Intelligent case regulation and control method based on Internet of Things and automatic temperature control case system

Abstract

The invention relates to the technical field of case intelligent regulation and control, in particular to a case intelligent regulation and control method based on the Internet of Things and an automatic temperature control case system.The case intelligent regulation and control method comprises the steps that pre-processed case real-time operation temperature data are compared with a preset temperature threshold value, whether the real-time operation temperature of a case is in a normal range or not is scientifically analyzed; when the case is not in the normal range, acquiring real-time operation abnormal temperature data of the case, searching corresponding real-time operation abnormal position data of the case, and constructing real-time abnormal temperature characteristic data of the case based on the real-time operation abnormal temperature data of the case and the real-time operation abnormal position data of the case; and accurately searching case real-time temperature regulation and control scheme data matched with the case real-time abnormal temperature characteristic data through an intelligent optimization algorithm, finally constructing case temperature regulation and control data, and pushing the case temperature regulation and control data to a case temperature regulation and control platform to realize accurate monitoring and intelligent regulation of the case operation temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of computer cases, and specifically to an intelligent control method for computer cases based on the Internet of Things and an automatic temperature control computer case system. Background Art

[0002] With the continuous development of Internet technology, various electronic devices are widely used in many industries. As an important component of many electronic devices, the stability of the operating temperature of a computer case has an important impact on aspects such as the working efficiency, service life, and safety of the device. However, existing technologies often cannot dynamically adjust solutions according to the real-time operating temperature of the computer case.

[0003] The Chinese invention patent with the publication number CN108897221B introduces a method for controlling the fan speed of a military power supply computer case. By combining traditional PID control theory, neural network technology, fuzzy control technology, and predictive control technology to construct a system model, comprehensive judgment is made on multiple groups of collected state variables through the system model, the temperature of the computer case is predicted, and the actual adjustment voltage value of the fan is calculated. Based on the actual adjustment voltage value, the speed of the fan is controlled in real time to provide a more accurate and reliable temperature adjustment method and achieve stable power supply for the military power supply computer case. However, it can only adjust the temperature of the computer case by controlling the fan speed and cannot select dynamic solutions according to the real-time operating temperature of the computer case. Summary of the Invention

[0004] To solve the deficiencies in the background art, the present invention provides an intelligent control method for computer cases based on the Internet of Things and an automatic temperature control computer case system, realizing precise monitoring and intelligent adjustment of the operating temperature of the computer case.

[0005] An intelligent control method for computer cases based on the Internet of Things includes the following steps: S1. Collect real-time operating temperature data of the computer case; S2. Perform data preprocessing on the real-time operating temperature data of the computer case to generate real-time operating temperature characteristic data of the computer case; S3. Set a temperature threshold, perform a numerical comparison process on the real-time operating temperature characteristic data of the computer case and the temperature threshold, generate real-time operating temperature analysis data of the computer case according to the numerical comparison result. If the real-time operating temperature analysis data of the computer case is normal, end the current computer case temperature control operation; otherwise, generate real-time operating abnormal temperature data of the computer case; S4. Perform an abnormal temperature position data search process on the real-time operating abnormal temperature data of the computer case and the computer case temperature monitoring position data to generate real-time operating abnormal position data of the computer case; S5. Construct real-time abnormal temperature characteristic data of the computer case based on the real-time operating abnormal temperature data of the computer case and the real-time operating abnormal position data of the computer case; S6. Establish the data of the chassis temperature regulation plan, perform real-time temperature regulation plan matching processing on the real-time abnormal temperature characteristic data of the chassis and the data of the chassis temperature regulation plan, and generate the real-time temperature regulation plan data of the chassis; S7. Based on the real-time abnormal temperature characteristic data of the chassis and the real-time temperature regulation plan data of the chassis, construct the chassis temperature regulation data and push it to the chassis temperature regulation platform.

[0006] In the present invention, by numerically comparing the pre-processed real-time operating temperature data of the chassis with the preset temperature threshold, scientifically analyze whether the real-time operating temperature of the chassis is within the normal range. When it is not within the normal range, obtain the real-time operating abnormal temperature data of the chassis, search out the corresponding real-time operating abnormal position data of the chassis, construct the real-time abnormal temperature characteristic data of the chassis based on the real-time operating abnormal temperature data of the chassis and the real-time operating abnormal position data of the chassis, accurately search out the real-time temperature regulation plan data of the chassis that matches the real-time abnormal temperature characteristic data of the chassis through an intelligent optimization algorithm, and finally construct the chassis temperature regulation data and push it to the chassis temperature regulation platform to achieve accurate monitoring and intelligent adjustment of the chassis operating temperature. Preferably, the specific steps for collecting the real-time operating temperature data of the chassis are as follows: S11. Establish a chassis temperature monitoring position data set A = {a 1 , a 2 , …, a i , …, a k}, where a i represents the position data corresponding to the i-th chassis temperature monitoring area, k represents the total number of chassis temperature monitoring areas, and the chassis temperature monitoring areas include but are not limited to the CPU, motherboard chipset, graphics card, and hard disk; S12. Install temperature sensors at the positions corresponding to each chassis temperature monitoring position data in the chassis temperature monitoring position data set, and the temperature sensor represents one of a thermistor temperature sensor, a resistance temperature detector, and an IC temperature sensor; S13. Set the chassis temperature monitoring period, evenly divide the chassis temperature monitoring period into several chassis temperature monitoring time points, and obtain a chassis temperature monitoring time point set B = {b 1 , b 2 , …, b i , …, b l}, where b i represents the i-th chassis temperature monitoring time point obtained by evenly dividing the chassis temperature monitoring period, and l represents the total number of chassis temperature monitoring time points; S14. At each chassis temperature monitoring time point in the set of chassis temperature monitoring time points, use each temperature sensor to collect in real time the operating temperature data corresponding to each chassis temperature monitoring position data in the chassis temperature monitoring position data set, and obtain the real-time operating temperature data matrix C of the chassis as follows: , where c ij represents the operating temperature data corresponding to the j-th chassis temperature monitoring area collected in real time by the temperature sensor at the i-th chassis temperature monitoring time point.

