Equipment interconnection method and device based on embedded system, equipment and medium

Abstract

The invention discloses an equipment interconnection method and device based on an embedded system, equipment and a medium. The method comprises the following steps: performing data compression on original data collected by target embedded equipment to obtain a compressed data packet; performing data format and interface protocol conversion on the compressed data packet to obtain standardized data; distributing the standardized data to the target embedded device through the data middleware layer; according to the current operation state information of the target embedded device, judging whether the target embedded device has a fault or not; if yes, performing hot backup on the target embedded device according to the fault type and the fault degree, and performing fault isolation and fault recovery on the target embedded device; and carrying out security protection on the data transmission, storage and processing processes of the target embedded equipment by adopting preset security protection measures. According to the scheme, efficient utilization of resources can be realized, the compatibility and expandability of the system are ensured, the real-time performance of data distribution is improved, and the stability, reliability and safety of equipment are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a device interconnection method, device, equipment and medium based on an embedded system. Background Art

[0002] Embedded devices play a vital role in the Internet scenario, and they are the foundation for building core Internet technologies. In practical applications, embedded devices face many technical challenges and contradictions.

[0003] 1. Embedded devices usually have limited resources, and their processing power and storage space are restricted. However, the interconnected scenarios place higher demands on the computing and storage capabilities of the devices. How to achieve efficient data processing and communication with limited resources has become a major problem. 2. There are many types of devices in the interconnected scenarios, and the communication protocols are different. How to achieve seamless connection and data interaction between different devices and ensure the compatibility and scalability of embedded systems is also a problem that needs to be solved. 3. Interconnected devices are usually deployed in complex and changing environments. How to ensure the reliability, stability and security of the equipment and resist various interferences and attacks is another challenge facing embedded devices. 4. The interconnected scenarios have high requirements on the real-time and low latency of the devices. How to achieve efficient real-time data processing and transmission in embedded devices to meet the needs of time-sensitive applications is also a problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a device interconnection method, apparatus, equipment and medium based on an embedded system, which realizes efficient utilization of resources through data compression, ensures the compatibility and scalability of the embedded system through data conversion, improves the real-time performance of data distribution through a data middleware layer, and enhances the stability, reliability and security of embedded devices through hot backup, fault recovery and data protection.

[0005] According to one aspect of the present invention, a method for interconnecting devices based on an embedded system is provided, the method comprising:

[0006] Acquire the original data collected by the target embedded device, and compress the original data to obtain a compressed data packet;

[0007] Converting the data format and interface protocol of the compressed data packet based on a preset data exchange format and a preset interface standard, and decompressing the converted compressed data packet to obtain standardized data;

[0008] Transmitting the standardized data to a pre-built data middleware layer, and distributing the standardized data to the target embedded device through the data middleware layer;

[0009] Acquire current running status information of the target embedded device, and determine whether the target embedded device has a fault according to the current running status information;

[0010] If so, hot backup the target embedded device according to the fault type and fault degree, and perform fault isolation and fault recovery on the target embedded device;

[0011] Preset security protection measures are used to protect the data transmission, storage and processing processes of the target embedded device; wherein the preset security protection measures include encryption authentication, access control and behavior auditing.

[0012] According to another aspect of the present invention, there is provided a device interconnection apparatus based on an embedded system, the device comprising:

[0013] A data compression module is used to obtain the original data collected by the target embedded device and compress the original data to obtain a compressed data packet;

[0014] A data conversion module, used to convert the data format and interface protocol of the compressed data packet based on a preset data exchange format and a preset interface standard, and decompress the converted compressed data packet to obtain standardized data;

[0015] A data distribution module, used for transmitting the standardized data to a pre-built data middleware layer, and distributing the standardized data to the target embedded device through the data middleware layer;

[0016] A fault detection module, used to obtain the current running status information of the target embedded device, and determine whether the target embedded device has a fault according to the current running status information;

[0017] A fault processing module, for performing hot backup of the target embedded device according to the fault type and fault degree, and performing fault isolation and fault recovery on the target embedded device, if any;

[0018] The data protection module is used to adopt preset security protection measures to securely protect the data transmission, storage and processing processes of the target embedded device; wherein the preset security protection measures include encryption authentication, access control and behavior auditing.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] at least one processor; and,

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the device interconnection method based on the embedded system described in any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the device interconnection method based on an embedded system described in any embodiment of the present invention when executed.

[0024] The technical solution of the embodiment of the present invention obtains the original data collected by the target embedded device, and compresses the original data to obtain a compressed data packet; converts the data format and interface protocol of the compressed data packet based on a preset data exchange format and a preset interface standard, and decompresses the converted compressed data packet to obtain standardized data; transmits the standardized data to a pre-built data middleware layer, and distributes the standardized data to the target embedded device through the data middleware layer; obtains the current operating status information of the target embedded device, and determines whether the target embedded device has a fault according to the current operating status information; if so, hot-backs up the target embedded device according to the fault type and fault degree, and performs fault isolation and fault recovery on the target embedded device; uses preset security protection measures to securely protect the data transmission, storage and processing processes of the target embedded device; wherein the preset security protection measures include encryption authentication, access control and behavior auditing. This technical solution achieves efficient use of resources through data compression, ensures the compatibility and scalability of embedded systems through data conversion, improves the real-time performance of data distribution through the data middleware layer, meets the needs of time-sensitive applications, enhances the stability, reliability and security of embedded devices through hot backup, fault recovery and data protection, and effectively resists interference and attacks in complex environments, thus providing strong technical support for the application of embedded devices in interconnected scenarios.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a device interconnection method based on an embedded system provided according to Embodiment 1 of the present invention;

