An intelligent industrial working environment adjustment system

Abstract

The present invention discloses an intelligent industrial working environment adjustment system; it belongs to the field of environmental balance technology, and includes an environmental parameter detection unit, a balance strategy design unit, a balance device control unit and a balance device; the environmental parameter detection unit includes a temperature sensor, a noise sensor and a humidity sensor, which are installed at multiple points as needed to detect the working environment parameters of factory workshop workers; the balance strategy design unit performs environmental parameter index balance strategy design based on environmental parameters through the HPO algorithm; the balance device control unit performs targeted control operations based on the control strategy, and also includes a monitoring module and an alarm module to perform alarm processing for abnormal situations. The present invention can respond to and adjust the working environment indicators of industrial workers, ensure that industrial workers work in a more comfortable working environment, and can significantly improve the quality of the working environment through precise monitoring and intelligent adjustment, thereby ensuring the health and production efficiency of workers.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental balance and relates to an intelligent industrial operating environment regulating system. Background Art

[0002] There may be many factors that affect work efficiency in the industrial workshop environment, such as humidity, temperature, noise, working hours, etc. These factors not only pose a threat to the health of workers, but may also lead to a decrease in production efficiency. The current situation of industrial workshop safety environment monitoring and protection technology is still insufficient: backward technology, imperfect monitoring system, imperfect early warning mechanism, etc. Traditional environmental monitoring and control methods may rely on manual detection and manual adjustment, which is not only inefficient, but also difficult to achieve real-time monitoring and precise control. Traditional methods may not be able to fully cover all working areas, resulting in blind spots in environmental monitoring. With the development of industrial automation and intelligence, the demand for intelligent environmental adjustment equipment is growing. This kind of equipment can have significant advantages in scenarios with backward technology, imperfect monitoring system and imperfect early warning mechanism. It can automatically adjust environmental conditions, intelligently learn, and efficiently use resources to ensure the health of workers and improve production efficiency. For the application of environmental balance adjustment equipment based on HPO algorithm in the working environment of industrial workers, we can propose the following solutions to ensure the optimization of the working environment and the health and safety of workers:

[0003] The existing environmental balance methods for industrial workshops are as follows:

[0004] The first method uses an array of multiple temperature sensors and humidity sensors to detect temperature and humidity values, and alarms are triggered when the threshold is exceeded. Relevant operators are responsible for manually adjusting the workshop environment values. Noise control and working hours are important control indicators for the work efficiency of workshop workers, and they are not intelligent enough. This solution lacks control over noise and working hours in industrial workshops.

[0005] The second method is that industrial workshops adjust the working hours of workers to reduce their working hours in order to improve production efficiency. High-power fans are used in the workshops to adjust the temperature. This method not only increases energy consumption but also increases production costs. This solution does not involve automatic adjustment of the environmental balance of the industrial workshop itself, which is a serious waste of resources.

[0006] By implementing the above solutions, industrial enterprises can effectively manage and regulate the working environment of workers, significantly improve the automation level of environmental control systems, optimize system performance, improve production efficiency, and protect the health and safety of workers, while achieving energy conservation and emission reduction, sustainable development goals, and adaptively adjusting to meet changing environments and needs. Summary of the invention

[0007] In view of the above problems, the purpose of the present invention is to address the problems existing in the prior art. The purpose of the invention of an environmental balance adjustment device based on a hyperparameter optimization (HPO) algorithm is mainly to provide an intelligent system that can automatically adjust multiple environmental parameters to maximize comfort, health and energy efficiency in work and living spaces. By integrating temperature sensors, noise sensors and humidity sensors, the system can monitor and analyze key environmental conditions and automatically adjust equipment settings to keep environmental conditions in optimal conditions.

