Chemical fiber production environment dynamic sensing and regulation method based on ad hoc network technology

Abstract

The invention provides a chemical fiber production environment dynamic sensing and regulation method based on an ad hoc network technology, and relates to the field of chemical fiber production environment control. The method comprises the following steps: arranging a plurality of ad hoc network sensor nodes in a production workshop, acquiring environment data in real time and transmitting the environment data to a central processing unit; through data fusion and processing, dynamic change trends of environmental parameters such as temperature and humidity, gas concentration and pressure are obtained; an environment regulation and control model is established according to the processing result, and control instructions such as temperature and humidity, wind speed and gas emission are generated; a regulation and control instruction is transmitted to related equipment through an ad hoc network protocol, and the workshop environment is regulated in real time; and a regulation and control strategy is dynamically optimized through a feedback control mechanism, so that the stability and the production efficiency of a production environment are ensured. According to the method, the intelligence and automation level of the production process is improved, the production environment is optimized, resource waste is reduced, and the method has high application value.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical fiber production environment control, and in particular to a method for dynamic sensing and regulating chemical fiber production environment based on self-organizing network technology. Background Art

[0002] In the process of chemical fiber production, factors such as temperature, humidity, and airflow in the workshop environment have a significant impact on production efficiency and product quality. However, existing technologies still have significant deficiencies in workshop environment monitoring and control, which are mainly reflected in the following aspects:

[0003] Limited monitoring coverage: Traditional workshop environment monitoring systems mostly use fixed-point sensor layouts, which are difficult to fully reflect the dynamic changes in the workshop environment. Especially when the production line is long or there are multiple operating areas, single-point monitoring cannot effectively cover the entire production environment, resulting in the existence of monitoring blind spots.

[0004] Insufficient data processing capabilities: Existing monitoring systems usually rely on a centralized data processing architecture to transmit all collected data to a central processing unit for analysis. This approach not only increases data transmission latency, but may also lead to data loss or reduced processing efficiency due to transmission bandwidth limitations.

[0005] Environmental control response lags: Due to the lack of efficient data processing and linkage mechanisms, existing control methods are mostly based on manual adjustments or fixed procedures, which are difficult to flexibly adjust according to real-time environmental changes, which may affect product quality stability and energy efficiency.

[0006] Poor system scalability and reliability: The centralized monitoring and control system needs to significantly transform the original system when expanding sensor nodes or adding new functions, which is complex and costly to implement. In addition, the system has a low tolerance for single point failures, which may lead to overall performance degradation or even paralysis.

[0007] Therefore, developing a dynamic perception and control technology for the workshop environment that can achieve full coverage real-time monitoring, efficient data processing, rapid response regulation, and has good scalability and reliability has become an important requirement for the chemical fiber industry to improve production efficiency and product quality. Summary of the invention

[0008] In order to solve the technical problems in the prior art such as limited monitoring coverage, insufficient data processing capability, delayed environmental control response, and poor system scalability and reliability, the present invention provides a method for dynamic perception and control of a chemical fiber production environment based on self-organizing network technology.

[0009] The technical solution provided by the present invention is as follows:

[0010] The present invention provides a method for dynamic perception and control of chemical fiber production environment based on self-organizing network technology, comprising:

[0011] S1. Arrange multiple self-organizing network sensor nodes in the chemical fiber production workshop, each sensor node includes a temperature sensor, a humidity sensor, a gas concentration sensor, a pressure sensor and an air flow rate sensor;

[0012] S2, through the ad hoc networking protocol, the sensor nodes form a self-organizing, distributed data transmission network, collecting and transmitting environmental data to the central processing unit in real time;

[0013] S3, the central processing unit receives the real-time data of all sensor nodes, performs data fusion and processing, and obtains the dynamic change trend of various important indicators in the chemical fiber production environment, including temperature and humidity indicators, gas concentration indicators, pressure indicators and flow rate indicators;

[0014] S4. Based on the data processing results, an environmental control model is established, wherein the model includes an environmental stability index ESI and a production efficiency index PEI;

[0015] S5. Generate environmental control instructions according to the control model, wherein the environmental control instructions include temperature and humidity control, wind speed adjustment and gas emission control;

[0016] S6. Transmitting the environmental control instructions to relevant devices in real time through a self-organizing network protocol for execution, so as to adjust the environment of the chemical fiber production workshop and optimize production conditions;

[0017] S7. During the regulation process, monitor the regulation effect in real time, and dynamically adjust the regulation strategy through the feedback control mechanism to ensure continuous optimization of the production environment, thereby improving production efficiency and reducing resource waste.

