Data collection method, system, medium and device for intelligent computing edge gateway

Abstract

A data collection method, system, medium and device for an intelligent computing edge gateway, relating to the field of data collection technology. The method includes: determining the collection link of each device based on the configuration information of the corresponding device collected by each edge gateway; performing data preprocessing on the device data collected by the collection link, processing the preprocessed data using an edge computing engine and returning instructions, or storing it locally and waiting to be transmitted back to the cloud; when receiving a data write instruction from the cloud, sending the parsed data to the data center according to the data priority rules. In implementing the technical solution provided in this application, edge computing adopts a distributed computing architecture, distributing the calculations to the proximal devices close to the data source for processing, sharing the workload of the cloud platform, and no longer needing to transmit the data back to the cloud for processing, with better real-time performance, higher efficiency and shortest delay. In the absence of a network and inability to access the cloud, it will not hinder the "ground-based" computing of edge devices.

Description

Technical Field

[0001] The present application relates to the field of data collection technology, and in particular to a data collection method, system, medium and device for an intelligent computing edge gateway. Background Art

[0002] With the continuous development of intelligent buildings and building automation systems, a large number of devices and subsystems are widely used in modern office buildings, commercial buildings and other places, such as lighting control systems, air conditioning systems, security monitoring systems, energy management systems, etc. These systems usually use different network protocols, topologies and communication methods, which will generate a large amount of operating data and status information, requiring timely and efficient data collection and management.

[0003] Currently, data collection servers are mainly deployed in buildings. Various devices and subsystems are connected through wired or wireless networks, and data is collected directly by the servers. However, due to the complex types and wide distribution of building systems, a large amount of network infrastructure is required for data transmission. Especially in dynamic and complex network environments, the timeliness of data collection is poor. Summary of the invention

[0004] The present application provides a data collection method, system, medium and device for an intelligent computing edge gateway, which can improve the timeliness of data collection.

[0005] In a first aspect, the present application provides a data collection method for an intelligent computing edge gateway, the method comprising:

[0006] Obtain configuration information of corresponding devices collected by each edge gateway in the target building, and determine the collection link of each device based on the configuration information;

[0007] Collect data from the device based on the collection link to obtain device data;

[0008] Preprocess the data of each device based on the data type of the device data to obtain preprocessed data, process the preprocessed data using an edge computing engine and return instructions, or store the preprocessed data locally and wait for transmission back to the cloud;

[0009] When a data write instruction is received from the cloud, the pre-processed data is parsed and the parsed data is sent to the data center according to the data priority rule.

[0010] By adopting the above technical solution, the configuration information of the corresponding devices collected by each edge gateway is obtained. By adopting the distributed intelligent edge gateway architecture, there is no need to build a large-scale and complex network setting, and the collection link of each device is determined based on the configuration information, so as to achieve adaptation to heterogeneous devices and network environments. Data is collected from the devices based on the planned collection links to achieve efficient acquisition of real-time device data. Then, the data is pre-processed in a targeted manner according to the data type of the collection device to generate pre-processed data, which reduces the complexity of subsequent data processing and management. When a write instruction is received, the pre-processed data is parsed according to the data priority rules and sent to the data center, ensuring that important data is sent first, thereby comprehensively improving the timeliness of data collection.

[0011] Optionally, the determining the collection link of each of the devices based on each of the configuration information includes:

[0012] Analyze the data collection methods supported by each of the devices based on the network protocols in the configuration information;

[0013] Planning a collection path for each device based on the geographical location, network topology, and data importance of each device in each configuration information;

[0014] The data collection method and the collection path are used as the collection link of each device.

[0015] By adopting the above technical solution, a distributed intelligent computing edge gateway is deployed in the target building to obtain the corresponding device configuration information collected by each gateway, including the device's network protocol, geographical location, network topology and data importance. Based on the network protocol in the device configuration information, the edge gateway can automatically analyze the data collection methods supported by each device. At the same time, according to the device's geographical location, network topology and the importance of device data, the optimal collection path for each device can be planned. By integrating the device protocol and collection path, the complete collection link of the device is determined, realizing comprehensive adaptation to heterogeneous devices, complex network environments and data importance.

[0016] Optionally, the performing data collection on the device based on the collection link to obtain device data includes:

[0017] Obtain the current device data of the current device and the last device data collected last time;

[0018] Determine whether the current device data is consistent with the previous device data; if the current device data is inconsistent with the previous device data, generate a data acquisition instruction;

[0019] When the data collection instruction is received, data is collected from the device based on the collection link to obtain device data.

[0020] By adopting the above technical solution, when collecting device data, the current data of the current device and the historical data collected last time are first obtained to determine whether the two are consistent. Only when the current data is inconsistent with the historical data, a data collection instruction is generated and real-time data collection is performed on the device based on the planned collection link. This incremental collection mechanism can reduce unnecessary data transmission and further improve the real-time and efficiency of data collection.