[0028] Figure 2 is a flow chart of a device interconnection method based on an embedded system provided according to Embodiment 2 of the present invention;

[0029] Figure 3 is a flow chart of a device interconnection method based on an embedded system provided according to Embodiment 3 of the present invention;

[0030] Figure 4 This is a schematic diagram of the structure of a device interconnection apparatus based on an embedded system provided according to a fourth embodiment of the present invention;

[0031] Figure 5 The present invention is a schematic diagram of the structure of an electronic device for implementing a device interconnection method based on an embedded system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1This is a flowchart of a device interconnection method based on an embedded system provided in the first embodiment of the present invention. This embodiment is applicable to the case of efficiently and securely interconnecting multiple embedded devices. The method can be executed by a device interconnection device based on an embedded system. The device interconnection device based on an embedded system can be implemented in the form of hardware and / or software. The device interconnection device based on an embedded system can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0036] S110, acquiring original data collected by the target embedded device, and compressing the original data to obtain a compressed data packet.

[0037] In this embodiment, the original data collected by the target embedded device is first obtained, and the original data is preprocessed (including noise removal and outlier removal). Among them, the target embedded device can be understood as an embedded device participating in the device interconnection, specifically including multiple embedded devices. Exemplarily, assuming that the original data is temperature data, after obtaining the temperature data collected by the target embedded device, the Gaussian white noise can be removed by the Kalman filter algorithm, and the outliers are limited to the range of plus or minus 1.5 times the standard deviation. Specifically, the denoising process of the Kalman filter algorithm is as follows: first input the original temperature data sequence T = {T1, T2, ..., Tn} and Gaussian white noise, then use the Kalman filter algorithm to process the temperature data to remove noise, and finally output the denoised temperature data sequence T' = {T1', T2', ..., Tn'}. Among them, Ti represents the i-th original temperature data point, and Ti' represents the i-th temperature data point after Kalman filter processing. The outlier removal process is as follows: first calculate the mean μ and standard deviation σ of the denoised data, and then remove the data outside the range of [μ-1.5σ,μ+1.5σ] as outliers.

[0038] After the raw data is preprocessed, a preset data compression algorithm can be used to compress the preprocessed raw data to obtain a compressed data packet, so as to reduce the amount of data transmission, thereby improving the efficiency of data transmission. Exemplarily, the preset data compression algorithm can be a lightweight data compression algorithm, such as run-length coding and Huffman coding. Exemplarily, in this embodiment, when acquiring the raw data collected by the target embedded device, data can be transmitted at a rate of 250kbps through wireless transmission based on the Zigbee wireless communication protocol, and a data cleaning algorithm based on regular expressions can be used to remove noise and outliers in the data. Among them, 250kbps (kilobits per second) means that the Zigbee protocol can transmit 250,000 bits of data per second; using regular expressions to clean data does not involve specific calculations, but it is necessary to preset the amount of cleaned data to be D bits. Then the GZIP algorithm is used to compress the raw data so that the compression ratio reaches 3:1.

[0039] S120, converting the data format and interface protocol of the compressed data packet based on the preset data exchange format and the preset interface standard, and decompressing the converted compressed data packet to obtain standardized data.

[0040] In this embodiment, after obtaining the compressed data packet, it can be determined whether the original data format and original interface protocol of the compressed data packet are consistent with the preset data exchange format and the preset interface standard. If not, the data format and interface protocol of the compressed data packet can be converted based on the preset data exchange format and the preset interface standard, thereby converting the original data format of the compressed data packet into the preset data exchange format, and converting the original interface protocol of the compressed data packet into the standard protocol corresponding to the preset interface standard. Exemplarily, the preset data exchange format can be the JSON format, and the preset interface standard can be the RESTful API standard (whose corresponding standard protocol is the HTTP protocol).

[0041] In this embodiment, optionally, the data format and transmission protocol of the compressed data packet are converted based on the preset data exchange format and the preset interface standard, including: determining the original data format and the original interface protocol of the compressed data packet; if the original data format is inconsistent with the preset data exchange format, converting the original data format to the preset data exchange format; if the original interface protocol does not match the preset interface standard, converting the original interface protocol to a standard protocol corresponding to the preset interface standard.

[0042] Exemplarily, it is assumed that the original data format of the compressed data packet is a private binary format, and the original interface protocol is the MQTT protocol; the preset data exchange format is the JSON format, and the preset interface standard is the RESTfulAPI standard (the corresponding standard protocol is the HTTP protocol). It can be seen that the original data format is inconsistent with the preset data exchange format, and the original interface protocol does not match the preset interface standard. In this case, it is necessary to convert the original data format and the original interface protocol of the compressed data packet. Specifically, through the data format conversion process, the binary data is converted into the JSON format, and the data fields are mapped and reorganized according to the unified standard. At the same time, through the protocol adaptation process (i.e., interface protocol conversion), the MQTT protocol is converted into the HTTP protocol compatible with the RESTfulAPI, and the request parameters and URL addresses that comply with the interface specifications are generated.

[0043] After completing the data format conversion and interface protocol conversion, the converted compressed data packet can be decompressed to obtain standardized data under the preset data exchange format and preset interface standard. Exemplarily, the converted compressed data packet can be decompressed using the DEFLATE algorithm, thereby recovering the original JSON format data. Further, the device ID, timestamp, sensor type and numerical key fields can be extracted from the standardized data, and the data can be formatted and converted according to the predefined data cleaning and conversion rules. Then the mean filtering algorithm is used to denoise the data, for example, the sliding window size can be set to 10, and outliers exceeding 3 times the standard deviation can be removed. Finally, the processed result data can be written into the time series database InfluxDB at a preset frequency (for example, once every 5 minutes), and statistical indicators such as average, maximum and minimum values ​​can be displayed through Grafana, and dashboards and trend charts can be generated for equipment performance monitoring and fault diagnosis analysis.