[0018] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0019] (1) In the present invention, by introducing the environmental stability index ESI and the production efficiency index PEI, the method can monitor and adjust the environmental conditions of the chemical fiber production workshop in real time, ensuring that parameters such as temperature, humidity, and gas concentration during the production process are always in the best state. The combination of ESI and PEI can effectively predict and adjust the production process and environmental conditions, thereby optimizing the production process, improving production efficiency, reducing resource waste, and ultimately improving overall production capacity and production benefits;

[0020] (2) In the present invention, self-organizing network sensor technology is used to collect environmental data such as temperature and humidity, gas concentration, wind speed, etc. in real time, and stably transmit them to the central processing unit through wireless protocol. The system can accurately analyze environmental parameters and timely feedback adjustment instructions to ensure that the environment of the production workshop is always in the optimal state. The feedback control mechanism also enables the control strategy to be dynamically adjusted, further improving production efficiency and reducing the impact of manual intervention and environmental fluctuations on production;

[0021] (3) In the present invention, a fault detection and repair mechanism for sensor nodes is provided to ensure the continuity and reliability of production environment monitoring. When a sensor fails, the system will automatically replace the failed node with a spare node to avoid data interruption or environmental loss of control due to equipment failure. This mechanism greatly improves the stability and reliability of the system and ensures that data collection and environmental control in the production process are always uninterrupted. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a process flow of a method for dynamic sensing and control of a chemical fiber production environment based on a self-organizing network technology provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of an optimization process of an environmental stability index ESI of a method for dynamic perception and control of a chemical fiber production environment based on a self-organizing network technology provided in an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of a production efficiency index PEI optimization process of a method for dynamic perception and control of a chemical fiber production environment based on a self-organizing network technology provided by an embodiment of the present invention;

[0026] Figure 4 A schematic diagram of a production process flow of dynamically adjusting the production efficiency index PEI of a method for dynamic perception and regulation of a chemical fiber production environment based on self-organizing network technology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0028] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0029] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0030] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0031] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0032] Reference Manual Attached Figure 1 , showing a flow chart of a method for dynamic perception and control of a chemical fiber production environment based on self-organizing network technology provided by an embodiment of the present invention.

[0033] The embodiment of the present invention provides a method for dynamic perception and control of chemical fiber production environment based on self-organizing network technology. The processing flow may include the following steps:

[0034] S1. Arrange multiple self-organizing network sensor nodes in the chemical fiber production workshop, each sensor node includes a temperature sensor, a humidity sensor, a gas concentration sensor, a pressure sensor and an air flow rate sensor.

[0035] It is understandable that by arranging multiple self-organizing network sensor nodes in the chemical fiber production workshop, the environmental conditions in the workshop can be fully monitored. Each sensor node integrates temperature, humidity, gas concentration, pressure and air flow rate sensors to ensure comprehensive perception of the environment. By arranging multiple sensor nodes, environmental data in different areas and at different heights can be obtained, providing more accurate information for subsequent regulation, thereby improving the management level of the production environment and ensuring the stability of the chemical fiber production process and product quality.

[0036] S2. Through the ad hoc networking protocol, the sensor nodes form a self-organizing, distributed data transmission network to collect and transmit environmental data to the central processing unit in real time.

[0037] It is understandable that the self-organizing network protocol enables each sensor node to automatically connect wirelessly and form a distributed data transmission network, which can reduce the complexity and cost of wiring. Each sensor node can work independently, but can also transmit data collaboratively to ensure data real-time and reliability. In this way, the system can collect and transmit environmental data in real time, provide a continuous input to the central processing unit, and enable the control system to dynamically respond to changes in the environment.

[0038] S3. The central processing unit receives real-time data from all sensor nodes, performs data fusion and processing, and obtains the dynamic change trends of various important indicators in the chemical fiber production environment, including temperature and humidity indicators, gas concentration indicators, pressure indicators and flow rate indicators.