[0007] Preferably, the specific steps for preprocessing the real-time operating temperature data of the chassis to generate real-time operating temperature characteristic data of the chassis are as follows: S21. Use the interquartile range method to perform abnormal data cleaning on the real-time operating temperature data in the real-time operating temperature data matrix of the chassis, and generate the real-time operating temperature true data matrix as follows: , where c ij represents the real-time operating temperature true data obtained after abnormal data cleaning of the operating temperature data corresponding to the i-th chassis temperature monitoring area collected in real time by the temperature sensor at the j-th chassis temperature monitoring time point; the abnormal data cleaning process is as follows: S211. Arrange the real-time operating temperature data of the i-th row in the real-time operating temperature data matrix of the chassis from smallest to largest to obtain a data sequence, and calculate the lower quartile , median and upper quartile of the data sequence respectively; S212. If , then determine that c ij is the real-time operating temperature true data of the chassis, and retain c ij ; otherwise, determine that c ij is an abnormal point of the real-time operating temperature data of the chassis, and use the median to replace c ij ; S213. Repeat the operations in S211 to S212 until all the real-time operating temperature data in the real-time operating temperature data matrix of the chassis are traversed, and generate the real-time operating temperature true data matrix; S22. Use the mean formula to perform average value measurement on the real-time operating temperature true data in the real-time operating temperature true data matrix of the chassis, and generate the real-time operating temperature characteristic data set , where denotes the real-time operating temperature characteristic data of the chassis corresponding to the i-th chassis temperature monitoring area, and .

[0008] Accurately identify the abnormal temperature data in the real-time operating temperature data matrix of the chassis through the interquartile range method, and select appropriate values for replacement, effectively avoiding the influence caused by errors in the data acquisition process, ensuring the reliability of the acquired data. Use the mean formula to perform average value measurement processing on the real-time operating temperature real data in the real-time operating temperature real data matrix of the chassis, and integrate multiple groups of real data into one value, providing a data basis for subsequent analysis of the chassis temperature status.

[0009] Preferably, set a temperature threshold, perform numerical comparison processing on the real-time operating temperature characteristic data of the chassis and the temperature threshold, and generate real-time operating temperature analysis data of the chassis according to the numerical comparison result. If the real-time operating temperature analysis data of the chassis is normal, end the current chassis temperature control operation; otherwise, the specific steps for generating the real-time operating abnormal temperature data of the chassis are as follows: S31. Set a temperature threshold, and sequentially perform numerical comparison processing on each real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis and the temperature threshold, and generate real-time operating temperature analysis data D fenxi ; If all the real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis are less than the temperature threshold, output that the real-time operating temperature analysis data D fenxi is normal, and push the real-time operating temperature analysis data D fenxi to the chassis temperature control platform through the communication network, and end the current chassis temperature control operation; If any real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis is greater than the temperature threshold, output that the real-time operating temperature analysis data D fenxi is abnormal, screen out all the real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis that are greater than the temperature threshold and perform data identification to generate a real-time operating abnormal temperature data set , where denotes the i-th real-time operating abnormal temperature data of the chassis, and p denotes the total number of real-time operating abnormal temperature data of the chassis.

[0010] Perform numerical comparison between the preset temperature threshold and the real-time operating temperature characteristic data of the chassis, and analyze the temperature status of the chassis in real time, making a preliminary determination for the intelligent control of the chassis temperature. When the temperature status of the chassis is not within the normal range, scientifically screen out the real-time operating abnormal temperature data of the chassis.

[0011] Preferably, the specific steps for performing abnormal temperature position data search processing on the real-time operation abnormal temperature data of the chassis and the chassis temperature monitoring position data to generate the real-time operation abnormal position data of the chassis are as follows: S41. Traverse all the chassis temperature monitoring position data in the chassis temperature monitoring position data set through the iterative deepening search algorithm, search for the chassis temperature monitoring position data that matches each real-time operation abnormal temperature data in the real-time operation abnormal temperature data set of the chassis, and perform data identification to generate the real-time operation abnormal position data set E = {e 1 , e 2 , …, e i , …, e p}, where represents the position data corresponding to the i-th real-time operation abnormal temperature data of the chassis.

[0012] Accurately search for the chassis temperature monitoring position data that matches the real-time operation abnormal temperature data of the chassis through the iterative deepening search algorithm, generate the real-time operation abnormal position data of the chassis, and accurately identify the chassis position where the temperature anomaly occurs and the corresponding temperature data.

[0013] Preferably, the specific steps for constructing the real-time abnormal temperature feature data of the chassis based on the real-time operation abnormal temperature data of the chassis and the real-time operation abnormal position data of the chassis are as follows: S51. Combine each real-time operation abnormal temperature data in the real-time operation abnormal temperature data set of the chassis with the corresponding real-time operation abnormal position data in the real-time operation abnormal position data set of the chassis to generate the real-time abnormal temperature feature data set , where represents the i-th real-time abnormal temperature feature data of the chassis, and .