[0021] Optionally, the current device data includes a first valid data value and a quality stamp, the previous device data includes a second valid data value, and the determining whether the current device data is consistent with the previous device data; if the current device data is inconsistent with the previous device data, generating a data collection instruction includes:

[0022] Determining whether the first valid data value is the same as the second valid data value, and whether the quality stamp conforms to a preset valid quality stamp;

[0023] When the first valid data value is different from the second valid data value, and / or the quality stamp does not conform to a preset valid quality stamp, a data collection instruction is generated.

[0024] By adopting the above technical solution, when collecting device data, the current device data (including valid data value and quality stamp) and previously collected historical data are obtained. By judging whether the current valid data value is the same as the historical value, and whether the current data quality stamp meets the preset valid quality requirements, if the current data and historical data have changed in valid value or quality, a data collection instruction is generated, and incremental real-time data collection is performed on the device based on the planned optimal collection link. This judgment mechanism can effectively avoid repeated collection of data that has not actually changed, accurately capture data changes, and greatly improve the timeliness and efficiency of data collection.

[0025] Optionally, performing data preprocessing on each of the device data based on the type of the device data to obtain preprocessed data includes:

[0026] When the data type is a numerical type, checking is performed according to a linear function corresponding to the device data to obtain preprocessed data;

[0027] When the data type is a calculation type, calculation is performed at a collection point of a calculation function corresponding to the device data to obtain preprocessed data;

[0028] When the data type is a mutation type, it is determined that the device data is an abnormal quality stamp, and the preprocessed data is invalid value data.

[0029] By adopting the above technical solution, the collected original device data will be preprocessed according to the data type to generate preprocessed data. For numerical type data, it will be verified according to the linear function of the device configuration; for calculation type data, calculation is performed at the collection point of the corresponding calculation function; for mutation type data, if an abnormal quality stamp is detected, it is marked as invalid value data. Through preprocessing, heterogeneous data can be unified into a standard format, reducing the complexity of subsequent data processing and management.

[0030] Optionally, when receiving the data write instruction, parsing the preprocessed data, and sending the parsed data to the data center according to the data priority rule, includes:

[0031] When receiving a data write instruction, the device data is parsed to obtain the identifier and valid data corresponding to each of the pre-processed data.

[0032] The valid data is sent to the data center according to a data priority rule, wherein the data priority rule is a corresponding relationship between an identifier and a priority.

[0033] By adopting the above technical solution, when a data write instruction is received, the edge gateway will parse the preprocessed data, obtain the identification and valid value of each data, and then write the valid value data into the corresponding device according to the priority according to the preset data priority rules (the correspondence between identification and priority), ensuring that important data is written first, thereby improving the controllability and efficiency of writing.

[0034] Optionally, the method further includes:

[0035] When a network abnormality signal is received, the collected pre-processed data is cached in the local database in the order of collection time;

[0036] When a network recovery signal is received, the data in the local database is used as preprocessed data, and the step of parsing the preprocessed data is performed, and the parsed data is sent to the data center according to the data priority rule.

[0037] By adopting the above technical solution, during the data writing process under normal network conditions, if a network abnormality signal is received, the edge gateway will cache the collected pre-processed data to the local database in the order of collection time. After the network is restored, the edge gateway will read the cached data from the local database as the pre-processed data, and send it to the data center according to the same priority rules, thereby ensuring the reliability of data transmission.

[0038] In a second aspect of the present application, a data acquisition system for an intelligent computing edge gateway is provided, the system comprising:

[0039] The acquisition link determination module is used to obtain the configuration information of the corresponding device collected by each edge gateway, and determine the acquisition link of each device based on the configuration information;

[0040] A device data acquisition module, used to acquire data from the device based on the acquisition link to obtain device data;

[0041] A data preprocessing module, used to perform data preprocessing on each of the device data based on the data type of the device data to obtain preprocessed data, process the preprocessed data using an edge computing engine and return instructions, or store the preprocessed data locally and wait for transmission back to the cloud;

[0042] The data sending module is used to parse the pre-processed data when receiving the data writing instruction sent by the cloud, and send the parsed data to the data center according to the data priority rule.

