A data interaction method and system for Internet of Things based on data collection

Abstract

The present invention discloses an Internet of Things data interaction method and system based on data collection, which relates to the technical field of the Internet of Things, and solves the problem that computing power resources cannot be reasonably allocated or data interaction process processing is not comprehensive enough. The present invention cleverly performs computing power scheduling by accurately locating load processes and lightweight processes in units of 1Hash; not only can the conversion rate of the load process be quickly improved at the moment, but also a curve can be virtually drawn according to the previous scheduling effect, and the total unit computing power required for the subsequent period can be forward-lookingly calculated, and computing power resources can be continuously and accurately allocated to ensure that multiple data interaction processes always maintain an efficient data conversion rate, so that the entire Internet of Things system can achieve optimal resource utilization under different load conditions, ensure the fluency and timeliness of data interaction, and inject strong impetus into the large-scale application and in-depth development of the Internet of Things.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things data interaction method and system based on data collection. Background Art

[0002] The patent application with the publication number CN117041380A discloses an Internet of Things data interaction system and an Internet of Things data interaction method. The Internet of Things data interaction system includes: a multi-protocol terminal, including a main protocol module and multiple sub-protocol modules, the multi-protocol terminal is configured to receive the working condition data of large-tonnage engineering lifting machinery, and to package the working condition data through the main protocol module and multiple sub-protocol modules respectively and upload the packaged data to the cloud platform; the cloud platform communicates with the multi-protocol terminal through a multi-link manner, and is configured to parse the packaged data according to the protocol corresponding to the packaged data, and to store and manage the parsed data. This application integrates multiple protocol terminal hardware into one multi-protocol terminal, which is conducive to streamlining the system structure, reducing repeated data collection and measurement work, and reducing the overall installation, debugging and operation and maintenance costs of the system;

[0003] Traditional methods often lack intelligent judgment mechanisms when facing data interaction requests between IoT devices. They either build complex interaction logic in a blanket manner, resulting in a waste of resources; or fail to quickly adapt when the encoding is inconsistent, delaying the interaction.

[0004] In an IoT system with multiple devices and multiple processes running simultaneously, the data interaction logic of newly connected devices may conflict or duplicate with existing processes, lacking effective identification and control, which can easily disrupt system stability. On the other hand, when the system is unbalanced in load, it is impossible to reasonably allocate computing resources, and some processes often convert slowly due to insufficient computing power, while other processes have idle computing power. This application uses precise interaction logic identification, conversion rate analysis, and innovative computing power allocation methods to ensure efficient and stable operation of multiple processes and achieve optimal configuration of system resources. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an Internet of Things data interaction method and system based on data collection, which solves the problem that computing resources cannot be reasonably allocated or the data interaction process is not fully processed.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data interaction method of the Internet of Things based on data collection, comprising the following steps:

[0007] Step 1: confirm several groups of IoT devices that need to interact with each other, and determine the data encoding compatible with the corresponding IoT devices based on the device data of the corresponding IoT devices;

[0008] Step 2: Receive the data interaction request generated by the IoT device, and based on the received data interaction request, determine the specified object that the IoT device needs to interact with from the data interaction request, and check the data encoding between the IoT device and the specified object, and based on the check result, assess whether it is necessary to build the relevant data interaction process in real time. The specific sub-steps are:

[0009] S21. Based on the received data interaction request, the IoT device that generates the data interaction request is taken as the main object, and the IoT device to be interacted with by the data interaction request is taken as the designated object;

[0010] S22, confirm whether the data coding compatible between the main object and the designated object is the same coding, if it is the same coding, directly perform data interaction, if not the same coding, confirm the data coding of the main object and the designated object, and perform data conversion on the determined data coding to confirm the interaction logic, build a data interaction process between the main object and the designated object based on the confirmed interaction logic, perform code conversion on the interaction data generated by the main object and the designated object in the data interaction process, convert the interaction data generated by the main object into a data coding format compatible with the designated object and perform interaction;

[0011] Step 3: Based on the data interaction process that has been constructed in the system, confirm the interaction logic associated with the data interaction request, and identify whether this interaction logic already exists in the constructed data interaction process. If so, analyze and confirm the data conversion rate to assess whether this data interaction process can execute the interaction logic associated with this data interaction request. If not, construct a new data interaction process based on this interaction logic again. The specific sub-steps are:

