Intelligent equipment regulation and control method and system based on device data analysis

Abstract

The invention discloses an intelligent equipment regulation and control method and system based on device data analysis, and relates to the technical field of data analysis, and the method comprises the steps: matching a first evaluation parameter in each control mode through equipment parameter information, confirming the corresponding first evaluation parameter in each control mode, and laying a stable foundation for subsequent equipment regulation and control. And the control period of the target intelligent equipment in each control mode is set in combination with the equipment configuration information and the first evaluation parameter, so that the adaptability of equipment regulation and control is improved. And analyzing the control process of the target intelligent equipment in the previous control period, regulating and controlling the control process in the next control period according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control period, and evaluating the control condition of the previous control period. And the configuration parameters in the optimization algorithm of the next control period are adaptively adjusted, so that the equipment regulation and control accuracy is improved, the efficient and stable operation of the intelligent equipment is ensured, and the regulation and control effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a smart device control method and system based on device data analysis. Background Art

[0002] With the widespread application of the Internet of Things, cloud computing and big data technologies, the management and control of smart devices are becoming more and more intelligent. The Internet of Things technology realizes the interconnection between devices through sensors, communication technology and cloud computing platforms, providing data support and communication foundation for device management and control. Sensor technology is the key to smart devices obtaining environmental information, which can monitor the status and environmental parameters around the device in real time. Cloud computing and big data technologies provide powerful data storage, processing and analysis capabilities for smart device management, making it possible to collect, store, analyze and visualize device data. These technologies provide strong support for the precise control and optimization of smart devices, and promote the development and application of smart device control technology.

[0003] In the prior art, when smart devices are regulated, they find the optimal solution according to the optimization algorithm as the control basis. However, the configuration parameters in the optimization algorithm are often fixed, resulting in low adaptability and accuracy of smart device regulation, and cannot guarantee the safe and reliable operation of smart devices.

[0004] Therefore, how to improve the adaptability and accuracy of smart device regulation is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the problem of low adaptability and accuracy of intelligent device control in the prior art, and the proposed intelligent device control method based on device data analysis is applied to a target intelligent device including multiple data acquisition devices, which includes:

[0006] Obtaining basic information and control mode of the target smart device, the basic information including device configuration information and device parameter information, and matching the first evaluation parameter in each control mode through the device parameter information;

[0007] Combining the device configuration information and the first evaluation parameter to set a control cycle of the target smart device in each control mode, analyzing a control process of the target smart device in a control cycle, and generating a second evaluation parameter;

[0008] According to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, the control process in the next control cycle is regulated to generate the target parameter of the target intelligent device;

[0009] The target parameters of the target intelligent device are analyzed to form a control instruction for the next control cycle of the target intelligent device, and the target intelligent device is controlled through the control instruction.

[0010] In some embodiments of the present application, the first evaluation parameter in each control mode is matched by device parameter information, including:

[0011] Filter out all categories of first evaluation parameters in the device parameter information, filter out the first evaluation parameters representing each control mode according to the characteristics of the first evaluation parameters, perform correlation analysis on the representative first evaluation parameters, and then filter out the first evaluation parameters related to the representative first evaluation parameters and record them as other evaluation parameters, so as to match the first evaluation parameters under each control mode;

[0012] In each control mode, the evaluation weight of each first evaluation parameter is allocated according to the correlation between other evaluation parameters and the representative first evaluation parameter, and the evaluation weight is marked in the matching relationship of the control mode.

[0013] In some embodiments of the present application, the control period of the target smart device in each control mode is set in combination with the device configuration information and the first evaluation parameter, including:

[0014] The equipment configuration information includes data acquisition device information, functional requirement information, environmental change information and energy efficiency ratio information, and the acquisition device robustness index, functional robustness index, environmental robustness index and energy efficiency ratio robustness index under each control mode are determined according to the data acquisition device information, functional requirement information, environmental change information and energy efficiency ratio information respectively;

[0015] Determine the robustness level of the target intelligent device in each control mode based on the acquisition device robustness index, the functional robustness index, the environmental robustness index, the energy efficiency ratio robustness index and the first evaluation parameter;

[0016]

[0017] in, is the robustness level of the target intelligent device under the i1th control mode, 4 represents the robustness index of the acquisition device, the robustness index of the function, the robustness index of the environment, and the robustness index of the energy efficiency ratio. is the weight of the i2th robust indicator under the i1th control mode of the target intelligent device, is the i2th robust indicator of the target intelligent device under the i1th control mode, is the number of the first evaluation parameters in the i1th control mode of the target intelligent device, is the evaluation weight of the i3th first evaluation parameter under the i1th control mode of the target intelligent device, is the i3th first evaluation parameter of the target intelligent device under the i1th control mode, k1 and k2 are preset constants, and [] is a rounding symbol;

[0018] Different robustness levels correspond to different control cycle sizes, thereby setting the control cycle of the target intelligent device in each control mode.