[0014] Preferably, establish the chassis temperature regulation scheme data, and perform real-time temperature regulation scheme matching processing on the real-time abnormal temperature feature data of the chassis and the chassis temperature regulation scheme data to generate the real-time temperature regulation scheme data of the chassis. The specific steps are as follows: S61. Establish the chassis temperature regulation scheme data set F = {f 1 , f 2 , …, f i , …, f o}, where f i represents the i-th chassis temperature regulation scheme data, o represents the total number of the chassis temperature regulation scheme data, the chassis temperature regulation scheme data represents the fan selection for performing the chassis temperature regulation operation and the corresponding rotation speeds of each fan, and the fans include the intake fan and the exhaust fan; S62. Perform real-time temperature control scheme matching processing on each chassis temperature control scheme data in the real-time abnormal temperature feature dataset of the chassis and the chassis temperature control scheme dataset by using the beluga optimization algorithm, search for the chassis temperature control scheme data that matches the real-time abnormal temperature feature dataset of the chassis, and perform data identification to generate the real-time temperature control scheme data F of the chassis fangan ; S621. Construct a beluga population for searching control schemes, set the population size as N, the current iteration number as t, the maximum iteration number as t max and the search space dimension of the chassis temperature control scheme data as P; Use the chassis temperature control scheme dataset as the search space for the chassis temperature control scheme data, randomly generate N pieces of chassis temperature control scheme data in the search space for the chassis temperature control scheme data, and each piece of chassis temperature control scheme data corresponds to a control scheme search beluga individual in the beluga population for searching control schemes; S622. Calculate the fitness value of each control scheme search beluga individual in the beluga population for searching control schemes, arrange each control scheme search beluga individual in the beluga population for searching control schemes in descending order according to the fitness value, and select the control scheme search beluga individual with the highest fitness value as the current optimal individual; the fitness value calculation formula is as follows: , where, Fit i represents the fitness value corresponding to the i-th control scheme search beluga individual in the beluga population for searching control schemes, x i represents the control cost of the chassis temperature control scheme data corresponding to the i-th control scheme search beluga individual in the beluga population for searching control schemes, v i represents the average temperature reduction speed of the chassis temperature control scheme data corresponding to the i-th control scheme search beluga individual in the beluga population for searching control schemes; S623. Each control scheme search beluga individual in the beluga population for searching control schemes controls its swimming behavior in the search space for the chassis temperature control scheme data through the balance factor ; the balance factor update formula is as follows: , where, represents the balance factor in the current iteration process, and rand represents a random number uniformly distributed between (0, 1); If the balance factor If it is greater than 0.5, each search beluga whale individual in the regulation scheme search beluga whale population will adopt a mirror swimming strategy and update its position in the chassis temperature regulation scheme data search space by swimming in the same direction with a randomly selected search beluga whale individual in the regulation scheme. The position update formula is as follows: , where, represents the position of the i-th search beluga whale individual in the regulation scheme search beluga whale population after position update in the j-th dimension of the chassis temperature regulation scheme data search space, and represent the current positions of the z-th and i-th search beluga whale individuals in the regulation scheme search beluga whale population in the j-th dimension of the chassis temperature regulation scheme data search space respectively. r 1 and r 2 both represent random numbers uniformly distributed between (0, 1); If the balance factor is less than or equal to 0.5, each search beluga whale individual in the regulation scheme search beluga whale population will share the prey position information and update its position in the chassis temperature regulation scheme data search space by cooperating to hunt the optimal prey. The position update formula is as follows: , where, represents the position of the i-th search beluga whale individual in the regulation scheme search beluga whale population after position update. r 3 and r 4 both represent random numbers uniformly distributed between (0, 1). X best represents the position of the current optimal individual, and represent the current positions of the z-th and i-th search beluga whale individuals in the regulation scheme search beluga whale population respectively. ε represents the Levy flight function, γ represents the random jump intensity of the Levy flight, and ; S624. Update the whale fall probability ω of each search beluga whale individual in the regulation scheme search beluga whale population. The update formula is as follows: , S625. Each search beluga whale individual in the regulation scheme search beluga whale population performs a whale fall behavior to update its position in the chassis temperature regulation scheme data search space according to the whale fall probability ω. The position update formula is as follows: , where, r 5 , r 6 and r 7All represent random numbers that follow a uniform distribution between (0, 1), X step represents the whale fall step length; S626. Calculate the fitness value of each beluga whale individual for regulating scheme search in the beluga whale population after position update. If the fitness value of a beluga whale individual for regulating scheme search after position update is greater than the fitness value of the original position, replace the original position with the new position; otherwise, retain the original position; Re - arrange all the beluga whale individuals for regulating scheme search in the beluga whale population according to the fitness value from large to small, and select the beluga whale individual with the highest fitness value as the new current optimal individual; S627. Determine whether the current iteration number t is less than the maximum iteration number t max . If the current iteration number t is less than the maximum iteration number t max , then increment the current iteration number t by 1 and return to S623; otherwise, take the current optimal individual as the global optimal solution, output the chassis temperature regulation scheme data corresponding to the global optimal solution and perform data identification to generate the real - time chassis temperature regulation scheme data F fangan .

[0015] Through the beluga whale optimization algorithm, perform real - time temperature regulation scheme matching processing on the real - time abnormal temperature characteristic data of the chassis and the chassis temperature regulation scheme data, quickly and accurately search for a temperature regulation scheme that conforms to the current chassis temperature state. At the same time, the beluga whale optimization algorithm has good robustness, which can ensure the stability of the matching process.