[0008] The technical solution of the present invention is: the intelligent industrial working environment adjustment system described in the present invention includes an environmental parameter detection unit, a balancing strategy design unit, a balancing device control unit and a balancing device; the environmental parameter detection unit includes a temperature sensor, a humidity sensor and a noise sensor to detect the working environment parameters of workers; the balancing device control unit includes an alarm module, a monitoring module and a control module; the balancing device includes an emission device and a temperature control device;

[0009] The temperature sensor, humidity sensor and noise sensor are connected to the balance strategy design unit, and the recorded environmental detection data are transmitted to the balance strategy design unit. The balance strategy design unit uses the HPO algorithm to derive a control strategy based on the data transmitted by the environmental parameter detection unit, and is connected to the balance device control unit. The control module receives the control signal and conveys the control instruction according to the control strategy. The balance device control unit is connected to the humidity control device, the temperature control device and the noise reduction device, and the environmental balance is adjusted through the balance device. The environmental monitoring unit monitors and feedbacks again. If an abnormal situation occurs, the value is transmitted to the alarm module for alarm. The monitoring module transmits the value provided by the environmental parameter detection unit to the electronic display screen. If a worker faints or other conditions are detected, the data is transmitted to the alarm module for alarm.

[0010] Furthermore, the temperature sensor, humidity sensor and noise sensor are coupled and installed at multiple points.

[0011] Furthermore, the environmental parameter detection unit includes a temperature sensor, a humidity sensor and a noise sensor installed in the worker's workshop environment. The temperature sensor is placed in the central area of ​​the workshop and installed as vertically as possible to measure changes in ambient temperature. The humidity sensor is placed in the top area of ​​the center of the workshop to measure changes in air humidity. The noise sensor is installed at a location away from the workshop machines to measure the overall environmental noise level of the workshop.

[0012] Furthermore, the environmental parameter detection unit includes a temperature sensor, a humidity sensor and a noise sensor. The temperature sensor measures and records data every 2 minutes, the humidity sensor records data every 4 minutes, and the noise sensor records data every 30 seconds to ensure the real-time nature of the data while avoiding inaccurate data caused by excessive frequency.

[0013] Furthermore, the balancing equipment control unit is based on the coordinated action of an alarm module, a monitoring module and a control module, wherein the alarm module receives in real time the temperature data of the temperature sensor, the humidity data of the humidity sensor and the decibel value data of the noise sensor; when the temperature of the temperature sensor, the humidity value of the humidity sensor and the decibel value data of the noise sensor exceed preset values, an abnormal situation occurs and the alarm module promptly sends an alarm signal.

[0014] Furthermore, the humidity control device and the temperature control device exist independently. When the humidity control device reaches a certain operating standard and the temperature control device reaches a certain temperature, the environmental balance is adjusted according to a reasonable strategy designed by the balance strategy design unit module.

[0015] Furthermore, the balancing strategy design unit uses the HPO algorithm to derive the control strategy based on the data transmitted by the environmental parameter detection unit, and the implementation process is as follows:

[0016] S1: Establish and initialize the position of prey or hunter. The position of each member in the initial group is randomly generated in the search space according to formula (1);

[0017] x i =rand(1,d)*(ub-lb)+lb (1)

[0018] In the formula, x i is the position of the prey or hunter, lb is the lower bound of the variable, ub is the upper bound of the variable, and d is the dimension of the variable;

[0019] S2: Bring the fitness objective function into the hunter position for evaluation, sort the positions, and use the fitness values ​​of the devices corresponding to the top positions as the positions for the hunter to search. The fitness objective function formula is as follows:

[0020] Z=-0.0943N+0.8243T+1.8022H+0.002N×T-0.0144T×H

[0021] +0.0006N×H+0.0007N 2 +0.0014T 2 -0.0118H 2

[0022] In the formula: Z represents the best comfort level, N represents the measured humidity value, T represents the measured temperature, and H represents the actual working time;

[0023] The constraints are as follows:

[0024]

[0025] Where N min Indicates that the minimum humidity limit in the workshop is 30% RH, N max Indicates that the maximum humidity limit of the workshop is 70% RH, T min Indicates that the minimum limit temperature of the workshop is 16℃, T max Indicates that the maximum limit temperature of the workshop is 25℃, H max It means that the longest working time in the workshop is 8h;