[0044] S130, transmitting the standardized data to a pre-built data middleware layer, and distributing the standardized data to the target embedded device through the data middleware layer.

[0045] In this embodiment, after obtaining the standardized data, the standardized data can be transmitted to the pre-built Kafka-based data middleware layer at a rate of 100 per second through a standard protocol corresponding to the preset interface standard, that is, 100 data records in the preset data exchange format can be transmitted per second. Then, the standardized data can be parsed using ProtocolBuffers through the data middleware layer, and the data transmission protocol that needs to be distributed is determined based on the type and source of the standardized data. Then, based on the preset routing rules, the data transmission protocol that needs to be distributed is adopted through the data middleware layer, and the data is distributed to each target embedded device at an average rate of 50 per second.

[0046] For example, if the data source is an embedded sensor, the raw data is collected at a frequency of 100Hz through the SPI interface, and the data compression algorithm based on Huffman coding is used to reduce the data volume by 40%. Finally, the compressed data packet is transmitted to the data middleware layer for processing at the reliability level of QoS1 through the MQTT protocol. Among them, 100Hz means that the SPI interface collects data 100 times per second; QoS1 means that the MQTT protocol provides a certain degree of reliability guarantee based on at least one data transmission; the data volume is reduced by 40% as follows: if the original data volume is V bits, the compressed data volume is V×(1-0.40)=0.60V bits.

[0047] S140, obtaining current operating status information of the target embedded device, and determining whether the target embedded device has a fault according to the current operating status information.

[0048] In this embodiment, during the device interconnection process, the current operating status information of the target embedded device can be obtained periodically (such as at intervals of 5 seconds), and whether the target embedded device has a fault can be determined based on the current operating status information. Exemplarily, the operating status information may include data such as CPU usage, memory occupancy, disk I / O performance indicators, and system log status (such as error information, abnormal warning information). Optionally, judging whether the target embedded device has a fault based on the current operating status information includes: judging whether the target embedded device has a fault based on the size relationship between the current operating status information and a preset fault threshold; and / or, predicting the current operating status information according to a pre-trained fault detection model, and judging whether the target embedded device has a fault based on the prediction result; wherein the fault detection model is obtained by training based on the historical operating status information of the reference embedded device.

[0049] Among them, the preset fault threshold may refer to a reference value pre-set according to actual needs for determining whether there is a device fault. Exemplarily, the preset fault threshold may include a CPU usage threshold, a memory occupancy threshold, and a disk I / O delay time threshold. The reference embedded device can be used to characterize the data source for fault detection model training, that is, which embedded device the data involved in model training comes from. Among them, the fault detection model can be used to predict the fault type of the embedded device and the corresponding fault risk. Exemplarily, the fault detection model can be a machine learning model based on algorithms such as decision trees and random forests. Specifically, the fault type can be used to indicate the specific component where the fault occurs, such as a CPU fault, a memory fault, or a disk I / O fault; the fault risk can be used to reflect the possibility of a fault. It can be understood that the higher the fault risk value, the greater the possibility of a fault.

[0050] In this embodiment, two different methods can be used to detect faults in the target embedded device, one is a threshold comparison method, and the other is a model prediction method. It should be noted that the two methods can be used alone or in combination, and can be flexibly selected according to actual needs. Specifically, when the threshold comparison method is used, the current operating state information needs to be compared with the corresponding preset fault threshold. If the current operating state information is greater than the corresponding preset fault threshold, it can be determined that the target embedded device has a fault. Exemplarily, when the CPU usage rate exceeds 90%, or the memory occupancy rate exceeds 80%, or the disk I / O delay time is greater than 100ms, it can be determined that the target embedded device has a fault, and the corresponding fault types are CPU fault, memory fault or disk I / O fault. When the model prediction method is used, it is necessary to perform model training in advance based on the historical operating state information of the reference embedded device to obtain a fault detection model, and after obtaining the current operating state information of the target embedded device, the current operating state information is input into the fault detection model, and the target embedded device can be judged whether it has a fault according to the model output result (including the fault type and the corresponding fault risk). Exemplarily, for a certain fault type, if the corresponding fault risk is greater than a preset risk threshold (such as 60%), it can be determined that the target embedded device has a fault.

[0051] Through such a setting, the present solution can use different methods to quickly and accurately determine whether the target embedded device has a fault, so as to perform subsequent fault processing in a timely manner.

[0052] Furthermore, before judging whether the target embedded device has a fault according to the current running status information, it is also possible to judge whether the target embedded device has an abnormality according to the current running status information. Exemplarily, a machine learning algorithm such as a support vector machine and an isolation forest can be used to model the historical running status information of the reference embedded device to obtain an abnormality detection model, and after obtaining the current running status information of the target embedded device, the current running status information is input into the abnormality detection model, and the device abnormality is judged according to the model output result (abnormality score). Exemplarily, if the abnormality score is greater than the preset warning score threshold (such as 0.7), an abnormal warning can be triggered, and further fault detection and diagnosis can be performed.

[0053] S150: If yes, hot backup is performed on the target embedded device according to the fault type and fault degree, and fault isolation and fault recovery are performed on the target embedded device.