[0039] It is understandable that the central processing unit can accurately analyze the dynamic change trends of various important indicators in the chemical fiber production environment by fusing and processing the data from all sensors. This process can not only monitor environmental changes in real time, but also predict environmental trends through data analysis, providing a basis for formulating precise control measures. By processing and analyzing environmental data in real time, the system can promptly detect potential abnormal changes, thereby avoiding production efficiency decline or product quality problems caused by environmental fluctuations.

[0040] S4. Based on the data processing results, an environmental control model is established. The model includes the Environmental Stability Index (ESI) and the Production Efficiency Index (PEI).

[0041] It is understandable that building an environmental control model based on the processing results of environmental data and introducing the environmental stability index ESI and the production efficiency index PEI can quantify and comprehensively reflect the stability and production efficiency of the production environment. These two indexes provide a scientific basis for environmental control and help decision makers adjust production conditions according to actual needs. The introduction of ESI and PEI makes the control work more systematic and precise, which helps to optimize the production environment and processes in real time and improve the overall production efficiency.

[0042] S5. Generate environmental control instructions based on the control model. The environmental control instructions include temperature and humidity control, wind speed adjustment and gas emission control.

[0043] It is understandable that generating environmental control instructions is a key step to ensure the optimization of the production environment. Through the calculation of the control model, the system can automatically generate instructions such as temperature and humidity adjustment, wind speed adjustment, and gas emission control based on real-time data and analysis results. These instructions will be used to directly adjust the workshop environment to meet the production process requirements. The automated generation of environmental control instructions reduces human intervention, improves response speed and control accuracy, and ensures the stability and efficiency of the production process.

[0044] S6. The environmental control instructions are transmitted to the relevant equipment in real time through the self-organizing network protocol for execution, so as to adjust the environment of the chemical fiber production workshop and optimize the production conditions.

[0045] It is understandable that through the self-organizing network protocol, environmental control instructions can be transmitted to relevant equipment in real time for execution, thereby effectively adjusting the environmental conditions of the chemical fiber production workshop. Wireless transmission not only improves the efficiency of instruction transmission, but also can flexibly respond to rapid changes in the workshop environment and ensure continuous optimization of production conditions. This real-time adjustment mechanism can adjust environmental parameters according to demand at different production stages, thereby providing more favorable conditions for production and reducing energy consumption and resource waste.

[0046] S7. During the regulation process, monitor the regulation effect in real time, and dynamically adjust the regulation strategy through the feedback control mechanism to ensure continuous optimization of the production environment, thereby improving production efficiency and reducing resource waste.

[0047] It is understandable that real-time monitoring of control effects and adjustment of control strategies through feedback control mechanisms can ensure continuous optimization of the production environment. This mechanism can flexibly adjust the control scheme based on real-time feedback information to cope with unexpected situations that may occur in different production processes. Feedback control can effectively ensure that the production environment is always in the best state, thereby avoiding production stagnation or waste of resources, ultimately improving production efficiency and product quality, while reducing energy consumption that may be generated during environmental adjustment.

[0048] In a possible implementation, the calculation formula of the environmental stability index ESI is:

[0049]

[0050] Among them, T i is the temperature value measured by the i-th sensor node, H i is the humidity value measured by the i-th sensor node, C i is the gas concentration value measured by the i-th sensor node, P i is the gas pressure value measured by the i-th sensor node, T ref , H ref , C ref , P refare the corresponding reference values, S j is the wind speed value measured by the jth sensor node, S ref is the reference wind speed value, n is the number of sensor nodes, and m is the number of wind speed sensor nodes.

[0051] In one possible implementation, the environmental stability index ESI is further weighted averaged based on the data of the temperature, humidity, gas concentration, pressure and wind speed sensor nodes to optimize the error correction in the calculation process, so that the calculation result of ESI is more accurate and can more accurately reflect the changing trend of the production workshop environment.

[0052] Specifically, in the method of dynamic perception and control of chemical fiber production environment based on self-organizing network technology, in order to optimize the calculation of environmental stability index ESI, the data of multiple sensor nodes need to be weighted averaged. The purpose of weighted average is to dynamically adjust the weight of each data item according to the importance or credibility of different sensor data, so as to more accurately reflect the real state of the environment. The steps are as follows:

[0053] The environmental data (such as temperature, humidity, gas concentration, air pressure and wind speed) collected by each sensor node may contain certain errors. The measurement accuracy, response speed and transmission stability of different sensors may be different, so their impact on the environmental stability index ESI is also different.

