition service.

[0054] The anomaly detection algorithm includes using the anomaly detection algorithm to check whether the acquired data meets the standard, eliminating or correcting the data that does not meet the standard, and inputting the data that meets the standard into the adaptive data format converter for standard format conversion.

[0055] The anomaly detection algorithm formula is expressed as: ; Where D(x) is the data anomaly detection value, μ is the average value of the data set, is the variance of the data set, x is the data point to be detected, is the sample mean of the data set, s is the sample standard deviation of the data set, k is the anomaly detection threshold, is a step function, λ is the learning rate of the adaptive data format converter, τ is the threshold of the adaptive data format converter, n is the number of samples in the data set, is the i-th sample of the data set.

[0056] The range of D(x) is [0,1], and the threshold of D(x) is set to 0.7. When , it means the test data is abnormal and needs to be eliminated or corrected; when , it means the detection data is normal and can be input into the adaptive data format converter for standard format conversion.

[0057] The format conversion includes inputting the data conforming to the standard into the adaptive data format converter for standard format conversion, and the formula is expressed as: ; in, is the converted data, For input data, is the minimum value of the input data, is the maximum value of the input data.

[0058] The adaptive data format converter includes selecting a conversion method according to the distribution characteristics of the data, introducing a machine learning algorithm to automatically adjust the conversion parameters, and performing data format conversion; wherein the optimized format conversion formula is expressed as: ; in, is the data after format conversion. For input data, is the mean of the data, is the standard deviation of the data, is the error function.

[0059] The error function is expressed as: ; in, is the input of the error function, and t is the time.

[0060] After the conversion is completed, it should be confirmed whether the converted data is within the range of [0,1] and whether the distribution characteristics of the original data are maintained. The converted data output will be optimized for subsequent data analysis and processing.

[0061] The dynamic encapsulation includes designing the data type, timestamp, data source identifier and data check code of the data encapsulation, dynamically selecting the encapsulation strategy according to the data type and characteristics, encapsulating the data using the encapsulation formula, establishing a communication connection between the device and the data acquisition service, using the TCP / IP communication protocol to establish a two-way communication channel, sending the encapsulated data packet to the data acquisition service, and receiving feedback and instructions from the data acquisition service, implementing a data compression algorithm to reduce the amount of transmitted data, and optimizing the communication process using a communication optimization algorithm.

[0062] The packaging formula is expressed as: ; in, is the encapsulated data packet, T is the timestamp, S is the data source identifier, ID is the data unique identifier, CRC is the cyclic redundancy check code, The data is after format conversion.

[0063] The communication optimization algorithm formula is expressed as: ; in, is the length of the sending queue at time b, is the maximum length of the send queue, is the data transmission rate at time b, is the packet size, is the average transmission time.

[0064] S3: Perform distributed front-end analysis at the device edge, process the encapsulated service data in real time, and respond to the data quickly. Perform background analysis on the server side, further process the data after the front-end analysis, and forward it to the IoT middle platform.

[0065] The distributed front-end analysis includes receiving the encapsulated service data packet at the device edge, decapsulating and decompressing the data, and processing the decompressed data in real time using a fast response formula.

[0066] The quick response formula is: ; in, The result after real-time processing. It is the edge computing function.

[0067] The background analysis includes receiving the data processed by the foreground analysis at the edge of the device on the server side, integrating the data at the edges of different devices, processing it using the server processing formula, applying machine learning algorithms to conduct in-depth analysis of the data, and forwarding the processed data to the IoT middle station.

[0068] The server processing formula is: ; in, The data is processed by the server. For the server-side processing function, This is the result after real-time processing.

[0069] The machine learning algorithm analyzes data by using a linear regression model to predict and analyze data, and trains the model using the least squares method; the linear regression model formula is: ; in, is the prediction function, are model parameters, As a feature.

[0070] The least squares formula is: ; Where m is the number of samples, is a single model parameter, is the prediction function of the jth feature, is the true value of the jth sample, is the function after training.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0072] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0073] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0074] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0075] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0076] Example 3, reference Figure 2 , which is the third embodiment of the present invention, provides a smart building Internet of Things equipment data acquisition system, including a communication connection module 100, a data acquisition module 200, an anomaly detection module 300, a format conversion module 400, a dynamic packaging module 500 and a parsing module 600.

[0077] The communication connection module 100 is used to utilize the communication protocol of the dynamically generated connector to match the device, perform protocol identification, connector template selection, connector parameter configuration and connector instantiation, and establish a communication connection with the device.

[0078] The data acquisition module 200 is used to utilize intelligent data interaction strategies to execute adaptive polling and subscription mechanisms, data priority and flow control, data compression and encryption strategies, and initialize data interaction strategies, determine data interaction modes, configure data acquisition parameters, execute data acquisition, data verification and cleaning, and data caching and preprocessing.

[0079] The anomaly detection module 300 is used to check whether the acquired data meets the standards and to remove or correct them.

[0080] The format conversion module 400 is used to input data that meets the standard into the adaptive data format converter for standard format conversion.

[0081] The dynamic encapsulation module 500 is used to design the data type, timestamp, data source identifier and data check code of data encapsulation, and perform encapsulation.