[0012] S31, confirm the data interaction request generated subsequently, and confirm the associated interaction logic based on the data interaction request, identify whether the interaction logic exists in the data interaction process constructed in the system, and different data interaction processes have different interaction logics. If it exists, the data interaction process is used as the process to be monitored, and step S32 is executed to analyze and confirm the data conversion rate of the process to be monitored. If it does not exist, the data interaction process corresponding to its interaction logic is constructed in the same way as steps S21-S22;

[0013] S32, based on the process to be monitored associated with the current interactive logic, the first group of data that needs to be interacted by the interactive logic is converted through the process to be monitored, and the data conversion rate of the process to be monitored in this data conversion process is monitored in real time, and the conversion rates of several groups of data monitored in this data conversion process are averaged to confirm the conversion mean J1 associated with this data conversion process;

[0014] And confirm several groups of data conversion rates of the process to be monitored in the past conversion process, and then confirm the average value J2, the average value confirmed this time does not include the data conversion rate associated with J1;

[0015] Identify whether J1 and J2 satisfy: |J1-J2|≤Y1, where Y1 is a preset value. If so, place this interaction logic in the process to be monitored and execute it. If not, based on this interaction logic, use the same method as steps S21-S22 to reconstruct a set of data interaction processes about this interaction logic, and perform data interaction between corresponding IoT devices.

[0016] Step 4: Based on the several groups of data interaction processes built in the current system, the data conversion rate associated with each group of data interaction processes is monitored in real time, and the load process is evaluated based on the specific parameters of the real-time monitoring. Then, based on the parameter manifestation of other data interaction processes, computing power is allocated to ensure the data conversion rate of multiple data interaction processes. The specific sub-steps are:

[0017] S41, based on different data conversion rates of different data interaction processes at different times, generating data conversion rate change curves belonging to different data interaction processes in real time;

[0018] And confirm the mean standard line of the data conversion rate change curve generated in real time: from the several groups of data conversion rates generated in the data conversion rate change curve, confirm the mean of several groups of data conversion rates in real time, and construct the mean standard line belonging to this data conversion rate change curve based on the mean;

[0019] S42, based on the data conversion rate change curve and the mean standard line generated in real time by the corresponding data interaction process, in the data conversion rate change curve, if the data conversion rate generated in real time is lower than the mean standard line, the data interaction process at the current moment is marked as the load process, and the current moment is marked as the reference moment;

[0020] S43, based on the determined reference time, identifying a lightweight process from other data interaction processes: identifying whether there is a curve with a data conversion rate higher than the mean standard line at the reference time in other data conversion rate change curves; if so, marking the data interaction process associated with the data conversion rate change curve as a lightweight process;

[0021] A unit of computing power is dispatched from each group of lightweight processes to the load process, where the unit is a preset unit. Based on the load characteristics of the load process, computing power is dispatched from different lightweight processes in subsequent different time periods to reduce the load of the load process.

[0022] Preferably, in step S43, the specific method of scheduling computing power from different lightweight processes is:

[0023] S431, confirm the rate change value generated by the load process in different unit time periods before and after the computing power scheduling, calibrate the data conversion rate of the load process in the first unit time before the computing power scheduling as V2, calibrate the data conversion rate of the load process at the current time as V1, calibrate the data conversion rate of the load process in a unit time after the computing power scheduling as V3, use (V1-V2)=Q1 to confirm the rate change feature Q1 of the previous unit time period, and then use (V3-V1)=Q2 to confirm the rate change feature Q2 of the next unit time period, and use Q2-Q1=PQ to determine the curve characteristic value PQ generated by the current scheduling feature;

[0024] S432. Based on the determined curve characteristic value PQ, the subsequent curves of the load process data conversion rate change curve are virtually drawn. The rate change characteristics of each time period are increased by a group of PQ values ​​compared with the previous time period. Similarly, when the data conversion rate at the end point of the virtually drawn curve is in the standard area of ​​the mean standard line, the total number of virtually drawn time periods G is recorded, and the number F of lightweight processes participating in the computing power allocation in this scheduling process is confirmed. The total number of units ZS required for this load process is confirmed by using: G×F=ZS;

[0025] S433. In each subsequent unit time, the lightweight process is confirmed, and one unit of computing power is dispatched from the confirmed lightweight process to the load process in each unit time, until the total computing power value allocated by the load process reaches ZS.

[0026] Preferably, in step S43, if there is no curve with a data conversion rate higher than the mean standard line at the reference time, an error signal is directly generated and displayed.