[0043] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0044] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0045] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0046] The configuration information of the corresponding devices collected by each edge gateway is obtained. The distributed intelligent edge gateway architecture is adopted, and there is no need to build a large-scale and complex network setting. The collection link of each device is determined based on the configuration information, and the adaptation to heterogeneous devices and network environments is realized. Data is collected from the device based on the planned collection link to achieve efficient acquisition of real-time device data. Then, the data is pre-processed according to the data type of the collection device to generate pre-processed data, which reduces the complexity of subsequent data processing and management. When a write instruction is received, the pre-processed data is parsed according to the data priority rules and sent to the data center, ensuring that important data is sent first, and comprehensively improving the timeliness of data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a data collection method of an intelligent computing edge gateway provided in an embodiment of the present application;

[0048] Figure 2It is a module schematic diagram of a data acquisition system for an intelligent computing edge gateway provided in an embodiment of the present application;

[0049] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0050] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0051] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0052] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0053] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0054] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0055] The embodiments of the present application can be applied but not limited to data collection and management scenarios in large-scale, complex heterogeneous environments, such as smart building systems, to solve the problems of low data collection efficiency, slow data processing, excessive cloud load, high system costs, etc. in traditional building automation systems. It can efficiently, reliably and economically collect and manage massive data of various heterogeneous equipment (air conditioners, elevators, lighting, etc.) in the building, providing a solid data foundation for smart building management.

[0056] The edge gateway data acquisition device provided by this application is located at the edge of the network architecture, acting as a bridge and link, connecting the physical world and the digital world. It integrates multiple functions such as data acquisition, processing, storage and communication, and can realize centralized management and control of various sensors, actuators and other equipment.

[0057] Taking advantage of its powerful data processing capabilities and efficient communication mechanisms, the built-in edge computing technology can analyze and process massive data from terminal devices in real time, extract valuable information, and provide intelligent decision support for upper-level applications. At the same time, it should also have high-speed and stable communication capabilities to ensure the security and reliability of data during transmission.

[0058] Please refer to Figure 1 , a flow chart of a data collection method for an intelligent computing edge gateway is proposed. The method can be implemented by a computer program, can be implemented by a single-chip microcomputer, or can be run on a data collection system of an intelligent computing edge gateway. The computer program can be integrated in a central control center or run as an independent tool application. Specifically, the method includes steps 10 to 40, and the steps are as follows:

[0059] Step 10: Obtain the configuration information of the corresponding devices collected by each edge gateway in the target building, and determine the collection link of each device based on each configuration information.

[0060] The target building refers to the building scenario where the distributed intelligent edge gateway system needs to be deployed for data collection and management, which can be understood as one or more commercial, office or residential buildings that need to be managed intelligently. There are a large number of heterogeneous devices arranged inside the building, such as air conditioning, lighting, elevators, security monitoring and other system devices. These heterogeneous devices are independent of each other and use different communication protocols and network access methods.

[0061] Edge gateway refers to a distributed intelligent computing device deployed in the target building. It has the following main features: Distributed deployment: Edge gateways are deployed in different areas of the building in a distributed manner, rather than concentrated in a single data center. They can obtain on-site equipment data nearby. As a data acquisition station, edge gateways can be deployed on multiple data acquisition servers. They have cross-platform distributed deployment and dynamic loading configuration features. They support distributed deployment, support deployment on multiple servers, establish multiple intelligent computing edge gateway data acquisition sites, and support deployment of multiple edge data acquisition gateway sites on one server. Physical communication methods supported by edge gateways: serial port channels, RS232, RS485, RS422; Ethernet channels; PC board channels; Through built-in edge computing technology, it can analyze and process massive data from terminal devices in real time, extract valuable information, and provide intelligent decision support for upper-level applications.

[0062] The edge gateway in the embodiment of the present application does not use the traditional method of pre-writing the collection configuration (the programming file program is downloaded to the gateway, and the collection data file needs to be manually configured in the gateway during deployment), but uses a method of dynamically loading configuration information. The collection driver and the collected device data information used by each gateway are obtained from the IOT data center. After the gateway is started, the collection driver, collection device, and collection data point are dynamically loaded according to the address information. The intelligent computing edge gateway can connect the physically isolated network and solve the problem of data islands in the building. It can collect data from different isolated networks and transmit the data to the data center, such as collecting office network and device network data information at the same time.

[0063] Specifically, in the intelligent building data collection scenario, since a large number of heterogeneous devices are deployed inside the building, these devices are independent of each other, use different communication protocols, network access methods, and are distributed in different physical locations. In order to achieve efficient data collection and processing of these heterogeneous devices, the embodiment of the present application proposes to deploy a distributed intelligent edge gateway in the building. Each edge gateway will collect the corresponding device information within the jurisdiction, including the network protocol type used by the device (such as Modbus, OPC UA, Bacnet, Lonworks and other industry standard protocols), the geographical location information of the device, the network topology structure and other configuration information. Based on the acquired device configuration information, the optimal collection link of the corresponding device is independently planned and determined. Based on the network protocol type in the configuration information, the data collection method supported by the device can be analyzed, such as serial port collection, Ethernet collection, etc. According to the device's geographical location, network topology and the importance of the device data, the optimal collection path to the device is planned, and the analyzed data collection method is combined with the optimal path as the complete collection link of the device.