[0026] S3: For the hunter search mechanism, the following mathematical model is given:

[0027] x i,j (t+1)=x i,j (t)+0.5[(2CZP pos(j) -x i,j (t))+(2(1-C)Zμ(j)-x i,j (t))]

[0028] Among them, x(t) is the current hunter position, x(t+1) is the next iteration position of the predator, and P pos is the position of the prey, μ is the average of all positions, and Z is the adaptive parameter calculated by equation (3):

[0029] S4: Set the algorithm related parameters calculated by equations (2)-(6);

[0030]

[0031] Where P is The index value of , Z is the adaptive parameter, and is a random vector in [0,1], R2 is a random number in [0,1], R1 represents the balance vector, R2 represents the balance weight of exploration and development, and R3 represents the exploration and development direction factor;

[0032] IDX is a vector that satisfies the condition (P == 0) The index value of , C is the balance parameter between exploration and development, μ is the average value of all positions, and D euc(i) is the Euclidean distance of all positions to the mean position, It is the location of the prey;

[0033] S5: Calculate the fitness value and record the optimal position, i.e., optimal temperature, optimal humidity, and optimal working time;

[0034] kbest = round(C×N)

[0035] Where N is the number of search agents,

[0036] S6: Update C by equation (7);

[0037]

[0038] In the formula, m is the current number of iterations, and M is the maximum number of iterations;

[0039] S7: Update the position according to the value of the determination factor R5;

[0040]

[0041] In the formula, x i (t) is the current position of the prey, x i (t+1) is the next iteration position of the prey, T pos is the global optimal position, R4 is a random constant in [0, 1], which represents the influencing factor of the global optimal position of the next prey in different directions; the decision factor R5 is a random number in [0, 1], and β is the adjustment parameter;

[0042] S8: Calculate the fitness value again and record the optimal position, that is, the optimal temperature, the optimal humidity, and the optimal working time;

[0043] kbest = round(C×N)

[0044] Where N is the number of search agents;

[0045] S9: Taking the working time and humidity, ambient temperature as input, and the operating standard of the balancing equipment as the objective function, determine whether the termination condition is met, and if so, output the optimal solution, otherwise repeat S4 to S6;

[0046] S10: transmitting the obtained balancing equipment operation standard to the control module to issue an adjustment instruction;

[0047] Furthermore, the improved part of the HPO algorithm is to improve S6, introduce a chaotic reverse learning strategy to improve the hunter position; adaptively reduce the early stage of algorithm iteration, so as to better search for the hunter's prey position and improve the speed of finding the optimal position; the improved formula is as follows:

[0048] ω=tanh(2(1-m / M))

[0049]

[0050] In the formula, ω is the weight coefficient, tanh is the activation function, and x i (t+1) is the optimal position of the hunter or prey.

[0051] The beneficial effects of the present invention are as follows: 1. Coupling the temperature sensor, the noise sensor and the humidity sensor can reduce the space occupied by the original equipment, and can simultaneously detect humidity, noise in the field and temperature in the field; multi-point installation can fully detect and adjust the environmental indicators of workers' work; 2. When the temperature data of the temperature sensor, the humidity value data of the humidity sensor and the decibel data of the noise sensor exceed the preset value, the alarm module and the monitoring module can work together to communicate and issue an alarm. 3. The present invention also uses the HPO algorithm to optimize the environmental balance adjustment equipment, so that the system can not only balance the environmental indicators of the workers' work, but also generate the optimal solution according to the algorithm, and minimize the use of environmental balance equipment under the premise of ensuring the adjustment of environmental indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the hardware structure of the present invention;

[0053] Figure 2 This is a flow chart of the HPO algorithm used in the present invention. DETAILED DESCRIPTION

[0054] The specific technical scheme of the present invention is further described in detail below with reference to specific examples.