[0019] In some embodiments of the present application, the control process of the target smart device in the previous control cycle is analyzed to generate the second evaluation parameter, including:

[0020] Generate an expected target curve of the target parameter corresponding to the previous control cycle according to the algorithm content of the control process in the previous control cycle of the target intelligent device, mark the realization point or realization segment on the expected target curve, and the point value or range value of the target parameter of the realization point or realization segment, so as to divide the expected target curve into two parts, a first curve and a second curve;

[0021] An actual target curve of a target parameter in a control cycle of a target intelligent device is obtained, and the actual target curve is divided into two parts, an unrealized target curve and an realized target curve, by a realization point or a realization segment. The unrealized target curve and the realized target curve are continuous in time.

[0022] The first curve is compared with the unrealized target curve, and the second curve is compared with the realized target curve to generate a second evaluation parameter.

[0023] In some embodiments of the present application, the first curve is compared with the unrealized target curve, and the second curve is compared with the realized target curve to generate a second evaluation parameter, including:

[0024] comparing the length, height and slope between the first curve and the unrealized target curve to form a control effect of the unrealized target curve;

[0025] Compare the height, slope and frequency of abnormal points between the second curve and the achieved target curve to form the control effect of the achieved target curve;

[0026] The control effect of the unrealized target curve and the control effect of the realized target curve are combined to obtain a control effect index, and the control effect index is used as the second evaluation parameter.

[0027] In some embodiments of the present application, the control process in the next control cycle is regulated according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle to generate the target parameter of the target smart device, including:

[0028] defining a third evaluation index according to the first evaluation parameter and the second evaluation parameter;

[0029]

[0030] Among them, D is the third evaluation index, τ is the conversion coefficient, is the number of the first evaluation parameters in the i1th control mode of the target intelligent device, is the evaluation weight of the i3th first evaluation parameter under the i1th control mode of the target intelligent device, is the i3th first evaluation parameter of the target intelligent device under the i1th control mode, σ is the control effect index, and k3 is a preset constant;

[0031] Setting a third evaluation index threshold and a first evaluation parameter threshold according to the algorithm content of the control process in the previous control cycle of the target intelligent device;

[0032] If the third evaluation indicator is not greater than the third evaluation indicator threshold, the first evaluation parameter is compared with the first evaluation parameter threshold to screen out the first evaluation parameter that does not meet the requirements, and the configuration parameters in the optimization algorithm are adjusted according to the first evaluation parameters that do not meet the requirements. The optimization algorithm after adjusting the configuration parameters is used to optimize the control parameters of the target intelligent device, and the optimization results are used as the target parameters of the target intelligent device.

[0033] In some embodiments of the present application, the method further includes:

[0034] If the third evaluation index is greater than the third evaluation index threshold, the configuration parameters in the optimization algorithm of the control process in the previous control cycle on the target smart device are not adjusted, and the optimization is performed, and the optimization result is used as the target parameter of the target smart device.

[0035] In some embodiments of the present application, the target parameters of the target smart device are analyzed to form a control instruction for the next control cycle of the target smart device, including:

[0036] The target parameters of the target intelligent device are formed into control instructions, and the control instructions are sent through a preset communication protocol.