[0064] Based on the above embodiment, as an optional embodiment, the step of determining the acquisition link of each device based on each configuration information may further include the following steps:

[0065] Step 101: Based on the network protocols in each configuration information, analyze the data collection method supported by each device.

[0066] Specifically, in the intelligent building data collection scenario, the heterogeneous devices inside the building use a variety of different industrial network protocols, such as Modbus, BACnet, OPC UA and other industry standard protocols, as well as some private protocols. Different network protocols support different data collection methods. The edge gateway will first extract the type of network protocol used by the device from the acquired device configuration information, such as the Modbus TCP / IP protocol. Next, based on the protocol type and combined with its own built-in protocol parsing library, it analyzes the data interaction mode and data acquisition method supported by the protocol, and then determines the available data collection methods, such as serial port collection, Ethernet collection, etc.

[0067] Step 102: Based on the geographical location of each device in each configuration information, network topology, and data importance, plan the collection path of each device.

[0068] Specifically, the edge gateway will first extract the geographical location of the device, the network topology to which it belongs, and the preset data importance from the acquired device configuration information. Among them, the geographical location refers to the specific location coordinates of the device in the actual building space, usually expressed in longitude and latitude or floor indoor coordinates, and these location coordinate information reflects the actual physical distribution of the device. The network topology structure refers to the topology of the network environment where the device is located, including the following levels: the network segment information, the connected network switch, the connected network type, etc., which can accurately describe the network access situation of the device. Data importance refers to the importance of device data to the intelligent building system. Usually, an important level classification can be preset, such as level 1: core equipment data related to system operation safety such as fire protection system and power supply system; level 2: important equipment data related to energy management and personnel travel such as air conditioning host and elevator system; level 3: auxiliary equipment data such as general lighting and access control. Based on this information, the edge gateway will combine the network topology map and equipment distribution map of its own area, and use graph theory algorithms such as Dijkstra shortest path algorithm and A* path finding algorithm to calculate the optimal path to the device. When determining the optimal path, the algorithm will comprehensively consider the weights of multiple dimensions, such as the physical distance between devices, the network environment, and the importance of device data, and use dynamic planning to solve the collection path plan that has the shortest path, the best network quality, and the highest data importance.

[0069] Step 103: Use the data collection method and collection path as the collection link of each device.

[0070] Specifically, the edge gateway will combine the data collection method analyzed for each device with the planned optimal collection path information, and store it as the collection link configuration file of the device. This collection link file clarifies how to reach the device (path) on the one hand, and how to obtain data from the device (collection method) on the other hand. It is a complete description of the instruction set for implementing data collection.

[0071] Step 20: Collect data from the device based on the collection link to obtain device data.

[0072] Specifically, the edge gateway loads the acquisition link configuration file of the target device and obtains key information such as the device address, communication protocol type, and acquisition path. Execute the data acquisition method specified in the acquisition link, such as serial port acquisition or network protocol acquisition, establish a data communication connection with the target device, and send a data reading request. Then, according to the path planned in the acquisition link, penetrate through the network layer by layer and finally reach the network environment where the target device is located. This routing addressing process makes full use of the information guidance such as the topology structure and geographical location in the link file. The collected device data includes: device code, device status, device address, basic point data information, data coding, timestamp, data value, status, quality stamp, etc. It integrates multiple functions such as data acquisition, processing, storage, and communication, and can analyze and process massive data from terminal devices in real time, and realize centralized management and control of various sensors, actuators and other devices.

[0073] Based on the above embodiment, as an optional embodiment, the step of collecting data from the device based on the collection link to obtain the device data may further include the following steps:

[0074] Step 201: Acquire the current device data of the current device and the last device data collected last time.

[0075] Specifically, when the edge gateway completes a device data collection according to the collection link rules, it will first temporarily store the newly collected device data in the real-time data table in the local database. At the same time, according to the device ID currently being collected, the historical database is queried for the data record of the device last collected. If it exists, the historical data record is read out from the historical database and stored as a pair of data with the newly collected data. In this way, the edge gateway simultaneously obtains the current latest real-time data and the previous historical data of the device, thus forming a data pair in the time dimension.

[0076] Step 202: Determine whether the current device data is consistent with the previous device data; if the current device data is inconsistent with the previous device data, generate a data collection instruction.

[0077] Specifically, each device data includes a valid data value and a corresponding quality stamp, where the valid data value refers to the data value of the core measurement point that reflects the actual operating status of the device, such as the temperature value of the temperature sensor, the power consumption value of the meter, etc. This is the core data that reflects the operating status of the device. The quality stamp is an identifier that characterizes the data quality of this valid data value itself, and usually includes the following values: Good (normal) - indicates that the data value is in normal working condition; high credibility, Bad (invalid) - indicates that the data has quality problems, such as sensor failure or communication abnormality, etc.; Uncertain (uncertain) - indicates that the credibility of the data is in doubt, and only data with a quality stamp of "normal" is credible.