[0055] The present invention proposes an intelligent industrial working environment adjustment technology, such as Figure 1 As shown, it includes an environmental parameter detection unit, a balancing strategy design unit, a balancing equipment control unit and a balancing equipment; the environmental parameter detection unit includes a temperature sensor, a noise sensor and a humidity sensor, which are installed at multiple points as needed to detect the working environment parameters of the workers; the balancing strategy design unit designs the balancing strategy of environmental parameter indicators based on the environmental parameters through the HPO algorithm; the balancing equipment control unit makes targeted control instructions based on the control strategy, including a monitoring module, an alarm module and a control module, and performs alarm processing for abnormal situations; the balancing equipment includes a humidity control device, a noise control device and a temperature control device.

[0056] The temperature sensor, humidity sensor and noise sensor in the environmental parameter detection unit are connected to the balancing device control unit. The alarm module and the monitoring module work together to convey abnormal conditions. The monitoring module of the control unit transmits the numerical value provided by the environmental parameter detection unit to the process balancing strategy design unit; the balancing strategy design unit is connected to the balancing device control unit, and the balancing device control unit is connected to the emission device and the temperature control device, and the environmental balance is adjusted through the balancing device.

[0057] The temperature sensor, noise sensor and humidity sensor in the environmental parameter detection unit are coupled and installed at multiple points. When the temperature data of the temperature sensor, the humidity value data of the humidity sensor and the decibel data of the noise sensor exceed the preset values, an alarm is issued.

[0058] The balancing equipment control unit module is based on the coordinated action of the alarm module, the monitoring module and the control module, wherein the alarm module receives the temperature data of the temperature sensor, the decibel data of the noise sensor and the concentration data of the humidity sensor in real time. When the temperature of the temperature sensor, the humidity value data of the humidity sensor and the decibel data of the noise sensor exceed the preset values, the alarm module promptly transmits the alarm signal; the control module in the balancing equipment control unit module receives the alarm signal and transmits various instructions according to the provided control strategy; the monitoring module monitors the various data of the temperature sensor, the noise sensor and the humidity sensor in real time.

[0059] The balance strategy design unit uses the HPO algorithm to derive the control strategy based on the data transmitted by the environmental parameter detection unit. The humidity control device and temperature control device in the system balance device exist independently. When the humidity control device reaches a certain operating standard and the temperature control device reaches a certain temperature, a reasonable strategy is designed according to the control module of the balance device control unit to adjust the environmental balance.

[0060] When in use, the present invention couples the temperature sensor, humidity sensor and noise sensor, and installs them at multiple points, and uses the alarm module in the balancing equipment control unit and the control unit monitoring module for collaborative detection. The control unit monitoring module monitors the temperature in real time, and the alarm module issues an alarm. The temperature is measured by the temperature sensor, and the monitoring module monitors the temperature, humidity value and noise decibel in real time. When the actual value exceeds the preset value, the alarm module alerts the abnormal condition, and the balancing strategy design unit transmits the data to the balancing equipment control unit. The balancing equipment control unit then uses the control strategy to enable the balancing equipment module to issue instructions, the emission equipment performs emission, and the temperature control equipment performs temperature control.

[0061] The alarm module and the monitoring module work together to promote the stable circulation of the system. The monitoring module monitors, and the alarm module is coupled with the temperature sensor, humidity sensor and noise sensor. The temperature sensor, noise sensor and humidity sensor output the detection results, and the balance strategy design unit transmits the data results to the balance equipment control unit. When the monitoring module encounters abnormal transmission data or the alarm module fails to alarm, the monitoring module transmits an alarm signal. When the alarm module detects the transmission data or encounters a situation where the normal temperature cannot be detected or the measured temperature exceeds the preset value, the alarm module sends an alarm signal. The monitoring module monitors and the alarm module monitors each other for double protection.