[0054] Among them, the degree of fault can be determined based on the difference between the current operating state information and the corresponding preset fault threshold or the fault risk value. Specifically, when the current operating state information is greater than the corresponding preset fault threshold (i.e., there is a fault), if the difference between the current operating state information and the corresponding preset fault threshold is less than or equal to the preset difference, the degree of fault can be determined to be a general fault; if the difference between the current operating state information and the corresponding preset fault threshold is greater than the preset difference, the degree of fault can be determined to be a serious fault. In addition, when the fault risk is greater than the preset risk threshold (i.e., there is a fault), if the fault risk is less than or equal to the preset high risk threshold (such as 80%), the degree of fault can be determined to be a general fault; if the fault risk is greater than the preset high risk threshold, the degree of fault can be determined to be a serious fault. Among them, the preset high risk threshold can refer to a high risk fault reference value pre-set according to actual application requirements, and the preset high risk threshold is greater than the preset risk threshold.

[0055] In this embodiment, after determining that the target embedded device has a fault, the target embedded device can be hot-backed up by using strategies such as active / standby switching and data replication according to the fault type and fault degree of the target embedded device to ensure the continuous availability of the target embedded device. Optionally, hot-backing up the target embedded device according to the fault type and fault degree includes: determining a target backup component according to the fault type; determining a target backup method according to the fault degree; and hot-backing up the target backup component based on the target backup method.

[0056] Among them, the target backup component may refer to a component that needs to be hot-backed up. The target backup method may include two methods: normal backup (applicable to general faults) and incremental backup (applicable to serious faults), and both methods can be described by the backup frequency and the number of backups. For example, in the normal backup method, the backup frequency can be set to once every two hours and the number of backups can be 1, and in the incremental backup method, the backup frequency can be set to once every hour and the number of backups can be 3. Exemplarily, if the fault type is a memory fault and the fault level is a serious fault, it can be determined that the target backup component is the memory, and the target backup method is the incremental backup method, so the memory can be backed up once every hour and the number of backups each time is 3.

[0057] After determining that the target embedded device has a fault, an adaptive fault recovery algorithm can be triggered immediately, which can automatically select a fault recovery strategy according to the fault type and the hardware configuration of the target embedded device (such as the number of CPU cores, memory size, etc.). Exemplarily, the adaptive fault recovery algorithm can be a decision tree algorithm. Exemplarily, the decision tree algorithm can be used to select the best solution from the preset recovery strategies (such as restarting the process, releasing memory, limiting CPU usage, etc.) as the fault recovery strategy. After determining the fault recovery strategy, the target embedded device is fault isolated, the process or service that causes the fault is stopped, and the occupied resources are released at the same time. Among them, fault isolation can be achieved through Docker container technology. After isolating the fault source, the adaptive fault recovery algorithm can use machine learning algorithms such as incremental learning or transfer learning (such as Q-Learning algorithm in reinforcement learning) to optimize and adjust the fault recovery strategy according to historical fault data and current status, and apply the optimized fault recovery strategy to the target embedded device, and perform fault recovery by restarting the service, rolling back the system, and limiting resource occupation operations. Among them, the fault recovery process can be executed by the Ansible automated operation and maintenance tool to remotely control and configure the target embedded device. After the fault recovery is completed, the fault information and recovery process can be recorded in the log to optimize the fault detection method and adaptive fault recovery algorithm. Kibana can also be used for visualization and analysis.

[0058] S160, using preset security protection measures to securely protect the data transmission, storage and processing of the target embedded device.

[0059] Among them, the preset security protection measures include encryption authentication, access control and behavior auditing. Optionally, the preset security protection measures are used to protect the data transmission, storage and processing of the target embedded device, including: using encryption authentication technology to authenticate the sender and receiver of data transmission, and encrypting the transmitted data; based on the role and authority of the access object, using access control technology to control the access and operation of the access object to the stored data; based on the preset data operation rules, using behavior auditing technology to identify abnormal behavior of various operation behaviors in the data processing process; determining the data security assessment results based on the encryption authentication results, access control results and behavior auditing results, and dynamically adjusting the preset security protection measures based on the data security assessment results.

[0060] Specifically, when obtaining information about the data transmission, storage, and processing process of the target embedded device, system log analysis and network traffic monitoring techniques can be used. For example, by deploying network traffic monitoring tools, the network communications of the target embedded device can be captured and analyzed to extract key information such as the source address, destination address, transmission protocol, and data content of the data transmission, and then combined with the preset data transmission security policy to determine whether the transmission behavior is compliant. During the data transmission process, encryption algorithms such as AES (such as AES-256) and RSA can be used to encrypt sensitive data to enhance the security strength of the data, and authentication mechanisms such as digital signatures and digital certificates can be used to verify the identities of the communicating parties (i.e., the sender and receiver of the data transmission) to prevent the data from being illegally intercepted and tampered with.

[0061] For data storage, a role-based access control model (RBAC) can be used to control access and operations to data based on the roles and permissions of the access objects. For example, the access objects are divided into different roles such as administrators, operators, and auditors, and corresponding data access permissions are assigned to each role. For example, the administrator has the highest permissions and can access and modify all data; the operator can only access the device data for which he is responsible; the auditor only has the permission to view the data and cannot modify it.

[0062] In the process of data processing, rule-based behavioral auditing technology can be used to monitor and record the operation behavior of the access object in real time by presetting a series of data operation behavior rules, and match and analyze with the rules to identify illegal or suspicious operations. For example, the rule is set to "prohibit data modification operations between 2 am and 5 am". If it is detected that a user modifies the data during this time period, an alarm will be triggered, the event will be recorded, and the administrator will be notified to deal with it.