[0078] After obtaining the current device data of the device and the last device data collected last time, it is necessary to perform consistency check on the two data to determine whether the device data has changed. First, check whether the two data are consistent as a whole. If they are completely consistent, it means that the device status has not changed and no other operations are required. If the data is inconsistent, the key attributes in the two data, namely the valid data value and the quality stamp, are further compared. The current device data includes the first valid data value and the quality stamp, and the last device data includes the second valid data value. Determine whether the first valid data value is the same as the second valid data value, and whether the quality stamp meets the preset valid quality stamp; the preset valid quality stamp is "normal". If the first valid data value is different from the second valid data value, it can be determined that the core working state of the device has changed, or the quality stamp does not meet the preset valid quality stamp. If it is in the "invalid" state, the data of the current device itself may have quality problems and needs to be re-collected and verified to generate a data collection instruction. Only when the valid data values ​​of the two data are the same and the data quality stamp is valid, data collection is not required.

[0079] Step 203: When a data collection instruction is received, data is collected from the device based on the collection link to obtain device data.

[0080] Specifically, when the edge gateway compares the current data of the device with the previous device data, if there is inconsistency, or the current data quality stamp does not meet the preset valid quality stamp, it will trigger the data collection instruction for collecting the device data. After receiving the data collection instruction, the edge gateway will execute the previous device data collection process again and immediately collect data for the target device involved in the instruction. Specifically, the edge gateway will first load the acquisition link configuration file pre-planned for the device, and obtain key information such as the address, communication protocol, and acquisition path of the device from it. According to the network topology and path information in the link file, the edge gateway will address through the network layer by layer, and finally establish a data channel connection with the network environment where the target device is located. According to the protocol specification specified in the acquisition link, a data read request message will be constructed and sent to the target device through the established data channel, requiring the device to reply with the latest operating data. After receiving the request, the target device will sample its various working status data in real time, encapsulate it according to the specification, and reply to the edge gateway. After receiving the response message, the edge gateway calls the protocol parsing module to parse the original data and extract the valid data value and quality stamp and other device data.

[0081] Step 30: Preprocess the data of each device based on the data type of the device data to obtain preprocessed data, process the preprocessed data using the edge computing engine and return instructions, or store it locally and wait to be transmitted back to the cloud.

[0082] Specifically, in the intelligent building system, due to the existence of a large number of heterogeneous devices, the raw data formats generated by different devices are very different, and the content and encoding methods are also different. After obtaining the raw data of each heterogeneous device, these data need to be preprocessed in a standardized manner. The intelligent computing edge gateway provides data collection preprocessing calculations, and performs different calculations on data of different data types, which can mainly include data linear change calculations, nonlinear changes, effective value calculations, abnormal data processing, etc. The data types in the embodiments of the present application are mainly divided into: numerical type, calculation type, and mutation type. Numerical type data refers to continuous numerical readings obtained by direct measurement or measurement of the device. This type of data generally exists in the form of continuous numerical values ​​with physical quantity units such as "25.6℃" and "88.9KPa". The identification method is to detect whether this continuous physical quantity numerical format exists in the data sample. Calculation type data cannot be directly measured and obtained. It needs to be calculated based on certain physical models and other original parameters to be obtained indirectly, such as: the seasonal energy efficiency ratio (SEER) of the air conditioner host, the active power output of the generator, and the comprehensive energy consumption of the energy site. This type of data itself is not a measurement value, but needs to be obtained using a formula calculation model. The identification method is to see whether it is necessary to calculate based on other original parameters during the data acquisition process. Mutation type data refers to the judgment of abnormal mutations. Abnormal mutations are given invalid quality stamps and treated as invalid values. For different types of data, data processing is performed according to the corresponding data preprocessing method to obtain preprocessed data. The preprocessed data is then processed using the edge computing engine and the instructions are returned, so as to realize on-site calculation and timely processing of the data, or when the data is more complex, the preprocessed data is stored on-site and waits to be transmitted back to the cloud.

[0083] Based on the above embodiment, as an optional embodiment, the step of performing data preprocessing on each device data based on the type of the device data to obtain the preprocessed data further includes:

[0084] Step 301: When the data type is a numerical type, verification is performed according to a linear function corresponding to the device data to obtain preprocessed data.