[0062] The present invention utilizes the environmental balance adjustment device optimization method of the HPO algorithm, and uses the balance design unit to be controlled by the algorithm, such as Figure 2 As shown, the HPO algorithm is specifically as follows:

[0063] 1) Initialization: the position of each member in the initial group is randomly generated in the search space according to formula (1);

[0064] x i =rand(1,d)*(ub-lb)+lb (1)

[0065] Among them, x i is the position of the hunter or prey, lb is the lower bound of the variable, ub is the upper bound of the variable, and d is the dimension of the variable;

[0066] 2) Bring the fitness objective function into the hunter position for evaluation, sort the positions, and use the fitness values ​​of the devices corresponding to the top positions as the positions for the hunter to search. The fitness objective function formula is as follows:

[0067] Z=-0.0943N+0.8243T+1.8022H+0.002N×T-0.0144T×H

[0068] +0.0006N×H+0.0007N 2 +0.0014T 2 -0.0118H 2

[0069] In the formula: Z represents the best comfort level, N represents the measured humidity value, T represents the measured temperature, and H represents the actual working time;

[0070] The constraints are as follows:

[0071]

[0072] Where N min Indicates that the minimum humidity limit in the workshop is 30% RH, N max Indicates that the maximum humidity limit of the workshop is 70% RH, T min Indicates that the minimum limit temperature of the workshop is 16℃, T max Indicates that the maximum limit temperature of the workshop is 25℃, H max It means that the maximum working time of the workshop is 8h;

[0073] 3) For the hunter search mechanism, the following mathematical model is given:

[0074] x i,j (t+1)=x i,j (t)+0.5[(2CZP pos(j) -xi,j (t))+(2(1-C)Zμ(j)-x i,j (t))]

[0075] Among them, x(t) is the current hunter position, x(t+1) is the next iteration position of the predator, and P pos is the position of the prey, μ is the average of all positions, and Z is the adaptive parameter calculated by equation (3):

[0076] 4) Set the algorithm-related parameters calculated by formulas (2)-(6);

[0077]

[0078] Where P is The index value of , Z is the adaptive parameter, and is a random vector in [0,1], R2 is a random number in [0,1], and IDX is a vector that satisfies the condition (P==0) The index value of , C is the balance parameter between exploration and development, μ is the average value of all positions, and D euc(i) is the Euclidean distance of all positions to the mean position, It is the location of the prey;

[0079] 5) Calculate the fitness value and record the optimal position;

[0080] kbest = round(C×N)

[0081] Where N is the number of search agents,

[0082] 6) Update C by formula (7);

[0083]

[0084] In the formula, m is the current number of iterations, and M is the maximum number of iterations;

[0085] 7) Update the position according to the value of the determination factor R5;

[0086]

[0087] In the formula, x i (t) is the current position of the prey, x i (t+1) is the next iteration position of the prey, Tpos is the global optimal position, R4 is a random number in [0, 1], R5 is a random number in [0, 1], and β is the adjustment parameter;

[0088] 8) Calculate the fitness value again and record the optimal position, that is, the optimal temperature, optimal humidity, and optimal working time;

[0089] kbest = round(C×N)

[0090] Where N is the number of search agents;

[0091] 9) Taking the working time, humidity value and ambient temperature as input and the operating standard of the balancing equipment as the objective function, determine whether the termination condition is met. If so, output the optimal solution, otherwise repeat steps 4 to 6);

[0092] 10) The obtained balancing equipment operation standard is transmitted to the control module to issue adjustment instructions.

[0093] Furthermore, the improved part of the HPO algorithm is to improve S6, introduce a chaotic reverse learning strategy to improve the hunter position; adaptively reduce the early stage of algorithm iteration, so as to better search for the hunter's prey position and improve the speed of finding the optimal position; the improved formula is as follows:

[0094] ω=tanh(2(1-m / M))

[0095]

[0096] In the formula, ω is the weight coefficient, tanh is the activation function, and x i (t+1) is the optimal position of the hunter or prey.

[0097] The balancing device proposed in the present invention is installed at multiple points in space and uses the HBO algorithm to obtain an excellent control strategy to regulate the balancing device, so that the balancing device proposed in the present invention has a faster response speed than other balancing devices, and the balancing time is also shorter than other balancing devices, which has an excellent environmental balancing effect.