[0063] After obtaining the encryption authentication results, access control results, and behavior audit results, the information can be integrated and the data security assessment results can be determined based on the integration results, so as to dynamically adjust and optimize the preset security protection measures according to the data security assessment results. For example, when it is found that the access frequency of a certain data interface is abnormally increased, the access flow limiting measures of the interface can be automatically enabled, and the maximum number of requests per second is limited to 100 times. Requests exceeding the limit will be discarded to avoid server overload. Then, the optimized security protection measures can be applied to the data processing flow of the target embedded device, and comprehensive protection can be provided for each link of data collection, transmission, storage, processing, etc., to form a complete data security protection system to minimize the risk of data leakage and tampering.

[0064] Furthermore, machine learning algorithms can be used to establish a data security risk prediction model by analyzing historical data security events and their corresponding historical data security assessment results, so as to predict the security risks that may occur in the future based on the model. For example, by analyzing historical data, it is found that November to December each year is the peak period for data leakage incidents. During this period, data access control and monitoring can be strengthened to prepare for prevention in advance.

[0065] The technical solution of the embodiment of the present invention obtains the original data collected by the target embedded device, and compresses the original data to obtain a compressed data packet; converts the data format and interface protocol of the compressed data packet based on a preset data exchange format and a preset interface standard, and decompresses the converted compressed data packet to obtain standardized data; transmits the standardized data to a pre-built data middleware layer, and distributes the standardized data to the target embedded device through the data middleware layer; obtains the current operating status information of the target embedded device, and determines whether the target embedded device has a fault according to the current operating status information; if so, hot-backs up the target embedded device according to the fault type and fault degree, and performs fault isolation and fault recovery on the target embedded device; uses preset security protection measures to securely protect the data transmission, storage and processing processes of the target embedded device; wherein the preset security protection measures include encryption authentication, access control and behavior auditing. This technical solution achieves efficient use of resources through data compression, ensures the compatibility and scalability of embedded systems through data conversion, improves the real-time performance of data distribution through the data middleware layer, meets the needs of time-sensitive applications, enhances the stability, reliability and security of embedded devices through hot backup, fault recovery and data protection, and effectively resists interference and attacks in complex environments, thus providing strong technical support for the application of embedded devices in interconnected scenarios.

[0066] Embodiment 2

[0067] Figure 2A flowchart of a device interconnection method based on an embedded system provided in the second embodiment of the present invention is provided. This embodiment is optimized based on the above embodiment. The specific optimization is: compressing the original data to obtain a compressed data packet, including: determining the target data compression algorithm according to the characteristics and distribution of the original data; processing the original data into blocks to obtain multiple data blocks, and using the target data compression algorithm to compress each data block to obtain multiple candidate compressed sub-packets; using a machine learning algorithm to preliminarily optimize the compression parameters of the target data compression algorithm according to the candidate compressed sub-packets, and re-compressing each data block based on the preliminarily optimized target data compression algorithm to obtain multiple reference compressed sub-packets; decompressing the reference compressed sub-packets based on the preset compression index, and re-optimizing the compression parameters of the target data compression algorithm based on the test results; wherein the preset compression index includes compression rate and information loss rate; re-compressing each data block based on the re-optimized target data compression algorithm to obtain multiple target compressed sub-packets, and determining the compressed data packet based on the target compressed sub-packets.

[0068] like Figure 2 As shown, the method of this embodiment specifically includes the following steps:

[0069] S210, acquiring original data collected by the target embedded device, and determining a target data compression algorithm according to the characteristics and distribution of the original data.

[0070] For example, assuming that the original data is temperature data, according to the numerical distribution characteristics of the temperature data, the run-length encoding algorithm can be selected for compression, and the run-length threshold is set to 5. The run-length threshold is a parameter used in run-length encoding to determine when to start a new run.

[0071] S220, dividing the original data into blocks to obtain a plurality of data blocks, and using a target data compression algorithm to perform data compression on each data block to obtain a plurality of candidate compressed sub-packets.

[0072] Exemplarily, the temperature data may be processed in blocks of 100 data points each, and run-length coding compression may be performed independently on each data block based on a set run-length threshold of 5 to obtain a plurality of candidate compressed sub-packets.

[0073] S230, using a machine learning algorithm to preliminarily optimize the compression parameters of the target data compression algorithm according to the candidate compression sub-packets, and re-compressing each data block based on the preliminarily optimized target data compression algorithm to obtain multiple reference compression sub-packets.

[0074] Exemplarily, the machine learning algorithm may be a decision tree algorithm. For example, assuming that the temperature data is divided into 1000 data blocks, each data block includes 100 data points, then the decision tree algorithm may be used to train 1000 groups of temperature data samples, and the optimal compression parameter combination may be obtained through grid search and cross-validation, thereby adjusting the run length threshold to 8. Then, the run length encoding algorithm with a run length threshold of 8 is used to recompress the 1000 data blocks to obtain multiple reference compressed sub-packets.

[0075] S240, performing a decompression test on the reference compressed sub-packet based on a preset compression index, and re-optimizing the compression parameters of the target data compression algorithm according to the test result.

[0076] The preset compression index includes compression ratio and information loss ratio. Specifically, the compression ratio can be expressed as: The information loss rate can be expressed as:

[0077]

[0078] In this embodiment, after obtaining a plurality of reference compressed sub-packets, a decompression test can be performed on each reference compressed sub-packet to evaluate the compression rate and information loss rate of the reference compressed sub-packet. Specifically, a compression rate threshold (such as 75%) and an information loss rate threshold (such as 1%) can be pre-set. If the compression rate of the reference compressed sub-packet is less than the set compression rate threshold and / or the information loss rate is greater than the information loss rate threshold, the compression parameters of the target data compression algorithm need to be optimized again according to the compression rate threshold and the information loss rate threshold, so that the compression rate is greater than the set compression rate threshold and the information loss rate is less than the information loss rate threshold. Exemplarily, assuming that the compression rate threshold is 75% and the information loss rate threshold is 1%, in order to make the compression rate reach 75% and the information loss rate be controlled within 1%, the run length threshold of the run-length encoding can be further optimized to 10.

