A method, device and medium for predicting the environment of a control room of a rubber production equipment

Abstract

The embodiment of the present application discloses a method, device and medium for predicting the environment of the control room of a rubber production equipment, which belongs to the field of data processing technology and solves the problem that the environmental adjustment of the control room of the rubber production equipment is lagging, thus affecting the stability and reliability of the system. The actual power value and the actual environmental parameters corresponding to each area to be tested are obtained; based on the actual power value, the actual environmental parameters and the equipment operation data corresponding to each area to be tested, the first reference environmental data corresponding to each area to be tested is output through a preset prediction model group; based on the first reference environmental data, the environmental distribution simulation of the control room of the rubber production equipment to be tested is performed to obtain the environmental distribution map of the control room of the rubber production equipment to be tested; based on the environmental distribution map of the control room of the rubber production equipment to be tested, the environmental linkage value between each area to be tested is determined; based on the environmental linkage value, the first reference environmental data is adjusted to obtain the second reference environmental data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and medium for predicting the environment of a control room of a rubber production equipment. Background Art

[0002] In today's era of deep integration of industrialization and informatization, the rubber product production line, as a key link supporting the operation of the manufacturing industry, has seen an increasing level of automation and informatization in its production process. Among them, the control room, as the core hub for information flow and command issuance, is of great importance. As the brain of the production line, the stable operation of the control room is directly related to all aspects of rubber product production efficiency, quality control and production safety, and is the cornerstone for ensuring production continuity, data accuracy and process stability.

[0003] In traditional technology, the rubber industry mainly focuses on improving the performance of raw materials, optimizing production processes and enhancing product durability, striving to promote product quality improvement and cost reduction through technological innovation. However, in a highly automated production environment, the stability and safety of the control room environmental facilities have also become important factors in ensuring smooth production. At present, the control room of the rubber industry can only take countermeasures for regulation after environmental parameters such as temperature, humidity, dust concentration, etc. deviate seriously from the reasonable range and trigger the alarm system. This control method of the control room of rubber production equipment is not only difficult to effectively prevent potential damage to production equipment caused by environmental factors, but may also aggravate the operating risks of the equipment due to untimely regulation, thereby affecting the stability and production efficiency of the entire production line. Summary of the invention

[0004] The embodiments of the present application provide a method, device and medium for predicting the environment of a control room of a rubber production equipment, which are used to solve the following technical problems: Currently, the control room of a rubber production equipment can only passively take control measures after the environmental parameters deviate from the preset threshold and an alarm signal is issued. This is not only difficult to effectively curb the potential damage to the equipment caused by environmental deterioration, but may also aggravate the operating risk of the equipment due to delayed control, thereby affecting the stability and reliability of the entire information system.

[0005] The present application embodiment adopts the following technical solutions:

[0006] The embodiment of the present application provides a method for predicting the environment of a control room of a rubber production equipment. The method comprises: obtaining the actual power values ​​corresponding to each area to be tested of the control room of the rubber production equipment to be tested, and the actual environmental parameters corresponding to each area to be tested; based on the actual power values, the actual environmental parameters and the equipment operation data corresponding to each area to be tested, outputting the first reference environmental data corresponding to each area to be tested through a preset prediction model group; based on the first reference environmental data, performing environmental distribution simulation on the control room of the rubber production equipment to be tested, and obtaining an environmental distribution map of the control room of the rubber production equipment to be tested; based on the environmental distribution map of the control room of the rubber production equipment to be tested, obtaining multiple linkage correlations corresponding to each area to be tested, and weighting the multiple linkage correlations to determine the environmental linkage values ​​between each area to be tested; adjusting the first reference environmental data based on the environmental linkage value to obtain the second reference environmental data, and performing environmental alarm processing on the control room of the rubber production equipment to be tested based on the second reference environmental data.

[0007] The embodiment of the present application uses a preset model group to predict the environmental change trend of the rubber production equipment control room in the future based on historical data and real-time data, discover problems in advance, and improve the accuracy and comprehensiveness of the prediction. By constructing an environmental distribution map, the environmental conditions of each area in the rubber production equipment control room are displayed in an intuitive way. By analyzing the environmental linkage relationship between each area, the areas that affect each other are identified, which helps to more comprehensively clarify the complexity of the rubber production equipment control room environment. Through the second reference environmental data and the adjustment of the first reference environmental data, the prediction results are dynamically adjusted according to the actual situation of environmental changes to ensure the accuracy and timeliness of the alarm information. Through early warning and intervention, potential environmental problems can be discovered in time, equipment damage and downtime can be reduced, and the stable operation of the rubber production equipment control room can be guaranteed.