[0037] Correspondingly, the present application also provides an intelligent device control system based on device data analysis, which is applied to a target intelligent device including multiple data acquisition devices, including:

[0038] A matching module is used to obtain basic information and control mode of the target intelligent device, the basic information includes device configuration information and device parameter information, and match the first evaluation parameter in each control mode through the device parameter information;

[0039] An evaluation module, configured to set a control cycle of a target smart device in each control mode in combination with the device configuration information and the first evaluation parameter, analyze a control process of the target smart device in a previous control cycle, and generate a second evaluation parameter;

[0040] An optimization module, used to adjust the control process in the next control cycle according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, and generate target parameters of the target intelligent device;

[0041] The control module is used to analyze the target parameters of the target intelligent device to form a control instruction for the next control cycle of the target intelligent device, and control the target intelligent device through the control instruction.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. Use device parameter information to match the first evaluation parameter in each control mode, confirm the corresponding first evaluation parameter in each control mode, and lay a stable foundation for subsequent device regulation. Combine device configuration information and the first evaluation parameter to set the control cycle of the target intelligent device in each control mode. Set different control cycles according to the characteristics of different control modes, and improve the adaptability of device regulation.

[0044] 2. Analyze the control process of the target intelligent device in the previous control cycle, regulate the control process in the next control cycle according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, evaluate the control situation of the previous control cycle, and adaptively adjust the configuration parameters in the optimization algorithm of the next control cycle, thereby improving the accuracy of device regulation, ensuring the efficient and stable operation of the intelligent device, and improving the regulation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the process of the intelligent device control method based on device data analysis proposed by the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of the intelligent device control system based on device data analysis proposed by the present invention. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be described clearly and completely 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, rather than all the embodiments.

[0048] Reference Figure 1The intelligent device control method based on device data analysis is applied to a target intelligent device including multiple data acquisition devices (such as sensors and other acquisition devices), and includes the following steps:

[0049] Step S101, obtaining basic information and control mode of a target smart device, wherein the basic information includes device configuration information and device parameter information, and matching a first evaluation parameter in each control mode through the device parameter information.

[0050] In this embodiment, the device configuration information includes the device model, manufacturer, hardware specifications (such as processor, memory, sensor type, etc.), software version, network interface, etc. This information is the basis for understanding the capabilities and limitations of the device. Device parameter information, which involves the operating parameters and evaluation parameters of the device, such as power consumption, operating temperature range, sensor sensitivity, maximum / minimum operating values, etc.

[0051] In some embodiments of the present application, the first evaluation parameter in each control mode is matched by device parameter information, including:

[0052] Filter out all categories of first evaluation parameters in the device parameter information, filter out the first evaluation parameters representing each control mode according to the characteristics of the first evaluation parameters, perform correlation analysis on the representative first evaluation parameters, and then filter out the first evaluation parameters related to the representative first evaluation parameters and record them as other evaluation parameters, so as to match the first evaluation parameters under each control mode;

[0053] In each control mode, the evaluation weight of each first evaluation parameter is allocated according to the correlation between other evaluation parameters and the representative first evaluation parameter, and the evaluation weight is marked in the matching relationship of the control mode.

[0054] In this embodiment, the correlation analysis can be described by the Pearson correlation coefficient. The control modes include energy-saving mode, balanced mode, high-performance mode, etc. The evaluation parameters involved in different control modes are different for smart devices. The different control modes are explained as follows:

[0055] High Performance Mode: Emphasizes the maximum performance of the device and is suitable for scenarios that require fast response or high computing power.

[0056] Balanced mode: Finds a balance between performance and energy consumption, suitable for daily use.

[0057] Energy saving mode: Reduces device performance to reduce energy consumption, suitable for low load or battery power conditions.

[0058] For the three control modes, the first evaluation parameters matched may be as follows (only examples, not all):

[0059] For the high performance mode, the first evaluation parameter may include processing speed, response time, throughput, etc.

[0060] For the balanced mode, the first evaluation parameter may include energy efficiency ratio, average response time, etc.

[0061] For the energy-saving mode, the first evaluation parameter may include energy consumption, standby time, etc.

[0062] It can be understood that the first evaluation parameters (parameters that best reflect the mode conditions) that represent the energy-saving mode, the balanced mode, and the high-performance mode are energy consumption, energy efficiency ratio, and processing speed, respectively.

[0063] Step S102 , combining the device configuration information and the first evaluation parameter to set the control cycle of the target smart device in each control mode, analyzing the control process of the target smart device in a control cycle, and generating a second evaluation parameter.

[0064] In this embodiment, the control cycles required for different control modes are not the same. Here, the robustness of the device in each control mode is determined by combining the device configuration information and the first evaluation parameter, so as to set the control cycle.