[0079] S250, re-compressing each data block based on the re-optimized target data compression algorithm to obtain multiple target compressed sub-packets, and determining a compressed data packet according to the target compressed sub-packets.

[0080] Exemplarily, if the run length threshold is optimized again to be 10, each data block is recompressed based on the run length encoding with the run length threshold of 10 to obtain multiple target compressed sub-packets, and the set of each target compressed sub-packet is used as a compressed data packet.

[0081] S260, converting the data format and interface protocol of the compressed data packet based on the preset data exchange format and the preset interface standard, and decompressing the converted compressed data packet to obtain standardized data.

[0082] S270, transmitting the standardized data to a pre-built data middleware layer, and distributing the standardized data to the target embedded device through the data middleware layer.

[0083] S280, obtaining current operating status information of the target embedded device, and determining whether the target embedded device has a fault according to the current operating status information.

[0084] S290: If yes, hot backup is performed on the target embedded device according to the fault type and fault degree, and fault isolation and fault recovery are performed on the target embedded device.

[0085] S2100, uses preset security protection measures to protect the data transmission, storage and processing of the target embedded device.

[0086] The preset security protection measures include encryption authentication, access control and behavior auditing. In addition, the specific implementation of S260-S2100 can refer to the relevant description in the above embodiment 1, which will not be repeated here.

[0087] The technical solution of the embodiment of the present invention determines the target data compression algorithm according to the characteristics and distribution of the original data; divides the original data into blocks to obtain multiple data blocks, and uses the target data compression algorithm to compress each data block to obtain multiple candidate compression sub-packets; uses the machine learning algorithm to preliminarily optimize the compression parameters of the target data compression algorithm according to the candidate compression sub-packets, and re-compresses each data block based on the preliminarily optimized target data compression algorithm to obtain multiple reference compression sub-packets; decompresses the reference compression sub-packets based on the preset compression index, and re-optimizes the compression parameters of the target data compression algorithm based on the test results; wherein the preset compression index includes compression rate and information loss rate; re-compresses each data block based on the re-optimized target data compression algorithm to obtain multiple target compression sub-packets, and determines the compressed data packet based on the target compression sub-packets. The technical solution can determine the optimal compression parameters by optimizing the compression parameters of the target data compression algorithm multiple times, and compresses each data block of the original data based on the target data compression algorithm with the optimal compression parameters, effectively improving the data compression amount, thereby enhancing the data compression effect, and further realizing the efficient use of resources.

[0088] Embodiment 3

[0089] Figure 3A flowchart of a device interconnection method based on an embedded system is provided for the third embodiment of the present invention. This embodiment is optimized based on the above embodiment. The specific optimization is: distributing standardized data to the target embedded device through the data middleware layer, including: encrypting and compressing the standardized data in sequence through the data middleware layer to obtain the target data; determining the cluster size of the data middleware layer according to the data volume of the target data and the preset concurrency requirements; wherein the data middleware layer adopts a distributed architecture; adopting a dynamic routing algorithm based on reinforcement learning to perform real-time analysis of the target data to determine the network congestion status, and determining the data transmission path according to the network congestion status; based on the cluster size and data transmission path of the data middleware layer, distributing the target data to the target embedded device through the data middleware layer.

[0090] like Figure 3 As shown, the method of this embodiment specifically includes the following steps:

[0091] S310, acquiring original data collected by the target embedded device, and compressing the original data to obtain a compressed data packet.

[0092] S320, converting the data format and interface protocol of the compressed data packet based on the preset data exchange format and the preset interface standard, and decompressing the converted compressed data packet to obtain standardized data.

[0093] S330, transmitting the standardized data to a pre-built data middleware layer, and performing data encryption and compression processing on the standardized data in sequence through the data middleware layer to obtain target data.

[0094] For example, during data transmission, the standardized data can be encrypted using the AES-256 encryption algorithm, and the data can be compressed using the GZIP algorithm to reduce the data packet size by 30%. The physical meaning of 30% is as follows: if the original data packet size is S bits, the compressed data packet size is S×(1-0.30)=0.70S bits.

[0095] S340, determining the cluster size of the data middleware layer according to the data volume of the target data and the preset concurrency requirement.

[0096] The data middleware layer adopts a distributed architecture. For example, when the data transmission peak arrives, the data middleware layer can achieve elastic expansion through Kubernetes, increasing the number of cluster nodes from 10 to 50, thereby improving the data processing capacity of the data middleware layer.

[0097] S350, using a dynamic routing algorithm based on reinforcement learning to perform real-time analysis on target data to determine network congestion conditions, and determining a data transmission path according to the network congestion conditions.

[0098] Exemplarily, the data middleware layer can have a built-in machine learning algorithm (i.e., a dynamic routing algorithm based on reinforcement learning) to analyze the target data in real time based on the machine learning algorithm, and dynamically optimize the data transmission path and strategy based on the analysis results. The analysis results include network congestion conditions. Exemplarily, the data transmission path can be adjusted in real time according to the network congestion conditions to reduce the end-to-end delay by 20%. The physical meaning of 20% is: if the original end-to-end delay is T seconds, then after adjustment by the dynamic routing algorithm, the delay is reduced to T×(1-0.20)=0.80T seconds.

[0099] S360, based on the cluster size and data transmission path of the data middleware layer, distributes the target data to the target embedded device through the data middleware layer.