[0016] Preferably, the specific steps for constructing the chassis temperature regulation data based on the real - time abnormal temperature characteristic data of the chassis and the real - time chassis temperature regulation scheme data and pushing it to the chassis temperature regulation platform are as follows: S71. Combine the real - time abnormal temperature characteristic data set of the chassis and the real - time chassis temperature regulation scheme data F fangan to construct the chassis temperature regulation data ; S72. Push the chassis temperature regulation data to the chassis temperature regulation platform through the communication network and execute the chassis temperature regulation operation.

[0017] The present invention also includes an automatic temperature - controlled chassis system based on the Internet of Things, including a chassis real - time operating temperature data acquisition module, a chassis real - time operating temperature data pre - processing module, a chassis real - time operating temperature analysis module, a chassis real - time operating abnormal position search module, a chassis real - time abnormal temperature characteristic data construction module, a chassis real - time temperature regulation scheme matching module, and a chassis temperature regulation data construction module; The real-time operating temperature data acquisition module of the chassis collects the operating temperature data corresponding to the data of each chassis temperature monitoring position in real time at each chassis temperature monitoring time point through a number of installed temperature sensors, and obtains the real-time operating temperature data of the chassis; The preprocessing module of the real-time operating temperature data of the chassis performs abnormal data cleaning processing on the real-time operating temperature data of the chassis through the interquartile range method, generates the real-time operating temperature true data of the chassis, and then performs average value measurement processing on the real-time operating temperature true data of the chassis by using the mean formula to generate the real-time operating temperature characteristic data of the chassis; The real-time operating temperature analysis module of the chassis sets a temperature threshold, compares the real-time operating temperature characteristic data of the chassis with the temperature threshold in sequence for numerical comparison processing, generates real-time operating temperature analysis data of the chassis according to the numerical comparison result, and if the real-time operating temperature analysis data of the chassis is normal, ends the current chassis temperature control operation; otherwise, generates real-time operating abnormal temperature data of the chassis; The real-time operating abnormal position search module of the chassis traverses the data of the chassis temperature monitoring position through the iterative deepening search algorithm, searches out the data of the chassis temperature monitoring position that matches the real-time operating abnormal temperature data of the chassis and performs data identification, and generates real-time operating abnormal position data of the chassis; The real-time abnormal temperature characteristic data construction module of the chassis combines the real-time operating abnormal temperature data and the real-time operating abnormal position data of the chassis to construct real-time abnormal temperature characteristic data of the chassis; The real-time temperature control scheme matching module of the chassis performs real-time temperature control scheme matching processing on the real-time abnormal temperature characteristic data of the chassis and the established chassis temperature control scheme data through the beluga optimization algorithm, searches out the chassis temperature control scheme data that matches the real-time abnormal temperature characteristic data of the chassis and performs data identification, and generates real-time temperature control scheme data of the chassis; The chassis temperature control data construction module combines the real-time abnormal temperature characteristic data set of the chassis and the real-time temperature control scheme data of the chassis to construct chassis temperature control data, pushes it to the chassis temperature control platform through the communication network, and executes the chassis temperature control operation.

[0018] 1. The present invention compares the real-time operating temperature data of the chassis after preprocessing with a preset temperature threshold value, scientifically analyzes whether the real-time operating temperature of the chassis is within the normal range. When it is not within the normal range, it obtains the real-time abnormal temperature data of the chassis and searches for the corresponding real-time abnormal position data of the chassis. Based on the real-time abnormal temperature data of the chassis and the real-time abnormal position data of the chassis, it constructs the real-time abnormal temperature characteristic data of the chassis, accurately searches for the real-time temperature control scheme data of the chassis that matches the real-time abnormal temperature characteristic data of the chassis through an intelligent optimization algorithm, and finally constructs the chassis temperature control data and pushes it to the chassis temperature control platform to achieve accurate monitoring and intelligent adjustment of the chassis operating temperature; 2. The quartile range method is used to accurately identify the abnormal temperature data in the real-time operating temperature data matrix of the chassis, and appropriate values are selected for replacement, effectively avoiding the influence caused by errors in the data collection process, ensuring the reliability of the obtained data. The mean formula is used to perform an average value measurement process on the real-time operating temperature real data in the real-time operating temperature real data matrix of the chassis, integrating multiple groups of real data into one value, providing a data basis for subsequent analysis of the chassis temperature state; 3. The preset temperature threshold value is compared with the real-time abnormal temperature characteristic data of the chassis to analyze the temperature state of the chassis in real time, making a preliminary determination for the intelligent control of the chassis temperature. When the chassis temperature state is not within the normal range, it scientifically screens out the real-time abnormal temperature data of the chassis, and accurately searches for the chassis temperature monitoring position data that matches the real-time abnormal temperature data of the chassis through an iterative deepening search algorithm, generating the real-time abnormal position data of the chassis, and accurately identifying the chassis position where the temperature anomaly occurs and the corresponding temperature data; 4. The beluga optimization algorithm is used to perform a real-time temperature control scheme matching process on the real-time abnormal temperature characteristic data of the chassis and the chassis temperature control scheme data, quickly and accurately searching for a temperature control scheme that conforms to the current chassis temperature state. At the same time, the beluga optimization algorithm has good robustness, ensuring the stability of the matching process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the invention. For those of ordinary skill in the art, without creative efforts, additional drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a flowchart of an intelligent control method for a chassis based on the Internet of Things provided by the present invention; Figure 2Schematic diagram of the modules of an automatic temperature control chassis system based on the Internet of Things provided by the present invention. Detailed implementation manners

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inner", etc. indicating orientations or positional relationships are only for the convenience of describing the invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation to the invention.