[0085] Specifically, in the process of intelligent building data collection and processing, numerical data is the most common and basic data type. Such data, such as temperature values, pressure values, etc., directly reflect the real-time operating status of the equipment. However, the original collected numerical data may be biased or abnormal due to sensor failure, communication interference, etc. If these deviation values ​​are used directly without verification, the accuracy of subsequent analysis will be greatly affected. In the embodiment of the present application, a device operation model knowledge base will be constructed in advance, and a linear rule model within its normal working range will be established for each numerical device, and a linear function equation will be used to describe the linear change law that the device operation data should present. For example, for the refrigeration unit in the building, by analyzing and fitting its historical operation data, it can be concluded that under normal circumstances, the outlet temperature of the refrigeration unit is a linear proportional relationship with the load, that is, as the load increases, the outlet temperature rises linearly. When new refrigeration unit data is collected, its corresponding linear model will be called up and the new data will be substituted into the equation for verification. If the newly collected data points fall within the reasonable range of this linear function, they are considered normal data. If the data points deviate significantly from the linear function, they are judged as outliers. For outliers, the edge gateway will mark them and perform further exception processing, such as interpolation correction and re-collection. For normal values, their values ​​will be used and converted into standard numerical data formats.

[0086] Step 302: When the data type is a calculation type, calculation is performed at the collection point of the calculation function corresponding to the device data to obtain preprocessed data.

[0087] Specifically, in addition to the measured numerical data that can be directly obtained, there are also a large number of derivative data that need to be calculated based on the physical model to be indirectly obtained, such as equipment efficiency coefficient, energy site energy consumption, etc. When the pre-processing object is identified as calculation type data, the calculation function model corresponding to the type of equipment will be retrieved from the equipment model library. Taking the comprehensive energy consumption calculation of the energy site as an example, the calculation function generally includes the following aspects: Equipment type energy consumption model: Establish corresponding calculation models, such as electricity model, heat model, etc., for the energy consumption characteristics of different energy loading equipment such as electricity, heat, and cold; Calculation function application: Substitute the real-time collected equipment state parameters into the existing model to automatically calculate the current energy consumption value of each device; Energy summary: Superimpose the energy consumption calculation results of each device according to the energy type to obtain the final comprehensive energy consumption data of electricity, heat, and cold at the site. Through this series of calculation steps, while collecting the original data related to the equipment, based on the existing calculation function model, the complex data derivative calculation process can be automatically and efficiently completed, and a comprehensive report containing all energy types and the energy consumption distribution of each equipment can be obtained, and the report data format is unified and standardized.

[0088] Step 303: When the data type is a mutation type, the device data is determined to be an abnormal quality stamp, and the pre-processed data is obtained as invalid value data.

[0089] Specifically, when mutation type data is identified in the collected device data, it means that the data is not a "normal" quality stamp. This type of data will be directly marked as invalid value data without any other processing, and its value will not be verified or other calculations will not be performed to avoid invalid data from entering subsequent data analysis links and affecting the accuracy of analysis and judgment. At the same time, computing and storage resources are saved, and no unnecessary processing operations are performed on clearly invalid data.

[0090] Step 40: When a data write instruction is received from the cloud, the pre-processed data is parsed and the parsed data is sent to the data center according to the data priority rule.

[0091] In the intelligent building management system, the edge gateway plays an important role in connecting devices and the cloud. Through the early data preprocessing, the edge gateway completes the standardized processing of the collected data, but to truly apply these high-quality data to actual intelligent decision-making and management, it is also necessary to efficiently upload them to the central database for subsequent big data analysis and application system calls.

[0092] Specifically, the standard data from the massive devices deployed in the building system is huge in volume and variety of types. If the full amount of data is simply uploaded, it may cause transmission delays for some important data, thereby affecting the real-time performance of the intelligent analysis and action response of the corresponding equipment. Therefore, the embodiment of the present application establishes a priority model for pre-processed data items, and divides all access data into different priority levels of high, medium and low. The principle of division is based on the device type and data timeliness requirements. After receiving the upstream data write instruction, all pre-processed data is parsed, and the target data is sorted into different priority groups according to the priority model rules. According to the preset strategy, high, medium and low priority data are pushed to the data center in batches according to different time frequencies and network priorities. Among them, for the high-priority data of key equipment, a priority push mechanism will be implemented to ensure low-latency transmission.

[0093] Based on the above embodiment, as an optional embodiment, when receiving a data write instruction sent by the cloud, the pre-processed data is parsed, and according to the data priority rule, the parsed data is sent to the data center. This step specifically includes:

[0094] Step 401: When a data write instruction is received, the device data is parsed to obtain the identifier corresponding to each pre-processed data and valid data.

[0095] After preprocessing, the raw data from various devices will form a standardized and unified preprocessed data set. However, to truly put these high-quality data into use, they need to be further analyzed and identified to clarify the specific content of each data item and the device to which it belongs. This is the prerequisite for investing in downstream intelligent applications.

[0096] Specifically, a unified standard data model is established in advance. For any pre-processed data, its data format specification is defined in the model, including the data header field, the numerical data section of the body part and the identification of each pre-processed data, which indicates the importance level of the data. After receiving the data write instruction from the upstream, the pre-processed data will be parsed one by one according to the preset data model specification. By parsing the data header field, the device number and data item name to which the data belongs can be accurately obtained; by parsing the body data section, valid data can be extracted.