[0054] Error correction is performed on the measurement data of each sensor node, which can be adjusted by factors such as sensor calibration error, environmental interference, etc. Based on the corrected data, the signal credibility or importance of each sensor node is calculated, and different weights are assigned.

[0055] The measurement data of all sensor nodes are weighted averaged, where the data of each node is multiplied by a predetermined weight. The result of weighted average can more accurately reflect the changing trend of the entire production environment.

[0056] Finally, the environmental stability index ESI is calculated by weighted averaging the data. Weighted averaging can not only optimize error correction, but also dynamically adjust the contribution of each sensor node to the environmental stability index.

[0057] Assume there are n sensor nodes, each node i provides a measurement value X i , the corresponding weight is w i , the weighted average calculation formula is:

[0058]

[0059] Among them, X i is the measurement value of the i-th sensor node (which can be temperature, humidity, gas concentration, air pressure or wind speed, etc.), wi is the weight of the i-th sensor node, indicating the influence of the measurement data of this node on the environmental stability index.

[0060] Weight w i The calculation method can be dynamically adjusted based on the accuracy, reliability or interference factors of the sensor in the environment. For example, if the error of the measurement value of a sensor node is small, then the weight of the node can be large; conversely, if the sensor node has a large error or instability, its weight is small.

[0061] A common way to calculate weights is to determine the weights based on the sensor's standard deviation σ (reflecting the degree of discreteness of the measured data). The smaller the standard deviation, the more accurate the sensor's measurement data, so its weight should be greater. The specific calculation formula is:

[0062]

[0063] Among them, σ is the standard deviation of the i-th sensor node, which reflects the reliability of the sensor node data.

[0064] Combining the above formulas, the weighted average environmental stability index ESI can be expressed as:

[0065]

[0066] Assume that there are three sensor nodes, and the measured temperature values ​​are X1 = 25 °C, X2 = 24.5 °C, and X3 = 25.2 °C, respectively. The corresponding standard deviations are σ1 = 0.5, σ2 = 0.3, and σ3 = 0.4, respectively. The weighted average of the environmental stability index ESI is calculated as:

[0067]

[0068] After calculating the weight of each sensor, the final ESI value is obtained through the weighted average formula. The weighted average method can effectively combine the data of each sensor, and use the measurement accuracy of each sensor (quantified by standard deviation) to dynamically adjust its impact on the environmental stability index ESI, thereby obtaining more accurate results. This method has practical application significance in the dynamic perception and regulation of the chemical fiber production environment, and helps to improve the environmental control accuracy and efficiency of the production process.

[0069] In a possible implementation, Figure 2 As shown, the environmental stability index ESI is further optimized by the following steps:

[0070] S401, each sensor node calculates the deviation between each environmental parameter and the reference value in real time according to the current temperature and humidity, gas concentration and pressure data, and the calculation result is used to update the ESI value;

[0071] S402: For a wind speed sensor node, calculate the difference between the current wind speed and the reference wind speed, and perform weighted correction based on the measurement accuracy of the wind speed sensor to obtain a more accurate wind speed difference value;

[0072] S403. The final ESI value combines the deviations of various environmental parameters from the reference values, as well as the correction value of wind speed, to reflect the stability of the entire production environment.

[0073] Specifically, the steps of weighted correction are as follows:

[0074] Each wind speed sensor node collects wind speed data in the current environment. Assume that the wind speed measured by a wind speed sensor node i is V i At the same time, the system provides a reference wind speed value V ref , usually this reference wind speed is obtained through historical data or standard measurements.

[0075] Calculate the difference between the current wind speed and the reference wind speed:

[0076] ΔV i =V i -V ref

[0077] Where, ΔV i is the difference between the ith sensor and the reference wind speed.

[0078] Since the accuracy of each wind speed sensor is different, there may be measurement errors in the wind speed sensor. In order to reduce the impact of the error, the wind speed difference value is usually weighted and corrected according to the measurement accuracy of the sensor. Assume that the measurement accuracy of the sensor is given by the standard deviation σ i It means that the smaller the standard deviation, the more accurate the sensor's measurement is and the larger the weight should be.