[0023] The first embodiment is as follows: Please refer to Figure 1 , an intelligent control method for a chassis based on the Internet of Things, including the following steps: S1. Collect real-time operating temperature data of the chassis; S11. Establish a chassis temperature monitoring position data set A = {a 1 , a 2 , …, a i , …, a k}, where a i represents the position data corresponding to the i-th chassis temperature monitoring area, k represents the total number of chassis temperature monitoring areas, and the chassis temperature monitoring areas include but are not limited to the CPU, motherboard chipset, graphics card, and hard disk; S12. Install temperature sensors at the positions corresponding to each chassis temperature monitoring position data in the chassis temperature monitoring position data set. The temperature sensor represents one of a thermistor temperature sensor, a resistance temperature detector, and an IC temperature sensor; S13. Set the chassis temperature monitoring period, and evenly divide the chassis temperature monitoring period into several chassis temperature monitoring time points to obtain a chassis temperature monitoring time point set B = {b 1 , b 2 , …, b i , …, b l}, where b i represents the i-th chassis temperature monitoring time point obtained by evenly dividing the chassis temperature monitoring period, and l represents the total number of chassis temperature monitoring time points; S14. At each chassis temperature monitoring time point in the set of chassis temperature monitoring time points, the operating temperature data corresponding to each chassis temperature monitoring position data in the chassis temperature monitoring position data set is collected in real time by each temperature sensor, and the following chassis real-time operating temperature data matrix C is obtained: , where c ij represents the operating temperature data corresponding to the j-th chassis temperature monitoring area collected in real time by the temperature sensor at the i-th chassis temperature monitoring time point.

[0024] S2. Perform data preprocessing on the chassis real-time operating temperature data to generate chassis real-time operating temperature feature data; S21. Perform abnormal data cleaning processing on the chassis real-time operating temperature data in the chassis real-time operating temperature data matrix by the interquartile range method to generate a chassis real-time operating temperature true data matrix as follows: , where c ij represents the chassis real-time operating temperature true data obtained after abnormal data cleaning processing on the operating temperature data corresponding to the i-th chassis temperature monitoring area collected in real time by the temperature sensor at the j-th chassis temperature monitoring time point; the abnormal data cleaning processing process is as follows: S211. Arrange the chassis real-time operating temperature data in the i-th row of the chassis real-time operating temperature data matrix in ascending order to obtain a data sequence, and calculate the lower quartile , median and upper quartile of the data sequence respectively; S212. If , then determine that c ij is the chassis real-time operating temperature true data and retain c ij ; otherwise, determine that c ij is the abnormal point of the chassis real-time operating temperature data, and replace c with the median ij ; S213. Repeat the operations in S211 to S212 until all the chassis real-time operating temperature data in the chassis real-time operating temperature data matrix are traversed, and then generate a chassis real-time operating temperature true data matrix; S22. Use the mean formula to perform average value measurement processing on the chassis real-time operating temperature true data in the chassis real-time operating temperature true data matrix to generate a chassis real-time operating temperature feature data set , where represents the real-time operating temperature characteristic data of the chassis corresponding to the i-th chassis temperature monitoring area, and .

[0025] S3. Set a temperature threshold, perform a numerical comparison process on the real-time operating temperature characteristic data of the chassis and the temperature threshold, generate real-time operating temperature analysis data of the chassis according to the numerical comparison result. If the real-time operating temperature analysis data of the chassis is normal, end this chassis temperature regulation operation; otherwise, generate real-time operating abnormal temperature data of the chassis; S31. Set a temperature threshold, sequentially perform a numerical comparison process on each real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis and the temperature threshold, and generate real-time operating temperature analysis data D of the chassis according to the numerical comparison result fenxi ; If all the real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis are less than the temperature threshold, output that the real-time operating temperature analysis data D fenxi is normal, and push the real-time operating temperature analysis data D of the chassis to the chassis temperature regulation platform through the communication network fenxi and end this chassis temperature regulation operation; If any real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis is greater than the temperature threshold, output that the real-time operating temperature analysis data D fenxi is abnormal, screen out all the real-time operating temperature characteristic data in the real-time operating temperature characteristic data set of the chassis that are greater than the temperature threshold and perform data identification to generate a real-time operating abnormal temperature data set , where represents the i-th real-time operating abnormal temperature data of the chassis, and p represents the total number of real-time operating abnormal temperature data of the chassis. S4. Perform an abnormal temperature position data search process on the real-time operating abnormal temperature data of the chassis and the chassis temperature monitoring position data to generate real-time operating abnormal position data; S41. Traverse all the chassis temperature monitoring position data in the chassis temperature monitoring position data set through the iterative deepening search algorithm, search for the chassis temperature monitoring position data that matches each real-time operating abnormal temperature data in the real-time operating abnormal temperature data set of the chassis and perform data identification to generate a real-time operating abnormal position data set E = {e 1 , e 2 , …, e i , …, e p}, where represents the position data corresponding to the i-th real-time operating abnormal temperature data of the chassis.

[0026] S5. Construct the real-time abnormal temperature feature data of the chassis based on the real-time abnormal temperature data and the real-time abnormal position data of the chassis during operation; S51. Combine each real-time abnormal temperature data of the chassis during operation in the real-time abnormal temperature data set of the chassis with the corresponding real-time abnormal position data of the chassis during operation in the real-time abnormal position data set of the chassis to generate a real-time abnormal temperature feature data set of the chassis , where represents the i-th real-time abnormal temperature feature data of the chassis, and .