[0065] In some embodiments of the present application, the control period of the target smart device in each control mode is set in combination with the device configuration information and the first evaluation parameter, including:

[0066] The equipment configuration information includes data acquisition device information, functional requirement information, environmental change information and energy efficiency ratio information, and the acquisition device robustness index, functional robustness index, environmental robustness index and energy efficiency ratio robustness index under each control mode are determined according to the data acquisition device information, functional requirement information, environmental change information and energy efficiency ratio information respectively;

[0067] Determine the robustness level of the target intelligent device in each control mode based on the acquisition device robustness index, the functional robustness index, the environmental robustness index, the energy efficiency ratio robustness index and the first evaluation parameter;

[0068]

[0069] in, is the robustness level of the target intelligent device under the i1th control mode, 4 represents the robustness index of the acquisition device, the robustness index of the function, the robustness index of the environment, and the robustness index of the energy efficiency ratio. is the weight of the i2th robust indicator under the i1th control mode of the target intelligent device, is the i2th robust indicator of the target intelligent device under the i1th control mode, is the number of the first evaluation parameters in the i1th control mode of the target intelligent device, is the evaluation weight of the i3th first evaluation parameter under the i1th control mode of the target intelligent device, is the i3th first evaluation parameter of the target intelligent device under the i1th control mode, k1 and k2 are preset constants, and [] is a rounding symbol;

[0070] Different robustness levels correspond to different control cycle sizes, thereby setting the control cycle of the target intelligent device in each control mode.

[0071] In this embodiment, the specific device configuration information is classified, and the sensors and processors are recorded as data acquisition device information. High-performance hardware (such as processors and memory) can usually process data and execute instructions more quickly, which helps the system to respond quickly to external disturbances, thereby enhancing the robustness of the system. Functional requirement information is the functional status of the intelligent device, and the robustness of different functional requirements is different. Environmental change information is that the environment in which the intelligent device is located may change frequently (such as temperature, humidity, light, etc.), reflecting the robustness of the device to adapt to environmental changes. Energy efficiency ratio information is a balance between performance requirements and energy consumption constraints. Energy efficiency ratio is a parameter that directly reflects the balance between performance and energy consumption. It is usually defined as the ratio of system output performance (such as processing speed, throughput, etc.) to input energy consumption. The ratio of the two often reflects the overall robustness of the device. A system with strong robustness can usually reduce energy consumption while ensuring performance. According to the above aspects, the robustness indicators in different directions under each control mode are determined. The number and situation of the first evaluation parameters involved can also reflect the robustness. The more parameters there are, the greater the impact, which means that the equipment is more complex and the robustness is also different.

[0072] In this embodiment, It represents the correction of the first evaluation parameter to the sum of the robustness index of the acquisition device, the functional robustness index, the environmental robustness index, and the energy efficiency ratio robustness index, thereby improving the accuracy of the robustness evaluation.

[0073] In some embodiments of the present application, the control process of the target smart device in the previous control cycle is analyzed to generate the second evaluation parameter, including:

[0074] Generate an expected target curve of the target parameter corresponding to the previous control cycle according to the algorithm content of the control process in the previous control cycle of the target intelligent device, mark the realization point or realization segment on the expected target curve, and the point value or range value of the target parameter of the realization point or realization segment, so as to divide the expected target curve into two parts, a first curve and a second curve;

[0075] An actual target curve of a target parameter in a control cycle of a target intelligent device is obtained, and the actual target curve is divided into two parts, an unrealized target curve and an realized target curve, by a realization point or a realization segment. The unrealized target curve and the realized target curve are continuous in time.

[0076] The first curve is compared with the unrealized target curve, and the second curve is compared with the realized target curve to generate a second evaluation parameter.

[0077] In this embodiment, the algorithm content of the control process in a control cycle on the target smart device is the algorithm of the entire data analysis process, which not only includes the optimization algorithm. The target parameter can be a control parameter of the smart device or an evaluation parameter, and the target parameter category is different in different cases. Achieving the point value or range value of the target parameter is a process, which is divided into two parts before and after the realization for analysis.

[0078] In some embodiments of the present application, the first curve is compared with the unrealized target curve, and the second curve is compared with the realized target curve to generate a second evaluation parameter, including:

[0079] comparing the length, height and slope between the first curve and the unrealized target curve to form a control effect of the unrealized target curve;

[0080] Compare the height, slope and frequency of abnormal points between the second curve and the achieved target curve to form the control effect of the achieved target curve;

[0081] The control effect of the unrealized target curve and the control effect of the realized target curve are combined to obtain a control effect index, and the control effect index is used as the second evaluation parameter.