[0027] Preferably, an IoT data interaction system based on data collection comprises:

[0028] The coding format confirmation end confirms several groups of IoT devices that need to interact with each other, and determines the data coding compatible with the corresponding IoT devices based on the device data of the corresponding IoT devices;

[0029] The interactive process building end receives the data interaction request generated by the IoT device, and based on the received data interaction request, determines from the data interaction request the designated object that the IoT device needs to interact with, and checks the data coding between the IoT device and the designated object, and based on the check result, determines whether it is necessary to build the relevant data interaction process in real time;

[0030] The process logic identification end confirms the interaction logic associated with the data interaction request based on the data interaction process that has been built in the system, and identifies whether this interaction logic already exists in the built data interaction process. If so, it analyzes and confirms the data conversion rate to assess whether this data interaction process can execute the interaction logic associated with this data interaction request. If not, it builds a new data interaction process based on this interaction logic again;

[0031] The process computing power allocation end, based on several groups of data interaction processes constructed in the current system, monitors the data conversion rate associated with each group of data interaction processes in real time, and evaluates the load process based on the specific parameters of real-time monitoring. Then, based on the parameters of other data interaction processes, it allocates computing power to ensure the data conversion rate of multiple data interaction processes.

[0032] The present invention provides an Internet of Things data interaction method and system based on data collection. Compared with the prior art, it has the following beneficial effects:

[0033] When receiving a data interaction request, the data encoding of the main object and the specified object is carefully checked, and based on the check result, it is intelligently determined whether a new data interaction process needs to be built; this dynamic construction mechanism not only avoids unnecessary waste of resources, but also allows direct interaction when the encoding is consistent, and can quickly build adaptive interaction logic and processes when the encoding is different, ensuring that data conversion and interaction proceed in an orderly manner, so that the entire IoT system can flexibly respond to diverse data interaction needs and adapt to the ever-changing device connection and data transmission scenarios;

[0034] With the help of the real-time generated data conversion rate change curve and mean standard line, the load process and lightweight process are accurately located, and the computing power is cleverly scheduled in units of 1Hash. Not only can the conversion rate of the load process be quickly improved at the moment, but the curve can also be virtually drawn based on the previous scheduling effect, and the total unit computing power required for the subsequent period can be forward-lookingly calculated. The computing power resources are continuously and accurately allocated to ensure that multiple data interaction processes always maintain an efficient data conversion rate, so that the entire Internet of Things system can achieve optimal resource utilization under different load conditions, ensure the smoothness and timeliness of data interaction, and inject strong impetus into the large-scale application and in-depth development of the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention;

[0036] Figure 2 It is a schematic diagram for determining the load process of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0038] Example 1

[0039] See also Figure 1 , the present application provides an IoT data interaction method based on data collection, comprising the following steps:

[0040] Step 1: Confirm several groups of IoT devices that need to interact with each other, and determine the data encoding compatible with the corresponding IoT devices based on the device data of the corresponding IoT devices (device data is preset description data, generally stored in the cloud, and data can be directly extracted) (different IoT devices can interact with related data in different data formats, and different data formats correspond to different data encodings, including ASILL encoding or UTF-8 encoding, etc. Different types of encodings present different data forms, so IoT devices related to different data encodings need to unify the data format before conversion, and perform real-time unification and conversion to interact with IoT devices);

[0041] Step 2: Receive the data interaction request generated by the IoT device, and based on the received data interaction request, determine the designated object that the IoT device needs to interact with from the data interaction request, and check the data coding between the IoT device and the designated object, and assess whether it is necessary to build the relevant data interaction process in real time based on the check result. Specifically, it is proposed that there are three IoT devices A, B and C, where A requests data interaction with B, then data interaction between A and B is required. When the data coding form between A and B is consistent, then there is no need to unify the interaction logic or data format, and the interaction processing can be directly performed. When the data coding form between A and B is inconsistent, it is necessary to unify the interaction logic or data format and perform interaction processing;

[0042] The specific sub-steps of checking are as follows:

[0043] S21. Based on the received data interaction request, the IoT device that generates the data interaction request is taken as the main object, and the IoT device to be interacted with by the data interaction request is taken as the designated object;