[0027] S6. Establish the data of the chassis temperature control scheme, perform real-time temperature control scheme matching processing on the real-time abnormal temperature feature data of the chassis and the data of the chassis temperature control scheme to generate the real-time temperature control scheme data of the chassis; S61. Establish a chassis temperature control scheme data set F = {f 1 , f 2 , …, f i , …, f o}, where f i represents the i-th chassis temperature control scheme data, o represents the total number of the chassis temperature control scheme data, and the chassis temperature control scheme data represents the fan selection for performing the chassis temperature control operation and the corresponding rotation speeds of each fan. The fans include intake fans and exhaust fans; S62. Perform real-time temperature control scheme matching processing on the real-time abnormal temperature feature data set of the chassis and each chassis temperature control scheme data in the chassis temperature control scheme data set through the beluga optimization algorithm, search for the chassis temperature control scheme data that matches the real-time abnormal temperature feature data set of the chassis and perform data identification to generate the real-time temperature control scheme data F fangan ; S621. Construct a beluga population for searching the control scheme, set the population size as N, the current iteration number as t, the maximum iteration number as t max and the search space dimension of the chassis temperature control scheme data as P; Use the chassis temperature control scheme data set as the search space of the chassis temperature control scheme data, randomly generate N chassis temperature control scheme data in the search space of the chassis temperature control scheme data, and each chassis temperature control scheme data corresponds to a control scheme search beluga individual in the beluga population for searching the control scheme; S622. Calculate the fitness values of each beluga individual for the regulation scheme search in the beluga population, arrange each beluga individual for the regulation scheme search in the beluga population in descending order of fitness value, and select the beluga individual with the highest fitness value as the current optimal individual. The fitness value calculation formula is as follows: , where, Fit i represents the fitness value corresponding to the i-th beluga individual for the regulation scheme search in the beluga population for the regulation scheme search, x i represents the regulation cost of the chassis temperature regulation scheme data corresponding to the i-th beluga individual for the regulation scheme search in the beluga population, and v i represents the average cooling rate of the chassis temperature regulation scheme data corresponding to the i-th beluga individual for the regulation scheme search in the beluga population; S623. Each beluga individual for the regulation scheme search in the beluga population controls its swimming behavior in the search space of the chassis temperature regulation scheme data through a balance factor . The balance factor update formula is as follows: , where, represents the balance factor in the current iteration process, and rand represents a random number uniformly distributed between (0, 1); If the balance factor is greater than 0.5, then each beluga individual for the regulation scheme search in the beluga population will adopt a mirror swimming strategy and update its position in the search space of the chassis temperature regulation scheme data by swimming in the same direction with a randomly selected beluga individual for the regulation scheme search. The position update formula is as follows: , where, represents the position of the i-th beluga individual for the regulation scheme search in the beluga population after position update in the j-th dimensional search space of the chassis temperature regulation scheme data, and respectively represent the current positions of the z-th and i-th beluga individuals for the regulation scheme search in the beluga population in the j-th dimensional search space of the chassis temperature regulation scheme data, and r 1 and r 2 both represent random numbers uniformly distributed between (0, 1); If the balance factor If it is less than or equal to 0.5, then each individual in the regulation scheme search beluga whale population that searches for regulation schemes will share prey position information and hunt for the optimal prey through collaborative cooperation, and perform position updates in the data search space of the chassis temperature regulation scheme; the position update formula is as follows: , where, represents the position after position update of the i-th individual in the regulation scheme search beluga whale population that searches for regulation schemes, r 3 and r 4 both represent random numbers uniformly distributed between (0, 1), X best represents the position of the current optimal individual, and respectively represent the current positions of the z-th and i-th individuals in the regulation scheme search beluga whale population that searches for regulation schemes, ε represents the Lévy flight function, γ represents the random jump intensity of the Lévy flight, and ; S624. Update the whale fall probability ω of each individual in the regulation scheme search beluga whale population that searches for regulation schemes; the update formula is as follows: , S625. Each individual in the regulation scheme search beluga whale population that searches for regulation schemes performs a whale fall behavior in the data search space of the chassis temperature regulation scheme for position update according to the whale fall probability ω; the position update formula is as follows: , where, r 5 , r 6 and r 7 all represent random numbers uniformly distributed between (0, 1), X step represents the whale fall step size; S626. Calculate the fitness value of each individual in the regulation scheme search beluga whale population that searches for regulation schemes after position update. If the fitness value of an individual in the regulation scheme search beluga whale population after position update is greater than the fitness value of the original position, replace the original position with the new position; otherwise, retain the original position; Re-arrange each individual in the regulation scheme search beluga whale population that searches for regulation schemes from largest to smallest according to the fitness value, and select the individual with the highest fitness value as the new current optimal individual; S627. Determine whether the current iteration number t is less than the maximum iteration number t max , if the current iteration number t is less than the maximum iteration number t max, then increment the current iteration count t by 1 and return to S623; otherwise, take the current optimal individual as the global optimal solution, output the chassis temperature control scheme data corresponding to the global optimal solution and perform data identification to generate the real-time chassis temperature control scheme data F fangan .

[0028] S7. Construct chassis temperature control data based on the real-time abnormal temperature feature data of the chassis and the real-time chassis temperature control scheme data, and push it to the chassis temperature control platform; S71. Combine the real-time abnormal temperature feature data set of the chassis and the real-time chassis temperature control scheme data F fangan to construct chassis temperature control data ; S72. Push the chassis temperature control data to the chassis temperature control platform through the communication network and execute the chassis temperature control operation.