[0079] Using the weighted correction method of standard deviation, the corrected wind speed difference value ΔV′ i The calculation formula is:

[0080]

[0081] Among them, is the wind speed difference value after weighted correction, is the uncorrected wind speed difference value, and is the standard deviation of the wind speed sensor, which indicates the measurement accuracy of the sensor.

[0082] Corrected wind speed difference ΔV′ iIt reflects the result after correction of the sensor's measurement error. A lower standard deviation (higher accuracy) will result in a larger correction value, while a higher standard deviation (lower accuracy) will reduce the impact of the sensor on the overall result.

[0083] Finally, by weighting and correcting the wind speed difference values ​​of all sensor nodes, a more accurate overall wind speed difference value is obtained, which can then be incorporated into the calculation of the environmental stability index ESI. The environmental stability index is updated by combining the data of all sensors and the corrected wind speed difference value.

[0084] Assume there are n wind speed sensor nodes, and the wind speed measured by each node i is V i , with a standard deviation of σ i , the reference wind speed is V ref , then for each wind speed sensor node, the corrected difference value ΔV′ o The calculation formula is:

[0085]

[0086] The weighted wind speed difference values ​​of all sensor nodes are combined to obtain a weighted wind speed difference value ΔV total :

[0087]

[0088] The standard deviation of a wind speed sensor is an indicator used to measure the stability and accuracy of its measurement data. A sensor with a smaller standard deviation means that its measurement result is closer to the true value, so it is given a higher weight when calculating the weighted correction. Through weighted correction, the impact of sensors with larger errors on the overall environmental stability index ESI can be effectively reduced, thereby improving the reliability of wind speed data in comprehensive evaluation. The wind speed difference value ΔV after comprehensive correction total Not only does it take into account the variance of all wind speed sensors, it also adjusts for the accuracy (standard deviation) of each sensor so that more reliable sensors account for a larger proportion in the calculation of wind speed variance.

[0089] In a possible implementation, the calculation formula of the production efficiency index PEI is:

[0090]

[0091] Among them, T k is the completion time of the kth production process, R k is the production rate of the kth process, D k is the downtime proportion of the kth process, and p is the total number of production processes.

[0092] In a possible implementation, Figure 3 As shown, the production efficiency index PEI is further optimized by the following steps:

[0093] S501. For each production process, the operation status of the process is tracked in real time, the real-time operation data of the equipment in the process is collected through sensors, and the production rate and downtime of each process are calculated.

[0094] It is understandable that by tracking the operating status of each production process in real time and collecting real-time operating data of the equipment through sensors, the dynamic situation of each process in the production process can be grasped in time. The core role of this step is to obtain accurate process operation data to help analyze the production rate and downtime of each process. Through these data, the work efficiency of each process can be accurately evaluated, providing a scientific basis for subsequent optimization of production efficiency.

[0095] S502, generating PEI according to the ratio of production rate and downtime, and dynamically adjusting the scheduling sequence of each process in combination with the data in the environmental stability index ESI to achieve optimal production efficiency.

[0096] It is understandable that the production efficiency index PEI is generated by calculating the ratio of production rate to downtime, and the scheduling order of the process is dynamically adjusted in combination with the data in the environmental stability index ESI. The significance of this step is that it can comprehensively consider the changes in production efficiency and environmental factors, making production scheduling more flexible and intelligent. In this way, production efficiency can be improved without affecting environmental stability, and the production process can be ensured to run in the optimal state.

[0097] In a possible implementation, Figure 4 As shown in the figure, PEI dynamically adjusts the priority of production processes in the following ways:

[0098] S801. The priority of each process is adjusted according to its PEI value and the current environmental stability index ESI value. When the PEI value of the process is higher, it is processed first.

[0099] It is understood that the priority of each process is adjusted according to its PEI value and the current ESI value. The purpose of this step is to adjust the priority of the process in combination with production efficiency and environmental stability to achieve a balanced and optimized production process. Processes with higher PEI values ​​are given higher priority, which ensures that efficient processes are given priority, thereby improving overall production efficiency.

[0100] S802. Dynamically adjust the working status of the equipment according to the priority of the production process to ensure the optimal operation of the production process.

[0101] It is understandable that by dynamically adjusting the working status of the equipment according to the priority of each process, the production process can be ensured to proceed smoothly. The key significance of this link is that it helps adjust the operating status of the equipment so that it can be adjusted according to the actual production needs and scheduling priorities, thereby avoiding ineffective equipment idling or resource waste and achieving the optimal operation of the production process.