[0044] S22, confirm whether the data coding compatible between the main object and the designated object is the same coding, if it is the same coding, directly perform data interaction, if not the same coding, confirm the data coding of the main object and the designated object, and perform data conversion on the determined data coding to confirm the interaction logic, and build a data interaction process between the main object and the designated object based on the confirmed interaction logic, and perform coding conversion on the interaction data generated by the main object and the designated object in the data interaction process, and convert the interaction data generated by the main object into a data coding format compatible with the designated object and interact (that is, the corresponding data interaction process needs to unify the data coding format, and interact after completing the data unification, and complete the corresponding interaction process. If the designated object still needs to interact with the main object, the corresponding data interaction request is regenerated, and a set of data interaction processes is rebuilt;

[0045] The interaction logic is the conversion between data encoding formats. The data interaction process includes the main object and the specified object, that is, the main object A and the specified object B. Then the data interaction process is the data conversion process between the main object A and the specified object B. Its interaction logic is the conversion between the corresponding data encoding formats of A or B, which is only for the format, not for the object);

[0046] Step 3: Based on the data interaction process that has been constructed in the system, confirm the interaction logic associated with the data interaction request, and identify whether this interaction logic already exists in the constructed data interaction process. If so, analyze and confirm the data conversion rate, and evaluate whether this data interaction process can execute the interaction logic associated with this data interaction request. If not, build a new data interaction process based on this interaction logic again (specifically, it is impossible for a system to have only a single data interaction process. Generally, there will be multiple data interaction processes. When different devices interact with each other, different data interaction logics are used to perform specific conversions between data formats. Therefore, different conversion processes are associated with different data interaction processes. Different data interaction processes execute different interaction logics and complete specific conversions of data encodings);

[0047] The specific sub-steps for identification are:

[0048] S31. Confirm the data interaction request generated subsequently, and confirm the associated interaction logic based on the data interaction request, identify whether the interaction logic exists in the data interaction process constructed in the system, and different data interaction processes have different interaction logics (there is a corresponding interaction logic in each group of data interaction processes, and the interaction logic is the format conversion between the corresponding encoded data. For example, if object A is format converted to object B, a group of data interaction processes for converting object A to object B is constructed. If object B needs to be format converted to object A, the data interaction process is reconfirmed). If it exists, the data interaction process is used as the process to be monitored, and step S32 is executed to analyze and confirm the data conversion rate of the process to be monitored. If it does not exist, the construction of the data interaction process corresponding to its interaction logic is completed in the same way as steps S21-S22;

[0049] S32, based on the process to be monitored associated with the current interactive logic, the first group of data that needs to be interacted by the interactive logic is converted through the process to be monitored, and the data conversion rate of the process to be monitored in this data conversion process is monitored in real time, and the conversion rates of several groups of data monitored in this data conversion process are averaged to confirm the conversion mean J1 associated with this data conversion process;

[0050] And confirm several groups of data conversion rates of the process to be monitored in the past conversion process, and then confirm the average value J2, the average value confirmed this time does not include the data conversion rate associated with J1;

[0051] Identify whether J1 and J2 satisfy: |J1-J2|≤Y1 (used to monitor the fluctuation state of the corresponding process to be monitored after the data conversion process of other devices is involved), where Y1 is a preset value, and its specific value is determined by the operator based on experience, and Y1 is generally 15. If it satisfies, then this interaction logic is placed in the process to be monitored and executed. Subsequently, this process to be monitored not only converts the data of the original interaction logic, but also synchronously converts the format of data encoding for the IoT devices associated with this interaction logic. If it does not satisfy, then based on this interaction logic, a set of data interaction processes related to this interaction logic is reconstructed in the same way as steps S21-S22, and data interaction between corresponding IoT devices is performed;

[0052] Specifically, although the data encodings associated with some devices are the same, such as AB or CD (where "-" represents the format conversion from object A to object B or from object C to object D), the data format compatible with A and C is G1, and the data format compatible with B and D is G2. The interaction logic associated with the data interaction process of AB is G1-G2, and the interaction logic confirmed by CD is also G1-G2. Therefore, the interaction logic confirmed by CD exists in the data interaction process of AB. Because in the data interaction process, there may be a locking mechanism for shared resources (such as database connections, file systems, etc.); when CD intervenes , may conflict with AB due to the order or time of acquiring locks; for example, AB is updating the database table storing data format conversion rules (used to convert from G1 to G2), and CD also tries to obtain the lock of the same table to read the rules, which will cause AB's operation to be blocked until CD completes the reading or times out, thereby slowing down AB's data interaction process. In order to avoid such situations, it is necessary to rebuild the data interaction process to ensure the data interaction rate between the corresponding IoT devices. That is, when the data interaction frequencies of the devices associated with the two interaction logics are relatively close, it will cause the problem of too slow data conversion rate;