[0082] The analysis module 600 is used to perform distributed foreground analysis and background analysis, and forward the analyzed data to the IoT middle station.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for collecting data from IoT devices in intelligent construction, characterized in that: include, Use dynamically generated connectors to match the device's communication protocol and establish a communication connection with the device. Use intelligent data interaction strategies to obtain data from the device through the dynamically generated connectors. Use anomaly detection algorithms to check whether the acquired data meets the standards, remove or correct the data that does not meet the standards, input the data that meets the standards into the adaptive data format converter for standard format conversion, dynamically encapsulate the converted data, and communicate the processed data in a two-way manner between the device and the data acquisition service; Distributed front-end analysis is performed at the device edge to process the encapsulated service data in real time and respond to the data quickly. Back-end analysis is performed on the server side to further process the data after the front-end analysis and forward it to the IoT middle platform.

2. The method for collecting data from smart building IoT devices as claimed in claim 1, characterized in that: The establishing of the communication connection includes using a dynamically generated connector, matching the communication protocol of the device, and establishing a communication connection with the device.

3. The method for collecting data from smart building IoT devices as claimed in claim 2, characterized in that: The acquiring of data includes acquiring data from a device through a dynamically generated connector using an intelligent data interaction strategy; Intelligent data interaction strategies include adaptive polling and subscription mechanisms, data priority and flow control, data compression and encryption, device status management, and heartbeat mechanisms and connection maintenance; The acquisition of data from the device includes initializing data interaction strategy, determining data interaction mode, configuring data acquisition parameters, executing data acquisition, data verification and cleaning, data caching and preprocessing; The data verification includes format verification, integrity verification, consistency verification and accuracy verification; The consistency verification refers to checking the logical consistency of data in time and space; The acquired equipment includes sensor data, physical quantity data, environmental data, electrical data, equipment status data and user interaction data.

4. The method for collecting data from smart building IoT devices as claimed in claim 3, characterized in that: The anomaly detection algorithm includes using the anomaly detection algorithm to check whether the acquired data meets the standard, eliminating or correcting the data that does not meet the standard, and inputting the data that meets the standard into the adaptive data format converter for standard format conversion.

5. The method for collecting data from smart building IoT devices as claimed in claim 4, characterized in that: The format conversion includes inputting the data conforming to the standard into an adaptive data format converter for standard format conversion; The adaptive data format converter includes selecting a conversion method according to the distribution characteristics of the data, introducing a machine learning algorithm to automatically adjust the conversion parameters, and performing data format conversion; wherein the optimized format conversion formula is expressed as: ； in, is the data after format conversion. For input data, is the mean of the data, is the standard deviation of the data, is the error function; The error function is expressed as: ； in, is the input of the error function, and t is the time.

6. The method for collecting data from smart building IoT devices as claimed in any one of claims 5, characterized in that: The dynamic encapsulation includes designing the data type, timestamp, data source identifier and data check code of the data encapsulation, dynamically selecting the encapsulation strategy according to the data type and characteristics, encapsulating the data using the encapsulation formula, establishing a communication connection between the device and the data acquisition service, establishing a two-way communication channel using the TCP / IP communication protocol, sending the encapsulated data packet to the data acquisition service, and receiving feedback and instructions from the data acquisition service, implementing a data compression algorithm to reduce the amount of transmitted data, and optimizing the communication process using a communication optimization algorithm; The communication optimization algorithm formula is expressed as: ； in, is the length of the sending queue at time b, is the maximum length of the send queue, is the data transmission rate at time b, is the packet size, is the average transmission time.

7. The method for collecting data from smart building IoT devices as claimed in claim 6, characterized in that: The distributed front-end analysis includes receiving the encapsulated service data packet at the device edge, decapsulating and decompressing the data, and processing the decompressed data in real time using a fast response formula; The quick response formula is: ； in, The result after real-time processing. is the edge computing function; The background analysis includes: the server receives the data processed by the foreground analysis at the edge of the device, integrates the data at the edges of different devices, processes them using the server processing formula, applies the machine learning algorithm to conduct in-depth analysis of the data, and forwards the processed data to the IoT middle station; The server processing formula is: ； in, The data is processed by the server. For the server-side processing function, It is the result after real-time processing; The machine learning algorithm analyzes data by using a linear regression model to predict and analyze data, and trains the model using the least squares method; the linear regression model formula is: ； in, is the prediction function, are model parameters, For the characteristics; The least squares formula is: ； Where m is the number of samples, is a single model parameter, is the prediction function of the jth feature, is the true value of the jth sample, is the function after training.

8. A system using a data collection method for intelligent building IoT equipment, characterized in that: It includes a communication connection module, a data acquisition module, an anomaly detection module, a format conversion module, a dynamic packaging module and a parsing module; The communication connection module is used to use the communication protocol of the dynamically generated connector to match the device, perform protocol identification, connector template selection, connector parameter configuration and connector instantiation, and establish a communication connection with the device; The data acquisition module is used to use intelligent data interaction strategies to implement adaptive polling and subscription mechanisms, data priority and flow control, data compression and encryption strategies, and initialize data interaction strategies, determine data interaction modes, configure data acquisition parameters, perform data acquisition, data verification and cleaning, data caching and preprocessing; The anomaly detection module is used to check whether the acquired data meets the standards and to remove or correct them; The format conversion module is used to input the data that meets the standard into the adaptive data format converter for standard format conversion; The dynamic encapsulation module is used to design the data type, timestamp, data source identifier and data check code of the data encapsulation, and perform encapsulation; The analysis module is used to perform distributed front-end analysis and back-end analysis, and forward the analyzed data to the IoT middle station.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the smart building Internet of Things equipment data collection method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart building Internet of Things equipment data collection method described in any one of claims 1 to 7 are implemented.

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