[0102] In a possible implementation, the ad hoc network protocol is a wireless ad hoc network protocol, including a ZigBee or LoRa protocol, which ensures stable and low-latency data transmission between sensor nodes and enables real-time monitoring and control.

[0103] In a possible implementation, based on the sensor node fault detection, a fault diagnosis and repair mechanism is started, and the faulty node is automatically replaced by a spare node to ensure the continuity and reliability of the sensor data.

[0104] Specifically, the working status of each sensor node is first detected through real-time data monitoring. When the system detects that the data of a sensor node is abnormal or cannot communicate, the fault detection algorithm is started to identify the fault. For example, if the node data is lost or seriously deviated, the system will mark the node as a faulty node and report the fault type and time. Then, the fault diagnosis mechanism analyzes the cause of the problem according to the fault type and selects the corresponding repair strategy. If the fault is a hardware fault, the system will automatically select and activate the backup node for replacement; if it is a communication or algorithm fault, the system will adjust or restore the communication path, algorithm, etc.

[0105] After the backup node is activated, the system will automatically configure the node to ensure that its working parameters are consistent with those of the failed node, including collection methods, communication protocols, etc., to ensure uninterrupted data collection and transmission. The data flow will quickly switch to the backup node to ensure data continuity. At the same time, the failed node is isolated and marked as failed, waiting for subsequent inspection or repair.

[0106] This mechanism ensures the continuity and reliability of sensor data, and minimizes the risk of system downtime and data loss through automated node replacement and fault repair processes.

[0107] In one possible implementation, the environmental control instructions include temperature and humidity control instructions, wind speed control instructions, and gas concentration control instructions. The control instructions are generated based on real-time collected data and calculation results in the evaluation model to ensure that the workshop environment is within the optimization range.

[0108] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0109] (1) In the present invention, by introducing the environmental stability index ESI and the production efficiency index PEI, the method can monitor and adjust the environmental conditions of the chemical fiber production workshop in real time, ensuring that parameters such as temperature, humidity, and gas concentration during the production process are always in the best state. The combination of ESI and PEI can effectively predict and adjust the production process and environmental conditions, thereby optimizing the production process, improving production efficiency, reducing resource waste, and ultimately improving overall production capacity and production benefits;

[0110] (2) In the present invention, self-organizing network sensor technology is used to collect environmental data such as temperature and humidity, gas concentration, wind speed, etc. in real time, and stably transmit them to the central processing unit through wireless protocol. The system can accurately analyze environmental parameters and timely feedback adjustment instructions to ensure that the environment of the production workshop is always in the optimal state. The feedback control mechanism also enables the control strategy to be dynamically adjusted, further improving production efficiency and reducing the impact of manual intervention and environmental fluctuations on production;

[0111] (3) In the present invention, a fault detection and repair mechanism for sensor nodes is provided to ensure the continuity and reliability of production environment monitoring. When a sensor fails, the system will automatically replace the failed node with a spare node to avoid data interruption or environmental loss of control due to equipment failure. This mechanism greatly improves the stability and reliability of the system and ensures that data collection and environmental control in the production process are always uninterrupted.

[0112] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0113] There are a few points to note:

[0114] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0115] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0116] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0117] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for dynamic perception and control of chemical fiber production environment based on self-organizing network technology, characterized in that: include: S1. Arrange multiple self-organizing network sensor nodes in the chemical fiber production workshop, each sensor node includes a temperature sensor, a humidity sensor, a gas concentration sensor, a pressure sensor and an air flow rate sensor; S2, through the ad hoc networking protocol, the sensor nodes form a self-organizing, distributed data transmission network, collecting and transmitting environmental data to the central processing unit in real time; S3, the central processing unit receives the real-time data of all sensor nodes, performs data fusion and processing, and obtains the dynamic change trend of various important indicators in the chemical fiber production environment, including temperature and humidity indicators, gas concentration indicators, pressure indicators and flow rate indicators; S4. Based on the data processing results, an environmental control model is established, wherein the model includes an environmental stability index ESI and a production efficiency index PEI; S5. Generate environmental control instructions according to the control model, wherein the environmental control instructions include temperature and humidity control, wind speed adjustment and gas emission control; S6. Transmitting the environmental control instructions to relevant devices in real time through a self-organizing network protocol for execution, so as to adjust the environment of the chemical fiber production workshop and optimize production conditions; S7. During the regulation process, monitor the regulation effect in real time, and dynamically adjust the regulation strategy through the feedback control mechanism to ensure continuous optimization of the production environment, thereby improving production efficiency and reducing resource waste.