[0100] S370, obtaining current operating status information of the target embedded device, and determining whether the target embedded device has a fault according to the current operating status information.

[0101] S380: If yes, hot backup is performed on the target embedded device according to the fault type and fault degree, and fault isolation and fault recovery are performed on the target embedded device.

[0102] S390, uses preset security protection measures to protect the data transmission, storage and processing of the target embedded device.

[0103] Among them, the preset security protection measures include encryption authentication, access control and behavior auditing.

[0104] The technical solution of the embodiment of the present invention is to encrypt and compress the standardized data in sequence through the data middleware layer to obtain the target data; determine the cluster size of the data middleware layer according to the data volume of the target data and the preset concurrency requirements; wherein the data middleware layer adopts a distributed architecture; adopt a dynamic routing algorithm based on reinforcement learning to perform real-time analysis on the target data to determine the network congestion status, and determine the data transmission path according to the network congestion status; based on the cluster size and data transmission path of the data middleware layer, distribute the target data to the target embedded device through the data middleware layer. This technical solution can dynamically adjust the cluster size and dynamically optimize the data transmission path, further improving the real-time performance of data distribution, so as to better meet the needs of time-sensitive applications.

[0105] Embodiment 4

[0106] Figure 4This is a schematic diagram of the structure of a device interconnection apparatus based on an embedded system provided in the fourth embodiment of the present invention. The device can execute the device interconnection method based on an embedded system provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Figure 4 As shown, the device comprises:

[0107] The data compression module 410 is used to obtain the original data collected by the target embedded device and compress the original data to obtain a compressed data packet;

[0108] The data conversion module 420 is used to convert the data format and interface protocol of the compressed data packet based on the preset data exchange format and the preset interface standard, and decompress the converted compressed data packet to obtain standardized data;

[0109] A data distribution module 430, configured to transmit the standardized data to a pre-built data middleware layer, and distribute the standardized data to the target embedded device through the data middleware layer;

[0110] A fault detection module 440 is used to obtain the current running status information of the target embedded device and determine whether the target embedded device has a fault according to the current running status information;

[0111] A fault processing module 450 is used to perform hot backup on the target embedded device according to the fault type and fault degree, and to perform fault isolation and fault recovery on the target embedded device if any;

[0112] The data protection module 460 is used to adopt preset security protection measures to securely protect the data transmission, storage and processing of the target embedded device; wherein the preset security protection measures include encryption authentication, access control and behavior auditing.

[0113] Optionally, the data compression module 410 is used to:

[0114] Determining a target data compression algorithm according to the characteristics and distribution of the original data;

[0115] The original data is processed into blocks to obtain a plurality of data blocks, and each of the data blocks is compressed using the target data compression algorithm to obtain a plurality of candidate compressed sub-packets;

[0116] Preliminarily optimizing the compression parameters of the target data compression algorithm according to the candidate compressed sub-packets using a machine learning algorithm, and re-compressing each of the data blocks based on the preliminarily optimized target data compression algorithm to obtain a plurality of reference compressed sub-packets;

[0117] Performing a decompression test on the reference compressed sub-packet based on a preset compression index, and re-optimizing the compression parameters of the target data compression algorithm according to the test result; wherein the preset compression index includes a compression ratio and an information loss ratio;

[0118] Based on the re-optimized target data compression algorithm, data compression is performed on each data block again to obtain multiple target compressed sub-packets, and a compressed data packet is determined according to the target compressed sub-packets.

[0119] Optionally, the data conversion module 420 is used to:

[0120] Determining the original data format and original interface protocol of the compressed data packet;

[0121] If the original data format is inconsistent with the preset data exchange format, converting the original data format into the preset data exchange format;

[0122] If the original interface protocol does not match the preset interface standard, the original interface protocol is converted into a standard protocol corresponding to the preset interface standard.

[0123] Optionally, the data distribution module 430 is used to:

[0124] The standardized data is encrypted and compressed in sequence by the data middleware layer to obtain target data;

[0125] Determine the cluster size of the data middleware layer according to the data volume of the target data and the preset concurrency requirements; wherein the data middleware layer adopts a distributed architecture;

[0126] A dynamic routing algorithm based on reinforcement learning is used to perform real-time analysis on the target data to determine a network congestion condition, and a data transmission path is determined according to the network congestion condition;

[0127] Based on the cluster size of the data middleware layer and the data transmission path, the target data is distributed to the target embedded device through the data middleware layer.

[0128] Optionally, the fault detection module 440 is used to:

[0129] Determining whether the target embedded device has a fault based on the magnitude relationship between the current operating state information and a preset fault threshold; and / or,

[0130] The current operating status information is subjected to fault prediction according to a pre-trained fault detection model, and whether the target embedded device has a fault is determined based on the prediction result; wherein the fault detection model is obtained by training based on historical operating status information of a reference embedded device.

[0131] Optionally, the fault processing module 450 is used to:

[0132] Determine a target backup component according to the fault type;

[0133] Determine a target backup method according to the fault severity;

[0134] The target backup component is hot backed up based on the target backup mode.

[0135] Optionally, the data protection module 460 is used to:

[0136] Use encryption authentication technology to authenticate the sender and receiver of data transmission, and encrypt the transmitted data;

[0137] Based on the role and authority of the access object, access control technology is used to control the access object's access to and operation of the stored data;

[0138] Based on the preset data operation rules, behavioral audit technology is used to identify abnormal behaviors of various operation behaviors in the data processing process;

[0139] The data security assessment result is determined according to the encryption authentication result, the access control result and the behavior audit result, and the preset security protection measures are dynamically adjusted according to the data security assessment result.