[0053] Step 4: Based on several groups of data interaction processes constructed in the current system, the data conversion rate associated with each group of data interaction processes is monitored in real time, and based on the specific parameters of real-time monitoring, the load process is evaluated, and then based on the parameter manifestation of other data interaction processes, computing power is allocated to ensure the data conversion rate of multiple data interaction processes. Specifically, when some processes perform different data encoding format conversions, some processes have a slower conversion process in some periods, but other processes have a faster conversion process in this period. In this case, computing power can be allocated from some processes with faster conversion processes to improve the conversion rate of the processes with slow conversion rates;

[0054] The specific sub-steps for computing power allocation are:

[0055] S41, based on different data conversion rates of different data interaction processes at different times, generating data conversion rate change curves belonging to different data interaction processes in real time;

[0056] Combination Figure 2, and confirm the mean standard line of the data conversion rate change curve generated in real time: from the several groups of data conversion rates generated in the data conversion rate change curve, confirm the mean of several groups of data conversion rates in real time, and construct the mean standard line belonging to this data conversion rate change curve based on the mean. Specifically, the horizontal coordinate axis of this curve is the time line, and the vertical coordinate axis is the rate parameter. The mean standard line is a standard line perpendicular to the vertical coordinate axis and parallel to the horizontal coordinate axis, which is used for boundary assessment;

[0057] S42, based on the data conversion rate change curve and the mean standard line generated in real time by the corresponding data interaction process, in the data conversion rate change curve, if the data conversion rate generated in real time is lower than the mean standard line, the data interaction process at the current moment is marked as the load process, and the current moment is marked as the reference moment;

[0058] S43, based on the determined reference time, identify the lightweight process from other data interaction processes: identify whether there is a curve with a data conversion rate higher than the mean standard line at the reference time in other data conversion rate change curves, if so, mark the data interaction process associated with the data conversion rate change curve as a lightweight process, if not, directly generate an error signal for display, generally, the full load situation will occur when there is a fault in the system or the processing end;

[0059] Dispatch one unit of computing power from each group of lightweight processes to the load process, where the unit is a preset unit, which is determined by the relevant operators based on experience, generally 1Hash. After the scheduling is completed, record the rate changes of the lightweight process and the load process:

[0060] S431. Confirm the rate change value generated by the load process in different unit time periods before and after computing power scheduling, calibrate the data conversion rate of the load process in the first unit time before computing power scheduling as V2, calibrate the data conversion rate of the load process at the current time as V1, and calibrate the data conversion rate of the load process in a unit time after computing power scheduling as V3 (for example, in order to confirm the rate change between time periods, it is necessary to be based on the corresponding time period change characteristics), use (V1-V2)=Q1 to confirm the rate change characteristic Q1 of the previous unit time period, and then use (V3-V1)=Q2 to confirm the rate change characteristic Q2 of the next unit time period (that is, to obtain the relevant characteristics after computing power scheduling), and use Q2-Q1=PQ to determine the curve characteristic value PQ generated by the current scheduling characteristics (that is, the increase value of the corresponding curve rate after scheduling compared with the previous time period);

[0061] S432, based on the determined curve characteristic value PQ, virtually draw the subsequent curves of the load process data conversion rate change curve, and the rate change characteristics of each time period are increased by a group of PQ values ​​compared with the previous time period, and so on, until the data conversion rate at the end point of the virtually drawn curve is in the standard area of ​​the mean standard line (that is, |J1-J2|≤Y1), record the total number of virtually drawn time periods G, and confirm the number of lightweight processes F participating in the computing power allocation in this scheduling process, and use: G×F=ZS to confirm the total number of units ZS required by this load process (here G is the total number of specific time periods corresponding to the scheduling, but the average number of computing power allocated in each time period in this scheduling process is 1, that is, each lightweight process allocates one unit of computing power in each time period, then when the G value is determined, each lightweight process allocates one unit of computing power in each time period, and continues for G time periods, that is, each lightweight process needs to allocate G units of computing power respectively, and can be directly allocated to ensure that the load process is not in a load state);

[0062] S433. In each subsequent unit time, the lightweight process is confirmed, and one unit of computing power is dispatched from the confirmed lightweight process to the load process in each unit time, until the total computing power value allocated by the load process reaches ZS.