2. According to claim 1, a method for dynamic perception and control of chemical fiber production environment based on self-organizing network technology is characterized in that: include: The calculation formula of the environmental stability index ESI is: Among them, T i is the temperature value measured by the i-th sensor node, H i is the humidity value measured by the i-th sensor node, C i is the gas concentration value measured by the i-th sensor node, P i is the gas pressure value measured by the i-th sensor node, T ref , H ref , C ref , P ref are the corresponding reference values, S j is the wind speed value measured by the jth sensor node, S ref is the reference wind speed value, n is the number of sensor nodes, and m is the number of wind speed sensor nodes.

3. The method for dynamic perception and control of chemical fiber production environment based on ad hoc network technology according to claim 1 is characterized in that: include: The calculation formula of the production efficiency index PEI is: Among them, T k is the completion time of the kth production process, R k is the production rate of the kth process, D k is the downtime proportion of the kth process, and p is the total number of production processes.

4. The method for dynamic perception and control of chemical fiber production environment based on self-organizing network technology according to claim 2 is characterized in that: include: The environmental stability index ESI is further optimized by the following steps: S401, each sensor node calculates the deviation between each environmental parameter and the reference value in real time according to the current temperature and humidity, gas concentration and pressure data, and the calculation result is used to update the ESI value; S402: For a wind speed sensor node, calculate the difference between the current wind speed and the reference wind speed, and perform weighted correction based on the measurement accuracy of the wind speed sensor to obtain a more accurate wind speed difference value; S403. The final ESI value combines the deviations of various environmental parameters from the reference values, as well as the correction value of wind speed, to reflect the stability of the entire production environment.

5. The method for dynamic perception and control of chemical fiber production environment based on ad hoc network technology according to claim 3 is characterized in that: include: The production efficiency index PEI is further optimized by the following steps: S501. For each production process, track the operation status of the process in real time, collect real-time operation data of the equipment in the process through sensors, and calculate the production rate and downtime of each process; S502, generating PEI according to the ratio of production rate and downtime, and dynamically adjusting the scheduling sequence of each process in combination with the data in the environmental stability index ESI to achieve optimal production efficiency.

6. The method for dynamic perception and control of chemical fiber production environment based on ad hoc network technology according to claim 1 is characterized in that: include: The ad hoc network protocol is a wireless ad hoc network protocol, including ZigBee or LoRa protocol, which ensures stable and low-latency data transmission between sensor nodes and can achieve real-time monitoring and control.

7. The method for dynamic perception and control of chemical fiber production environment based on ad hoc network technology according to claim 2 is characterized in that: include: The environmental stability index ESI is further weighted averaged according to the data of temperature, humidity, gas concentration, pressure and wind speed sensor nodes to optimize the error correction in the calculation process, so that the calculation result of ESI is more accurate and can more accurately reflect the changing trend of the production workshop environment.

8. The method for dynamic perception and control of chemical fiber production environment based on self-organizing network technology according to claim 5 is characterized in that: include: The PEI dynamically adjusts the priority of production processes in the following ways: S801, the priority of each process is adjusted according to its PEI value and the current environmental stability index ESI value. When the PEI value of the process is higher, it is processed first; S802. Dynamically adjust the working status of the equipment according to the priority of the production process to ensure the optimal operation of the production process.

9. The method for dynamic perception and control of chemical fiber production environment based on ad hoc network technology according to claim 1 is characterized in that: include: Based on the sensor node fault detection, the fault diagnosis and repair mechanism is started, and the faulty node will be automatically replaced by a spare node to ensure the continuity and reliability of the sensor data.

10. The method for dynamic perception and control of chemical fiber production environment based on ad hoc network technology according to claim 1, characterized in that: include: The environmental control instructions include temperature and humidity control instructions, wind speed control instructions and gas concentration control instructions. The control instructions are generated based on real-time collected data and calculation results in the evaluation model to ensure that the workshop environment is within the optimization range.

Images