[0140] An embedded system-based device interconnection apparatus provided in an embodiment of the present invention can execute an embedded system-based device interconnection method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0141] Embodiment 5

[0142] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0143] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0144] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0145] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a device interconnection method based on an embedded system.

[0146] In some embodiments, the device interconnection method based on the embedded system can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the device interconnection method based on the embedded system described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the device interconnection method based on the embedded system in any other appropriate manner (for example, by means of firmware).

[0147] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0148] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0149] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0150] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0151] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0152] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0153] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0154] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A device interconnection method based on an embedded system, characterized in that: The method comprises: Acquire the original data collected by the target embedded device, and compress the original data to obtain a compressed data packet; Converting the data format and interface protocol of the compressed data packet based on a preset data exchange format and a preset interface standard, and decompressing the converted compressed data packet to obtain standardized data; Transmitting the standardized data to a pre-built data middleware layer, and distributing the standardized data to the target embedded device through the data middleware layer; Acquire current running status information of the target embedded device, and determine whether the target embedded device has a fault according to the current running status information; If so, hot backup the target embedded device according to the fault type and fault degree, and perform fault isolation and fault recovery on the target embedded device; Preset security protection measures are used to protect the data transmission, storage and processing processes of the target embedded device; wherein the preset security protection measures include encryption authentication, access control and behavior auditing.

2. The method according to claim 1, characterized in that Compressing the original data to obtain a compressed data packet includes: Determining a target data compression algorithm according to the characteristics and distribution of the original data; The original data is processed into blocks to obtain a plurality of data blocks, and each of the data blocks is compressed using the target data compression algorithm to obtain a plurality of candidate compressed sub-packets; Preliminarily optimizing the compression parameters of the target data compression algorithm according to the candidate compressed sub-packets using a machine learning algorithm, and re-compressing each of the data blocks based on the preliminarily optimized target data compression algorithm to obtain a plurality of reference compressed sub-packets; Performing a decompression test on the reference compressed sub-packet based on a preset compression index, and re-optimizing the compression parameters of the target data compression algorithm according to the test result; wherein the preset compression index includes a compression ratio and an information loss ratio; Based on the re-optimized target data compression algorithm, data compression is performed on each data block again to obtain multiple target compressed sub-packets, and a compressed data packet is determined according to the target compressed sub-packets.

3. The method according to claim 1, characterized in that Converting the data format and transmission protocol of the compressed data packet based on a preset data exchange format and a preset interface standard includes: Determining the original data format and original interface protocol of the compressed data packet; If the original data format is inconsistent with the preset data exchange format, converting the original data format into the preset data exchange format; If the original interface protocol does not match the preset interface standard, the original interface protocol is converted into a standard protocol corresponding to the preset interface standard.

4. The method according to claim 1, characterized in that: Distributing the standardized data to the target embedded device through the data middleware layer includes: The standardized data is encrypted and compressed in sequence by the data middleware layer to obtain target data; Determine the cluster size of the data middleware layer according to the data volume of the target data and the preset concurrency requirements; wherein the data middleware layer adopts a distributed architecture; A dynamic routing algorithm based on reinforcement learning is used to perform real-time analysis on the target data to determine a network congestion condition, and a data transmission path is determined according to the network congestion condition; Based on the cluster size of the data middleware layer and the data transmission path, the target data is distributed to the target embedded device through the data middleware layer.

5. The method according to claim 1, characterized in that Determining whether the target embedded device has a fault according to the current running status information includes: Determining whether the target embedded device has a fault based on the magnitude relationship between the current operating state information and a preset fault threshold; and / or, The current operating status information is subjected to fault prediction according to a pre-trained fault detection model, and whether the target embedded device has a fault is determined based on the prediction result; wherein the fault detection model is obtained by training based on historical operating status information of a reference embedded device.

6. The method according to claim 5, characterized in that Performing hot backup on the target embedded device according to the fault type and fault degree includes: Determine a target backup component according to the fault type; Determine a target backup method according to the fault severity; The target backup component is hot backed up based on the target backup mode.

7. The method according to any one of claims 1 to 6, characterized in that Preset security protection measures are used to protect the data transmission, storage and processing of the target embedded device, including: Use encryption authentication technology to authenticate the sender and receiver of data transmission, and encrypt the transmitted data; Based on the role and authority of the access object, access control technology is used to control the access object's access to and operation of the stored data; Based on the preset data operation rules, behavioral audit technology is used to identify abnormal behaviors of various operation behaviors in the data processing process; The data security assessment result is determined according to the encryption authentication result, the access control result and the behavior audit result, and the preset security protection measures are dynamically adjusted according to the data security assessment result.

8. A device interconnection device based on an embedded system, characterized in that: The device comprises: A data compression module is used to obtain the original data collected by the target embedded device and compress the original data to obtain a compressed data packet; A data conversion module, used to convert the data format and interface protocol of the compressed data packet based on a preset data exchange format and a preset interface standard, and decompress the converted compressed data packet to obtain standardized data; A data distribution module, used for transmitting the standardized data to a pre-built data middleware layer, and distributing the standardized data to the target embedded device through the data middleware layer; A fault detection module, used to obtain the current running status information of the target embedded device, and determine whether the target embedded device has a fault according to the current running status information; A fault processing module, for performing hot backup of the target embedded device according to the fault type and fault degree, and performing fault isolation and fault recovery on the target embedded device, if any; The data protection module is used to adopt preset security protection measures to securely protect the data transmission, storage and processing processes of the target embedded device; wherein the preset security protection measures include encryption authentication, access control and behavior auditing.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the device interconnection method based on an embedded system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the device interconnection method based on an embedded system according to any one of claims 1 to 7 when executed.

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