[0098] The present invention provides an advanced and accurate workshop environment prediction and control solution, which is mainly used in the central area of ​​the main workshop; in the actual working environment of the workers, the technology can monitor the changes in the workshop environment values ​​in real time and adjust the workshop environment through an intelligent system integrating an environmental parameter detection unit, a balance strategy design unit and a balance equipment control unit. Through accurate data detection, analysis and intelligent control, the technology can respond to and adjust the working environment indicators of industrial workers, ensure that industrial workers work in a more comfortable environment, and significantly improve the quality of the working environment, thereby ensuring the health and production efficiency of workers.

[0099] Implementable cases: The application of the present invention is not limited to workshop environment control, it also has the potential for flexible use in a variety of scenarios. For example, in large poultry farms, environmental conditions are directly related to poultry growth efficiency and economic benefits. By applying this technology to poultry farms, the environment for poultry growth, such as key parameters such as temperature and humidity, can be monitored in real time, and real-time control can be performed by analyzing the changing trends of these parameters. Once it is monitored that the environment exceeds the preset value and the secondary control is invalid, the system can automatically trigger the alarm mechanism to notify the management personnel to take corresponding measures in time to ensure the normal operation of the farm and the reliability of environmental values. In addition, this technology can also be applied to other similar scenarios regarding environmental control, such as vegetable and fruit planting, laboratory chemical storage, etc., to provide strong technical support and security for various environmental control needs.