[0097] Step 402: Send valid data to the data center according to the data priority rule, where the data priority rule is the correspondence between the identifier and the priority.

[0098] Specifically, after completing the parsing of the preprocessed data and obtaining the device identification and valid numerical content corresponding to each data, since the local "data priority rule library" has been pre-established, the rule library sets clear priority levels for the identification corresponding to each type of device data, such as high, medium, and low priority. According to the identification information of the parsed data, the edge gateway will query the priority level corresponding to the data in the priority rule library, and classify the data items into different priority groups accordingly. A multi-level cache queue is set up to temporarily store classified high, medium, and low priority data. According to the preset transmission strategy, data of different priorities will be transmitted in sequence according to different time frequencies and bandwidth priorities. The graded data is sent to the data center through fieldbus or industrial wireless. Among them, high-priority data will be sent to the data center at the highest transmission frequency.

[0099] Based on the above embodiment, as an optional embodiment, a data collection method for an intelligent computing edge gateway also includes the following process:

[0100] Specifically, in the actual operation of the intelligent building management and control system, network fluctuations or interruptions may occur occasionally, which may cause device data to be lost or delayed during transmission. Take a large shopping mall as an example. A large number of environmental monitoring and energy consumption detection devices are deployed in its building management system. These devices are connected to the edge gateway through a wireless LAN. Assume that at a certain point in time, due to a brief interruption of the wireless network, the edge gateway will not be able to normally receive data uploaded from the device. At this time, the edge gateway will automatically detect the network anomaly and trigger the local cache mechanism to start. The edge gateway is locally configured with a high-capacity cache database. All collected pre-processed data will be temporarily cached in the local database in chronological order without data loss due to network anomalies. When the wireless network returns to normal, the edge gateway will receive the corresponding network recovery signal. At this time, the edge gateway will automatically retrieve all cached pre-processed data from the cache database and re-execute the subsequent data parsing and upload process. It can effectively avoid data loss or delay caused by network fluctuations, and through priority-based upload, it maximizes the real-time transmission of key data, so that the entire system can be continuously online, data integrity, and extremely reliable.

[0101] See also Figure 2 , is a module diagram of a data acquisition system for an intelligent computing edge gateway provided in an embodiment of the present application. The data acquisition system for the intelligent computing edge gateway may include: an acquisition link determination module, a device data acquisition module, a data preprocessing module, and a data sending module, wherein:

[0102] The acquisition link determination module is used to obtain the configuration information of the corresponding device collected by each edge gateway, and determine the acquisition link of each device based on the configuration information;

[0103] A device data acquisition module, used to acquire data from the device based on the acquisition link to obtain device data;

[0104] A data preprocessing module, used to perform data preprocessing on each of the device data based on the data type of the device data to obtain preprocessed data, process the preprocessed data using an edge computing engine and return instructions, or store the preprocessed data locally and wait for transmission back to the cloud;

[0105] The data sending module is used to parse the pre-processed data when receiving the data writing instruction sent by the cloud, and send the parsed data to the data center according to the data priority rule.

[0106] Optionally, the collection link determination module is also used to analyze the data collection method supported by each of the devices based on the network protocol in each of the configuration information; plan the collection path of each of the devices based on the geographical location, network topology and data importance of each of the devices in each of the configuration information; and use the data collection method and the collection path as the collection link of each of the devices.

[0107] Optionally, the device data acquisition module is also used to obtain the current device data of the current device and the previous device data collected last time; determine whether the current device data is consistent with the previous device data, and if the current device data is inconsistent with the previous device data, generate a data acquisition instruction; when the data acquisition instruction is received, perform data acquisition on the device based on the acquisition link to obtain device data.

[0108] Optionally, the device data acquisition module is also used to determine whether the first valid data value is the same as the second valid data value, and whether the quality stamp conforms to a preset valid quality stamp; when the first valid data value is not the same as the second valid data value, and / or the quality stamp does not conform to a preset valid quality stamp, a data acquisition instruction is generated.

[0109] Optionally, the data preprocessing module is also used to, when the data type is a numerical type, perform verification according to a linear function corresponding to the device data to obtain preprocessed data; when the data type is a calculation type, perform calculation at a collection point of the calculation function corresponding to the device data to obtain preprocessed data; when the data type is a mutation type, determine that the device data is an abnormal quality stamp, and obtain the preprocessed data as invalid value data.

[0110] Optionally, the data sending module is also used to parse the device data when receiving a data write instruction, to obtain an identifier and valid data corresponding to each of the preprocessed data; and to send the valid data to the data center according to a data priority rule, wherein the data priority rule is a correspondence between the identifier and the priority.