[0029] Embodiment 2 is as follows: Please refer to Figure 2 , an automatic temperature control chassis system based on the Internet of Things, including a real-time chassis operating temperature data acquisition module, a real-time chassis operating temperature data preprocessing module, a real-time chassis operating temperature analysis module, a real-time chassis operating abnormal position search module, a real-time abnormal temperature feature data construction module of the chassis, a real-time chassis temperature control scheme matching module, and a chassis temperature control data construction module; The real-time chassis operating temperature data acquisition module collects the operating temperature data corresponding to the data at each chassis temperature monitoring position at each chassis temperature monitoring time point through a plurality of installed temperature sensors to obtain the real-time chassis operating temperature data; The real-time chassis operating temperature data preprocessing module performs abnormal data cleaning processing on the real-time chassis operating temperature data through the interquartile range method to generate real-time chassis operating temperature real data, and then performs average value measurement processing on the real-time chassis operating temperature real data by using the mean formula to generate real-time chassis operating temperature feature data; The real-time chassis operating temperature analysis module sets a temperature threshold, sequentially compares the real-time chassis operating temperature feature data with the temperature threshold for numerical comparison processing, generates real-time chassis operating temperature analysis data according to the numerical comparison result, and if the real-time chassis operating temperature analysis data is normal, ends the current chassis temperature control operation; otherwise, generates real-time chassis operating abnormal temperature data; The real-time operation abnormal position search module of the chassis traverses the chassis temperature monitoring position data through an iterative deepening search algorithm, searches for the chassis temperature monitoring position data that matches the real-time operation abnormal temperature data of the chassis and performs data identification, and generates the real-time operation abnormal position data of the chassis; The real-time abnormal temperature feature data construction module of the chassis constructs real-time abnormal temperature feature data of the chassis by combining the real-time operation abnormal temperature data and the real-time operation abnormal position data of the chassis; The real-time temperature regulation scheme matching module of the chassis performs real-time temperature regulation scheme matching processing on the real-time abnormal temperature feature data of the chassis and the established chassis temperature regulation scheme data through a beluga optimization algorithm, searches for the chassis temperature regulation scheme data that matches the real-time abnormal temperature feature data of the chassis and performs data identification, and generates the real-time temperature regulation scheme data of the chassis; The chassis temperature regulation data construction module combines the real-time abnormal temperature feature data set of the chassis and the real-time temperature regulation scheme data of the chassis to construct chassis temperature regulation data, pushes it to the chassis temperature regulation platform through a communication network, and executes the chassis temperature regulation operation.

[0030] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0031] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not elaborate on all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principle and practical application of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. A chassis intelligent control method based on the Internet of Things, characterized in that: The steps include: S1, collect real-time operating temperature data of the chassis; S2, performing data preprocessing on the real-time operating temperature data of the chassis to generate real-time operating temperature characteristic data of the chassis; S3, setting a temperature threshold, performing numerical comparison processing on the real-time operating temperature characteristic data of the chassis and the temperature threshold, generating real-time operating temperature analysis data of the chassis according to the numerical comparison result, and ending the current chassis temperature control operation if the real-time operating temperature analysis data of the chassis is normal; otherwise, generating real-time operating abnormal temperature data of the chassis; S4, performing abnormal temperature position data search processing on the chassis real-time operation abnormal temperature data and the chassis temperature monitoring position data to generate chassis real-time operation abnormal position data; S5, constructing chassis real-time abnormal temperature characteristic data based on the chassis real-time abnormal operation temperature data and the chassis real-time abnormal operation position data; S6, establishing chassis temperature control solution data, performing real-time temperature control solution matching processing on the chassis real-time abnormal temperature characteristic data and the chassis temperature control solution data, and generating chassis real-time temperature control solution data; S7. Construct chassis temperature control data based on the chassis real-time abnormal temperature feature data and the chassis real-time temperature control solution data, and push the data to the chassis temperature control platform.

2. According to the method of intelligent chassis control based on the Internet of Things in claim 1, it is characterized in that: The S1 comprises the following steps: S11, establish the chassis temperature monitoring location data set A={a1,a2,…,a i ,…,a k }, where a i represents the position data corresponding to the i-th chassis temperature monitoring area, and k represents the total number of chassis temperature monitoring areas; S12, installing a temperature sensor at a position corresponding to each chassis temperature monitoring position data in A; S13, setting a chassis temperature monitoring cycle, and evenly dividing the chassis temperature monitoring cycle into a number of chassis temperature monitoring time points, to obtain a chassis temperature monitoring time point set B = {b1, b2, ..., b i ,…,b l }, where b i represents the i-th chassis temperature monitoring time point obtained by evenly dividing the chassis temperature monitoring period, and l represents the total number of chassis temperature monitoring time points; S14, using each temperature sensor to collect the operating temperature data corresponding to each chassis temperature monitoring position data in A at each chassis temperature monitoring time point in B in real time, and obtain the chassis real-time operating temperature data matrix C as follows: ， Among them, c ij It represents the operating temperature data corresponding to the j-th chassis temperature monitoring area collected in real time by the temperature sensor at the i-th chassis temperature monitoring time point.

3. The method for intelligent chassis control based on the Internet of Things according to claim 2 is characterized in that: The S2 comprises the following steps: S21, using the interquartile range method to perform abnormal data cleaning on the real-time operating temperature data of the chassis in C, and generate a real data matrix of the real-time operating temperature of the chassis as follows: ， in, It indicates the real data of the real operating temperature of the chassis obtained after the abnormal data cleaning process of the operating temperature data corresponding to the i-th chassis temperature monitoring area collected in real time by the temperature sensor at the j-th chassis temperature monitoring time point; the abnormal data cleaning process is as follows: S211, arranging the real-time operating temperature data of the chassis in the i-th row in C from small to large to obtain a data sequence, and calculating the lower quartiles of the data sequence respectively. , median and the upper quartile ; S212, if , then determine c ij The real-time operating temperature data of the chassis is ij Keep; otherwise, judge c ij The abnormal points of the real-time operating temperature data of the chassis are calculated using the median Replace c ij ; S213, repeat the operations from S211 to S212 until all the real-time operating temperature data of the chassis in C are traversed to generate the ; S22, using the mean formula to The real data of the real-time operating temperature of the chassis in the system is averaged and processed to generate a characteristic data set of the real-time operating temperature of the chassis ,in, represents the real-time operating temperature characteristic data of the chassis corresponding to the i-th chassis temperature monitoring area, and .