[0063] Example 2

[0064] An Internet of Things data interaction system based on data collection, comprising:

[0065] The coding format confirmation end confirms several groups of IoT devices that need to interact with each other, and determines the data coding compatible with the corresponding IoT devices based on the device data of the corresponding IoT devices;

[0066] The interactive process building end receives the data interaction request generated by the IoT device, and based on the received data interaction request, determines from the data interaction request the designated object that the IoT device needs to interact with, and checks the data coding between the IoT device and the designated object, and based on the check result, determines whether it is necessary to build the relevant data interaction process in real time;

[0067] The process logic identification end confirms the interaction logic associated with the data interaction request based on the data interaction process that has been built in the system, and identifies whether this interaction logic already exists in the built data interaction process. If so, it analyzes and confirms the data conversion rate to assess whether this data interaction process can execute the interaction logic associated with this data interaction request. If not, it builds a new data interaction process based on this interaction logic again;

[0068] The process computing power allocation end, based on several groups of data interaction processes constructed in the current system, monitors the data conversion rate associated with each group of data interaction processes in real time, and evaluates the load process based on the specific parameters of real-time monitoring. Then, based on the parameters of other data interaction processes, it allocates computing power to ensure the data conversion rate of multiple data interaction processes.

[0069] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0070] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data interaction method for the Internet of Things based on data collection, characterized in that: The following steps are involved: Step 1: confirm several groups of IoT devices that need to interact with each other, and determine the data encoding compatible with the corresponding IoT devices based on the device data of the corresponding IoT devices; Step 2: receiving the data interaction request generated by the IoT device, and based on the received data interaction request, determining the designated object with which the IoT device needs to interact from the data interaction request, and checking the data coding between the IoT device and the designated object, and assessing whether it is necessary to build a relevant data interaction process in real time based on the checking result; Step 3: Based on the data interaction process that has been constructed in the system, confirm the interaction logic associated with the data interaction request, and identify whether this interaction logic already exists in the constructed data interaction process. If so, analyze and confirm the data conversion rate to assess whether this data interaction process can execute the interaction logic associated with this data interaction request. If not, construct a new data interaction process based on this interaction logic again; Step 4: Based on the several groups of data interaction processes built in the current system, the data conversion rate associated with each group of data interaction processes is monitored in real time, and the load process is evaluated based on the specific parameters of the real-time monitoring. Then, based on the parameter manifestation of other data interaction processes, computing power is allocated to ensure the data conversion rate of multiple data interaction processes. The specific sub-steps are: S41, based on different data conversion rates of different data interaction processes at different times, generating data conversion rate change curves belonging to different data interaction processes in real time; And confirm the mean standard line of the data conversion rate change curve generated in real time: from the several groups of data conversion rates generated in the data conversion rate change curve, confirm the mean of several groups of data conversion rates in real time, and construct the mean standard line belonging to this data conversion rate change curve based on the mean; S42, based on the data conversion rate change curve and the mean standard line generated in real time by the corresponding data interaction process, in the data conversion rate change curve, if the data conversion rate generated in real time is lower than the mean standard line, the data interaction process at the current moment is marked as the load process, and the current moment is marked as the reference moment; S43, based on the determined reference time, identifying the lightweight process from other data interaction processes: identifying whether there is a curve with a data conversion rate higher than the mean standard line at the reference time in other data conversion rate change curves; if not, directly generating an error signal for direct display; if so, marking the data interaction process associated with the data conversion rate change curve as a lightweight process; Dispatch one unit of computing power from each group of lightweight processes to the load process, where the unit is a preset unit. Based on the load characteristics of the load process, in different subsequent time periods, perform computing power dispatch from different lightweight processes to reduce the load of the load process. The specific method is as follows: S431, confirm the rate change value generated by the load process in different unit time periods before and after the computing power scheduling, calibrate the data conversion rate of the load process in the first unit time before the computing power scheduling as V2, calibrate the data conversion rate of the load process at the current time as V1, calibrate the data conversion rate of the load process in a unit time after the computing power scheduling as V3, use (V1-V2)=Q1 to confirm the rate change feature Q1 of the previous unit time period, and then use (V3-V1)=Q2 to confirm the rate change feature Q2 of the next unit time period, and use Q2-Q1=PQ to determine the curve characteristic value PQ generated by the current scheduling feature; S432. Based on the determined curve characteristic value PQ, the subsequent curves of the load process data conversion rate change curve are virtually drawn. The rate change characteristics of each time period are increased by a group of PQ values ​​compared with the previous time period. Similarly, when the data conversion rate at the end point of the virtually drawn curve is in the standard area of ​​the mean standard line, the total number of virtually drawn time periods G is recorded, and the number F of lightweight processes participating in the computing power allocation in this scheduling process is confirmed. The total number of units ZS required for this load process is confirmed by using: G×F=ZS; S433. In each subsequent unit time, the lightweight process is confirmed, and one unit of computing power is dispatched from the confirmed lightweight process to the load process in each unit time, until the total computing power value allocated by the load process reaches ZS.