Claims

1. An intelligent industrial working environment adjustment system, characterized in that: It includes an environmental parameter detection unit, a balancing strategy design unit, a balancing device and a balancing device control unit which are connected to each other; The environmental parameter detection unit includes a temperature sensor, a humidity sensor and a noise sensor; The balancing equipment includes a humidity control device, a temperature control device and a noise reduction device; The balancing equipment control unit includes an alarm module, a monitoring module and a control module; The temperature sensor, humidity sensor and noise sensor are connected to the balance strategy design unit and transmit the recorded environmental detection data to the balance strategy design unit; The balancing strategy design unit uses the HPO algorithm to derive the control strategy based on the data transmitted by the environmental parameter detection unit, and is connected to the balancing device control unit through the balancing device; The balancing device control unit is connected to the humidity control device, the temperature control device and the noise reduction device, and performs environmental balance adjustment through the balancing device; The control module receives the control signal and transmits the control instruction according to the control strategy. The environmental parameter detection unit monitors and feedbacks again. If an abnormal situation occurs, the value is transmitted to the alarm module for alarm. The monitoring module transmits the value provided by the environmental parameter detection unit to the electronic display screen, and then transmits the data to the alarm module to issue an alarm; The temperature sensor, humidity sensor and noise sensor are placed in the worker's workshop environment and are placed at multiple points; The temperature sensor is placed in the center of the workshop and arranged vertically to measure the change of ambient temperature; The humidity sensor is placed in the center top area of ​​the workshop to measure the change of air humidity; The noise sensor is placed at a location far away from the workshop machines to measure the overall ambient noise level of the workshop; The temperature sensor measures and records data every 2 minutes, the humidity sensor records data every 4 minutes, and the noise sensor records data every 30 seconds to ensure the real-time nature of the data; The alarm module receives the temperature data of the temperature sensor, the humidity data of the humidity sensor and the decibel value data of the noise sensor in real time. When the temperature of the temperature sensor, the humidity value of the humidity sensor and the decibel value data of the noise sensor exceed the preset values, an abnormal situation occurs and the alarm module promptly sends an alarm signal. The humidity control device, temperature control device and noise reduction device exist independently. When the humidity control device reaches the operating standard, the temperature control device reaches a fixed temperature, and the noise reduction device reaches a fixed decibel standard, the environmental balance is adjusted according to the reasonable strategy designed by the balance strategy design unit module. The balance strategy design unit uses the HPO algorithm to obtain the control strategy based on the data transmitted by the environmental parameter detection unit. The implementation process is as follows: S1: Establish and initialize the position of prey or hunter. The position of each member in the initial group is randomly generated in the search space according to formula (1); x i =rand(1,d)·*(ub-lb)+lb (1) In the formula, x i is the position of the prey or hunter, lb is the lower bound of the variable, ub is the upper bound of the variable, and d is the dimension of the variable; S2: Bring the fitness objective function into the prey position for evaluation, sort the positions, and use the fitness values ​​of the devices corresponding to the top positions as the prey search positions. The fitness objective function formula is as follows: Z=-0.0943N+0.8243T+1.8022H+0.002N×T-0.0144T×H +0.0006N×H+0.0007N 2 +0.0014T 2 -0.0118H 2 In the formula: Z represents the best comfort level, N represents the measured humidity value, T represents the measured temperature, and H represents the actual working time; The constraints are as follows: Where N min Indicates that the minimum humidity limit in the workshop is 30% RH, N max Indicates that the maximum humidity limit of the workshop is 70% RH, T min Indicates that the minimum limit temperature of the workshop is 16℃, T max Indicates that the maximum limit temperature of the workshop is 25℃, H max It means that the maximum working time of the workshop is 8h; S3: For the hunter search mechanism, the following mathematical model is given: x i,j (t+1)=x i,j (t)+0.5[(2CZP pos(j) -x i,j (t))+(2(1-C)Zμ(j)-x i,j (t))] Among them, x(t) is the current hunter position, x(t+1) is the next iteration position of the predator, and P pos is the position of the prey, μ is the average of all positions, and Z is the adaptive parameter calculated by equation (3): S4: Set the algorithm related parameters calculated by equations (2)-(6); Where P is The index value of , Z is the adaptive parameter, and is a random vector in [0,1], R2 is a random number in [0,1], R1 represents the balance vector, R2 represents the balance weight of exploration and development, and R3 represents the exploration and development direction factor. IDX is a vector that satisfies the condition (P == 0) The index value of , C is the balance parameter between exploration and development, μ is the average value of all positions, and D euc(i) is the Euclidean distance of all positions to the mean position, It is the location of the prey; S5: Calculate the fitness value and record the optimal position, i.e., optimal temperature, optimal humidity, and optimal working time; kbest = round(C×N) Where N is the number of search agents; S6: Update C by equation (7); In the formula, m is the current number of iterations, and M is the maximum number of iterations; S7: Update the position according to the value of the determination factor R5; In the formula, x i (t) is the current position of the prey, x i (t+1) is the next iteration position of the prey, T pos is the global optimal position, R4 is a random constant in [0, 1], which indicates the influencing factor of the global optimal position of the next prey in different directions; the decision factor R5 is a random number in [0, 1], β is an adjustment parameter with a value of 0.2; if R5<β, the search agent is regarded as a hunter and the position is updated according to the hunter hunting mechanism; if R5≥β, the search agent is regarded as a prey and the position is updated according to the prey movement mechanism; S8: Calculate the fitness value again and record the optimal position, that is, the optimal temperature, the optimal humidity, and the optimal working time; kbest = round(C×N) Where N is the number of search agents; S9: Taking the working time and humidity, ambient temperature as input, and the operating standard of the balancing equipment as the objective function, determine whether the termination condition is met, and if so, output the optimal solution, otherwise repeat S4 to S6; S10: The obtained balancing equipment operation standard is transmitted to the control module to issue an adjustment instruction.

2. The intelligent industrial working environment adjustment system according to claim 1, characterized in that: The HPO algorithm is improved specifically in S6, introducing a chaotic reverse learning strategy to improve the hunter position; Through the algorithm iteration, it is adaptively reduced in the early stage, so as to search for the hunter's prey position and improve the speed of finding the optimal position; the improved formula is as follows: ω=tanh(2(1-m / M)) In the formula, ω is the weight coefficient, tanh is the activation function, and x i (t+1) is the optimal position of the prey.

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