4. The method for intelligent chassis control based on the Internet of Things according to claim 3 is characterized in that: The S3 comprises the following steps: S31, set the temperature threshold, The real-time operating temperature characteristic data of each chassis in the system are numerically compared with the temperature threshold in turn, and the real-time operating temperature analysis data D of the chassis is generated according to the numerical comparison result. fenxi ; If the If the real-time operating temperature characteristic data of all chassis in the system are less than the temperature threshold, the D fenxi If it is normal, the D fenxi Push to the chassis temperature control platform and end the chassis temperature control operation; If the If the real-time operating temperature characteristic data of any chassis in the system is greater than the temperature threshold, the D fenxi For abnormalities, filter out the The real-time operating temperature characteristic data of all chassis greater than the temperature threshold are marked and the real-time operating abnormal temperature data set of the chassis is generated. ,in, represents the real-time abnormal temperature data of the i-th chassis, and p represents the total number of real-time abnormal temperature data of the chassis.

5. The method for intelligent chassis control based on the Internet of Things according to claim 4 is characterized in that: The S4 comprises the following steps: S41, traverse all chassis temperature monitoring location data in A through an iterative deepening search algorithm, and search for the location data corresponding to the chassis temperature monitoring location in A. The chassis temperature monitoring location data that matches the real-time abnormal temperature data of each chassis in the system are matched and the data is marked to generate the chassis real-time abnormal location data set E={e1,e2,…,e i ,…,e p },in, Indicates the location data corresponding to the real-time abnormal temperature data of the i-th chassis.

6. The method for intelligent chassis control based on the Internet of Things according to claim 5 is characterized in that: The S5 comprises the following steps: S51, the The real-time abnormal temperature data of each chassis in the E is combined with the real-time abnormal position data of the corresponding chassis in the E to generate a real-time abnormal temperature feature data set of the chassis ,in, represents the real-time abnormal temperature characteristic data of the i-th chassis, and .

7. The method for intelligently controlling a chassis based on the Internet of Things according to claim 6, characterized in that: The S6 comprises the following steps: S61. Establish chassis temperature control solution data set F={f1,f2,…,f i ,…,f o }, where f i represents the i-th chassis temperature control solution data, and o represents the total number of chassis temperature control solution data; S62, using the White Whale optimization algorithm to The temperature control scheme data of each chassis in F is matched with the temperature control scheme data of each chassis in F to search for the temperature control scheme matching the temperature control scheme of the chassis in F. The matching chassis temperature control solution data is identified and the chassis real-time temperature control solution data F is generated. fangan .

8. The method for intelligent chassis control based on the Internet of Things according to claim 7, characterized in that: The S62 comprises the following steps: S621. Construct a control scheme to search for beluga populations, set the population size to N, the current number of iterations to t, and the maximum number of iterations to t. max And the dimension of the chassis temperature control solution data search space is P; The chassis temperature control scheme data set is used as a chassis temperature control scheme data search space, and N chassis temperature control scheme data are randomly generated in the chassis temperature control scheme data search space, each chassis temperature control scheme data corresponds to a control scheme search beluga individual; S622, calculating the fitness value of each control scheme to search for the beluga individual; S623, each control scheme searches for beluga individuals through the balance factor Controlling its own swimming behavior in the data search space of the chassis temperature control solution; If the balance factor If it is greater than 0.5, each control scheme search white whale individual will adopt a mirror swimming strategy, and update its position in the chassis temperature control scheme data search space by swimming in the same direction with a randomly selected control scheme search white whale individual; If the balance factor If it is less than or equal to 0.5, each control scheme searching white whale individual will share the prey location information, and capture the best prey through collaborative methods to update the position in the chassis temperature control scheme data search space; S624, update the control scheme to search for the whale fall probability ω of the beluga individual; S625, each control scheme searches for white whale individuals that perform whale fall behavior in the chassis temperature control scheme data search space to update their positions according to the whale fall probability ω; S626, calculating the fitness value of each control scheme search white whale individual after the position is updated, if the fitness value of the control scheme search white whale individual after the position is updated is greater than the fitness value of the original position, then the new position is used to replace the original position; otherwise, the original position is retained; Arrange the beluga whale search individuals in each control scheme from large to small according to the fitness value, and select the beluga whale search individual with the highest fitness value as the current optimal individual; S627, determine whether t is less than t max , if t is less than t max , then t is increased by 1, and the process returns to S623; otherwise, the current optimal individual is taken as the global optimal solution, the chassis temperature control solution data corresponding to the global optimal solution is output and the data is marked, and F is generated. fangan .

9. The method for intelligently controlling a chassis based on the Internet of Things according to claim 8, characterized in that: The S7 comprises the following steps: S71, the and the F fangan Combine data to construct chassis temperature control data ; S72, transmitting the Push to the chassis temperature control platform and execute the chassis temperature control operation.

10. An automatic temperature control chassis system that implements the chassis intelligent control method based on the Internet of Things as described in any one of claims 1 to 9.

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