[0082] In this embodiment, the length, height and slope between the first curve and the unrealized target curve are compared, and the height, slope and frequency of abnormal points between the second curve and the realized target curve are compared. The comparison length is the difference in the time required to achieve the point value or range value of the target parameter, the height is the deviation between the actual value and the predicted value of the target parameter, and the slope is the stability of the process of not reaching the point value or range value of the target parameter or the stability of the point value or range value process of the target parameter after realization. These differences are quantified to obtain the control effect of the unrealized target curve and the control effect of the realized target curve, and finally integrated to obtain the control effect index.

[0083] Step S103 , regulating the control process in the next control cycle according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, and generating target parameters of the target intelligent device.

[0084] In this embodiment, the completion status of the control process in the previous control cycle is analyzed based on the first evaluation parameter (device own parameter) and the second evaluation parameter (parameter implementation difference).

[0085] In some embodiments of the present application, the control process in the next control cycle is regulated according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle to generate the target parameter of the target smart device, including:

[0086] defining a third evaluation index according to the first evaluation parameter and the second evaluation parameter;

[0087]

[0088] Among them, D is the third evaluation index, τ is the conversion coefficient, is the number of the first evaluation parameters in the i1th control mode of the target intelligent device, is the evaluation weight of the i3th first evaluation parameter under the i1th control mode of the target intelligent device, is the i3th first evaluation parameter of the target intelligent device under the i1th control mode, σ is the control effect index, and k3 is a preset constant;

[0089] Setting a third evaluation index threshold and a first evaluation parameter threshold according to the algorithm content of the control process in the previous control cycle of the target intelligent device;

[0090] If the third evaluation indicator is not greater than the third evaluation indicator threshold, the first evaluation parameter is compared with the first evaluation parameter threshold to screen out the first evaluation parameter that does not meet the requirements, and the configuration parameters in the optimization algorithm are adjusted according to the first evaluation parameters that do not meet the requirements. The optimization algorithm after adjusting the configuration parameters is used to optimize the control parameters of the target intelligent device, and the optimization results are used as the target parameters of the target intelligent device.

[0091] In this embodiment, It represents the correction of the control effect index to the sum of the first evaluation parameters, and then the class average value is taken as the third evaluation index. The class average value should be slightly larger than the average value.

[0092] In this embodiment, if the third evaluation index is not greater than the third evaluation index threshold, it means that the control process in the previous control cycle of the target intelligent device does not meet the requirements, and the configuration parameters (crossover rate, mutation rate, number of iterations, inertia weight, learning factor, maximum speed, etc.) of the optimization algorithm (genetic algorithm and particle swarm algorithm) need to be adjusted. The configuration parameters in the optimization algorithm are adjusted by the first evaluation parameter that does not meet the requirements. Different first evaluation parameter differences correspond to different correction factors, which are adjusted by correction factor * configuration parameters. Different first evaluation parameters correspond to different configuration parameters. For example, delayed response and increased energy consumption (first evaluation parameter) indicate that the optimization algorithm converges too slowly, and the crossover rate or inertia weight may need to be reduced.

[0093] Step S104: Analyze the target parameters of the target smart device to form a control instruction for the next control cycle of the target smart device, and control the target smart device through the control instruction.

[0094] In some embodiments of the present application, the method further includes:

[0095] If the third evaluation index is greater than the third evaluation index threshold, the configuration parameters in the optimization algorithm of the control process in the previous control cycle on the target smart device are not adjusted, and the optimization is performed, and the optimization result is used as the target parameter of the target smart device.

[0096] In some embodiments of the present application, the target parameters of the target smart device are analyzed to form a control instruction for the next control cycle of the target smart device, including:

[0097] The target parameters of the target intelligent device are formed into control instructions, and the control instructions are sent through a preset communication protocol.

[0098] In this embodiment, specific control instructions are formed based on the target parameters. These instructions may include starting / stopping devices, adjusting operating parameters, triggering specific actions, etc. The control instructions are sent to the target smart device through an appropriate communication protocol (such as Wi-Fi, Bluetooth, Zigbee, etc.), and their execution is monitored.