[0111] Optionally, a data acquisition system of an intelligent computing edge gateway also includes a data caching module, which is used to cache the collected preprocessed data to a local database in the order of collection time when a network abnormality signal is received; when a network recovery signal is received, the data in the local database is used as the preprocessed data, and the preprocessed data is parsed, and the parsed data is sent to the data center according to the data priority rules.

[0112] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0113] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a data collection method for an intelligent computing edge gateway of the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0114] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0115] The communication bus 302 is used to realize the connection and communication between these components.

[0116] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0117] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0118] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0119] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , as a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and an application program of a data collection method for an intelligent computing edge gateway.

[0120] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a data acquisition method for an intelligent computing edge gateway stored in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders 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 required for the present application.

[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0125] If the 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 this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0126] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0127] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A data collection method for an intelligent computing edge gateway, characterized in that: The method comprises: Obtain the configuration information of the corresponding devices collected by each edge gateway in the target building; Analyze the data collection methods supported by each of the devices based on the network protocols in the configuration information; Based on the geographical location, network topology and data importance of each device in each configuration information, combined with the network topology map and device distribution map of the area where the edge gateway is located, the graph theory algorithm is used to calculate the optimal collection path to each device; Using the data collection method and the optimal collection path as the collection link of each device; Collect data from the device based on the collection link to obtain device data; Preprocess the data of each device based on the data type of the device data to obtain preprocessed data, process the preprocessed data using an edge computing engine and return instructions, or store the preprocessed data locally and wait for transmission back to the cloud; When a data write instruction is received from the cloud, the pre-processed data is parsed and the parsed data is sent to the data center according to the data priority rule.

2. The data collection method of the intelligent computing edge gateway according to claim 1 is characterized in that: The step of collecting data from the device based on the collection link to obtain device data includes: Obtain the current device data of the current device and the last device data collected last time; Determine whether the current device data is consistent with the previous device data, and if the current device data is inconsistent with the previous device data, generate a data collection instruction; When the data collection instruction is received, data is collected from the device based on the collection link to obtain device data.

3. The data collection method of the intelligent computing edge gateway according to claim 2 is characterized in that: The current device data includes a first valid data value and a quality stamp, the previous device data includes a second valid data value, and the determining whether the current device data is consistent with the previous device data, if the current device data is inconsistent with the previous device data, generating a data acquisition instruction, includes: Determining whether the first valid data value is the same as the second valid data value, and whether the quality stamp conforms to a preset valid quality stamp; When the first valid data value is different from the second valid data value, and / or the quality stamp does not conform to a preset valid quality stamp, a data collection instruction is generated.

4. The data collection method of the intelligent computing edge gateway according to claim 1 is characterized in that: Preprocessing each of the device data based on the data type of the device data to obtain preprocessed data includes: When the data type is a numerical type, checking is performed according to a linear function corresponding to the device data to obtain preprocessed data; When the data type is a calculation type, calculation is performed at a collection point of a calculation function corresponding to the device data to obtain preprocessed data; When the data type is a mutation type, it is determined that the device data is an abnormal quality stamp, and the preprocessed data is invalid value data.

5. The data collection method of the intelligent computing edge gateway according to claim 1 is characterized in that: When receiving the data writing instruction sent by the cloud, parsing the pre-processed data, and sending the parsed data to the data center according to the data priority rule, including: When receiving the data writing instruction sent by the cloud, the device data is parsed to obtain the identifier and valid data corresponding to each of the pre-processed data; The valid data is sent to the data center according to a data priority rule, wherein the data priority rule is a corresponding relationship between an identifier and a priority.

6. The data collection method of the intelligent computing edge gateway according to claim 1 is characterized in that: The method further comprises: When a network anomaly signal is received, the collected pre-processed data is cached in the local database in the order of collection time; When a network recovery signal is received, the data in the local database is used as preprocessed data, and the step of parsing the preprocessed data is performed, and the parsed data is sent to the data center according to the data priority rule.

7. A data acquisition system for an intelligent computing edge gateway, characterized in that: The system comprises: The collection link determination module is used to obtain the configuration information of the corresponding device collected by each edge gateway, analyze the data collection method supported by each device based on the network protocol in each configuration information; based on the geographical location, network topology and data importance of each device in each configuration information, combined with the network topology map and device distribution map of the area where the edge gateway is located, use the graph theory algorithm to calculate the optimal collection path to reach each device; use the data collection method and the optimal collection path as the collection link of each device; A device data acquisition module, used to acquire data from the device based on the acquisition link to obtain device data; A data preprocessing module is used to perform data preprocessing on each of the device data based on the data type of the device data to obtain preprocessed data, process the preprocessed data using an edge computing engine and return instructions, or store the preprocessed data locally and wait for transmission back to the cloud; The data sending module is used to parse the pre-processed data when receiving the data writing instruction sent by the cloud, and send the parsed data to the data center according to the data priority rule.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-6.

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