2. The method for data interaction of the Internet of Things based on data collection according to claim 1, characterized in that: In step 2, the specific sub-steps for evaluating whether it is necessary to build the relevant data interaction process in real time based on the verification result are: S21. Based on the received data interaction request, the IoT device that generates the data interaction request is taken as the main object, and the IoT device to be interacted with by the data interaction request is taken as the designated object; S22. Confirm whether the data encodings compatible between the main object and the designated object are the same. If they are the same, directly perform data interaction. If they are not the same, confirm the data encodings of the main object and the designated object, and perform data conversion on the determined data encodings to confirm the interaction logic. Based on the confirmed interaction logic, build a data interaction process between the main object and the designated object. In the data interaction process, perform code conversion on the interaction data generated by the main object and the designated object, and convert the interaction data generated by the main object into a data encoding format compatible with the designated object for interaction.

3. The method for data interaction of the Internet of Things based on data collection according to claim 2 is characterized in that: In step 3, the specific sub-steps for identifying whether the interaction logic already exists in the constructed data interaction process are: S31, confirm the data interaction request generated subsequently, and confirm the associated interaction logic based on the data interaction request, identify whether the interaction logic exists in the data interaction process constructed in the system, and different data interaction processes have different interaction logics. If it exists, the data interaction process is used as the process to be monitored, and step S32 is executed to analyze and confirm the data conversion rate of the process to be monitored. If it does not exist, the data interaction process corresponding to its interaction logic is constructed in the same way as steps S21-S22; S32, based on the process to be monitored associated with the current interactive logic, the first group of data that needs to be interacted by the interactive logic is converted through the process to be monitored, and the data conversion rate of the process to be monitored in this data conversion process is monitored in real time, and the conversion rates of several groups of data monitored in this data conversion process are averaged to confirm the conversion mean J1 associated with this data conversion process; And confirm several groups of data conversion rates of the process to be monitored in the past conversion process, and then confirm the average value J2, the average value confirmed this time does not include the data conversion rate associated with J1; Identify whether J1 and J2 satisfy: |J1-J2|≤Y1, where Y1 is a preset value. If so, place this interaction logic in the process to be monitored and execute it. If not, based on this interaction logic, use the same method as steps S21-S22 to reconstruct a set of data interaction processes about this interaction logic, and perform data interaction between corresponding IoT devices.

4. An Internet of Things data interaction system based on data collection, the interaction system operates according to an Internet of Things data interaction method based on data collection according to any one of claims 1 to 3, characterized in that: include: The coding format confirmation end confirms several groups of IoT devices that need to interact with each other, and determines the data coding compatible with the corresponding IoT devices based on the device data of the corresponding IoT devices; The interactive process building end receives the data interaction request generated by the IoT device, and based on the received data interaction request, determines from the data interaction request the designated object that the IoT device needs to interact with, and checks the data coding between the IoT device and the designated object, and based on the check result, determines whether it is necessary to build the relevant data interaction process in real time; The process logic identification end confirms the interaction logic associated with the data interaction request based on the data interaction process that has been built in the system, and identifies whether this interaction logic already exists in the built data interaction process. If so, it analyzes and confirms the data conversion rate to assess whether this data interaction process can execute the interaction logic associated with this data interaction request. If not, it builds a new data interaction process based on this interaction logic again; The process computing power allocation end, based on several groups of data interaction processes constructed in the current system, monitors the data conversion rate associated with each group of data interaction processes in real time, and evaluates the load process based on the specific parameters of real-time monitoring. Then, based on the parameters of other data interaction processes, it allocates computing power to ensure the data conversion rate of multiple data interaction processes.

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