[0099] Compared with the prior art, the present invention has the following beneficial effects:

[0100] 1. Use device parameter information to match the first evaluation parameter in each control mode, confirm the corresponding first evaluation parameter in each control mode, and lay a stable foundation for subsequent device regulation. Combine device configuration information and the first evaluation parameter to set the control cycle of the target intelligent device in each control mode. Set different control cycles according to the characteristics of different control modes, and improve the adaptability of device regulation.

[0101] 2. Analyze the control process of the target intelligent device in the previous control cycle, regulate the control process in the next control cycle according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, evaluate the control situation of the previous control cycle, and adaptively adjust the configuration parameters in the optimization algorithm of the next control cycle, thereby improving the accuracy of device regulation, ensuring the efficient and stable operation of the intelligent device, and improving the regulation effect.

[0102] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0103] Correspondingly, the present application also provides an intelligent device control system based on device data analysis, which is applied to a target intelligent device including multiple data acquisition devices, such as Figure 2 As shown, including,

[0104] A matching module is used to obtain basic information and control mode of the target intelligent device, the basic information includes device configuration information and device parameter information, and match the first evaluation parameter in each control mode through the device parameter information;

[0105] An evaluation module, configured to set a control cycle of a target smart device in each control mode in combination with the device configuration information and the first evaluation parameter, analyze a control process of the target smart device in a previous control cycle, and generate a second evaluation parameter;

[0106] An optimization module, used to adjust the control process in the next control cycle according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, and generate target parameters of the target intelligent device;

[0107] The control module is used to analyze the target parameters of the target intelligent device to form a control instruction for the next control cycle of the target intelligent device, and control the target intelligent device through the control instruction.

[0108] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.

[0109] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0110] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent device control method based on device data analysis is applied to a target intelligent device including multiple data acquisition devices, characterized in that: include, Obtaining basic information and control mode of the target smart device, the basic information including device configuration information and device parameter information, and matching the first evaluation parameter in each control mode through the device parameter information; Combining the device configuration information and the first evaluation parameter to set a control cycle of the target smart device in each control mode, analyzing a control process of the target smart device in a control cycle, and generating a second evaluation parameter; According to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, the control process in the next control cycle is regulated to generate the target parameter of the target intelligent device; The target parameters of the target intelligent device are analyzed to form a control instruction for the next control cycle of the target intelligent device, and the target intelligent device is controlled through the control instruction.

2. The method for controlling intelligent devices based on device data analysis according to claim 1, characterized in that: The first evaluation parameter in each control mode is matched by the device parameter information, including: Filter out all categories of first evaluation parameters in the device parameter information, filter out the first evaluation parameters representing each control mode according to the characteristics of the first evaluation parameters, perform correlation analysis on the representative first evaluation parameters, and then filter out the first evaluation parameters related to the representative first evaluation parameters and record them as other evaluation parameters, so as to match the first evaluation parameters under each control mode; In each control mode, the evaluation weight of each first evaluation parameter is allocated according to the correlation between other evaluation parameters and the representative first evaluation parameter, and the evaluation weight is marked in the matching relationship of the control mode.

3. The intelligent device control method based on device data analysis according to claim 2 is characterized in that: The control cycle of the target intelligent device in each control mode is set by combining the device configuration information and the first evaluation parameter. include, The equipment configuration information includes data acquisition device information, functional requirement information, environmental change information and energy efficiency ratio information, and the acquisition device robustness index, functional robustness index, environmental robustness index and energy efficiency ratio robustness index under each control mode are determined according to the data acquisition device information, functional requirement information, environmental change information and energy efficiency ratio information respectively; Determine the robustness level of the target intelligent device in each control mode based on the acquisition device robustness index, the functional robustness index, the environmental robustness index, the energy efficiency ratio robustness index and the first evaluation parameter; in, is the robustness level of the target intelligent device under the i1th control mode, 4 represents the robustness index of the acquisition device, the robustness index of the function, the robustness index of the environment, and the robustness index of the energy efficiency ratio. is the weight of the i2th robust indicator under the i1th control mode of the target intelligent device, is the i2th robust indicator of the target intelligent device under the i1th control mode, is the number of the first evaluation parameters in the i1th control mode of the target intelligent device, is the evaluation weight of the i3th first evaluation parameter under the i1th control mode of the target intelligent device, is the i3th first evaluation parameter of the target intelligent device under the i1th control mode, k1 and k2 are preset constants, and [] is a rounding symbol; Different robustness levels correspond to different control cycle sizes, thereby setting the control cycle of the target intelligent device in each control mode.

4. The method for controlling intelligent devices based on device data analysis according to claim 1, characterized in that: Analyze the control process of the target intelligent device in the previous control cycle and generate the second evaluation parameter, including: Generate an expected target curve of the target parameter corresponding to the previous control cycle according to the algorithm content of the control process in the previous control cycle of the target intelligent device, mark the realization point or realization segment on the expected target curve, and the point value or range value of the target parameter of the realization point or realization segment, so as to divide the expected target curve into two parts, a first curve and a second curve; An actual target curve of a target parameter in a control cycle of a target intelligent device is obtained, and the actual target curve is divided into two parts, an unrealized target curve and an realized target curve, by a realization point or a realization segment. The unrealized target curve and the realized target curve are continuous in time. The first curve is compared with the unrealized target curve, and the second curve is compared with the realized target curve to generate a second evaluation parameter.

5. The method for controlling intelligent devices based on device data analysis according to claim 4, characterized in that: The first curve is compared with the unrealized target curve, and the second curve is compared with the realized target curve to generate a second evaluation parameter, including: comparing the length, height and slope between the first curve and the unrealized target curve to form a control effect of the unrealized target curve; Compare the height, slope and frequency of abnormal points between the second curve and the achieved target curve to form the control effect of the achieved target curve; The control effect of the unrealized target curve and the control effect of the realized target curve are combined to obtain a control effect index, and the control effect index is used as the second evaluation parameter.

6. The method for controlling intelligent devices based on device data analysis according to claim 5, characterized in that: According to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, the control process in the next control cycle is regulated to generate the target parameters of the target intelligent device, including: defining a third evaluation index according to the first evaluation parameter and the second evaluation parameter; Among them, D is the third evaluation index, τ is the conversion coefficient, is the number of the first evaluation parameters in the i1th control mode of the target intelligent device, is the evaluation weight of the i3th first evaluation parameter under the i1th control mode of the target intelligent device, is the i3th first evaluation parameter of the target intelligent device under the i1th control mode, σ is the control effect index, and k3 is a preset constant; Setting a third evaluation index threshold and a first evaluation parameter threshold according to the algorithm content of the control process in the previous control cycle of the target intelligent device; If the third evaluation indicator is not greater than the third evaluation indicator threshold, the first evaluation parameter is compared with the first evaluation parameter threshold to screen out the first evaluation parameter that does not meet the requirements, and the configuration parameters in the optimization algorithm are adjusted according to the first evaluation parameters that do not meet the requirements. The optimization algorithm after adjusting the configuration parameters is used to optimize the control parameters of the target intelligent device, and the optimization results are used as the target parameters of the target intelligent device.

7. The method for controlling intelligent devices based on device data analysis according to claim 6, characterized in that: The method further comprises, If the third evaluation index is greater than the third evaluation index threshold, the configuration parameters in the optimization algorithm of the control process in the previous control cycle on the target smart device are not adjusted, and the optimization is performed, and the optimization result is used as the target parameter of the target smart device.

8. The method for controlling intelligent devices based on device data analysis according to claim 1, characterized in that: Analyze the target parameters of the target intelligent device to form the control instructions for the next control cycle of the target intelligent device, including: The target parameters of the target intelligent device are formed into control instructions, and the control instructions are sent through a preset communication protocol.

9. An intelligent device control system based on device data analysis, applied to a target intelligent device including multiple data acquisition devices, characterized in that: include, A matching module is used to obtain basic information and control mode of the target intelligent device, the basic information includes device configuration information and device parameter information, and match the first evaluation parameter in each control mode through the device parameter information; An evaluation module, configured to set a control cycle of a target smart device in each control mode in combination with the device configuration information and the first evaluation parameter, analyze a control process of the target smart device in a previous control cycle, and generate a second evaluation parameter; An optimization module, used to adjust the control process in the next control cycle according to the first evaluation parameter and the second evaluation parameter of the control process in the previous control cycle, and generate target parameters of the target intelligent device; The control module is used to analyze the target parameters of the target intelligent device to form a control instruction for the next control cycle of the target intelligent device, and control the target intelligent device through the control instruction.

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