[0008] In one implementation of the present application, based on the actual power value, the actual environmental parameters and the equipment operation data corresponding to each area to be tested, the first reference environmental data corresponding to each area to be tested is output through a preset prediction model group, specifically including: based on the historical operation data in the equipment operation data, determining the historical power value and the historical environmental parameters corresponding to each area to be tested; inputting the historical power value and the actual power value into the preset equipment operation status prediction model, so as to output the first predicted equipment operation information corresponding to the area to be tested through the preset equipment operation status prediction model; wherein the first predicted equipment operation information includes at least one of the equipment heat dissipation, the equipment humidity and the equipment cooling system efficiency; integrating the first predicted equipment operation information with the actual environmental parameters to obtain reference predicted environmental information; based on the future task operation data in the equipment operation data, determining the future power value corresponding to each area to be tested; comparing the future power value with the actual power value to determine the power difference corresponding to each preset time period; inputting the power difference corresponding to each preset time period, the actual power value and the reference predicted environmental information into the preset environmental information prediction model, so as to output the first reference environmental data through the preset environmental information prediction model.

[0009] In one implementation of the present application, based on the first reference environmental data, an environmental distribution simulation is performed on the control room of the rubber production equipment to be tested to obtain an environmental distribution map of the control room of the rubber production equipment to be tested, specifically including: based on the first reference environmental data, determining the environmental data corresponding to each area to be tested; wherein the environmental data at least includes regional temperature, regional humidity and regional air flow information; based on the air flow information, determining the linkage relationship between each area to be tested, and associating and connecting the areas to be tested that have a linkage relationship; determining multiple area nodes through each area to be tested, and connecting each area node based on the associative connection; based on the environmental data corresponding to the multiple area nodes, adjusting the morphology of the multiple area nodes, and displaying the data to obtain the environmental distribution map of the control room of the rubber production equipment to be tested.

[0010] In one implementation of the present application, a plurality of linkage correlation degrees corresponding to each tested area are obtained based on the environmental distribution map of the control room of the rubber production equipment to be tested, and a plurality of linkage correlation degrees are weighted to determine the environmental linkage value between each tested area, specifically including: determining a tested area group with environmental linkage based on the node connection relationship in the environmental distribution map of the control room of the rubber production equipment to be tested; determining the distance between different tested areas in each tested area group, and determining the first linkage degree corresponding to each tested area group based on the distance; when the tested area group is an adjacent area, determining the first linkage degree corresponding to each tested area group based on the air flow velocity and the air flow velocity in the air flow information; determining the first linkage degree corresponding to each tested area group based on the distance; determining the first linkage degree corresponding to each tested area group based on the air flow velocity and the air flow velocity in the air flow information ... The second linkage degree corresponding to each group of tested areas is determined according to the movement direction; when the group of tested areas is non-adjacent, the weight factor is determined based on the number of interval areas and the interval distance corresponding to the group of tested areas, and the second linkage degree corresponding to each group of tested areas is determined based on the weight factor and the air flow speed and the air flow direction in the air flow information; the third linkage degree corresponding to each group of tested areas is obtained based on the first linkage degree and the second linkage degree; the reference group of tested areas corresponding to the current area to be tested is determined, and the third linkage degrees corresponding to each reference group of tested areas are integrated to obtain the environmental linkage value corresponding to the current area to be tested.

[0011] In one implementation of the present application, the first reference environmental data is adjusted based on the environmental linkage value to obtain the second reference environmental data, specifically including: generating an environmental parameter matrix from the first reference environmental data according to each environmental sensing device in the measured area; and determining the environmental linkage type corresponding to the environmental linkage value; determining the reliability corresponding to each environmental linkage type based on a historical environmental linkage type reliability reference table; matching the environmental linkage type with the environmental sensing device to determine the parameters to be adjusted in the environmental parameter matrix; integrating the reliability, environmental linkage value and the parameters to be adjusted to obtain the second reference environmental data.

[0012] In one implementation of the present application, before outputting the first reference environmental data corresponding to each area to be tested through a preset prediction model group, the method also includes: taking a preset sample data set as input, and taking an environmental information sample data set and an equipment operation status data set corresponding to the preset sample data set as output, to train the preset neural network model group; based on a preset loss function, determining the difference between the input sample data and the output data, and adding the preset loss function to obtain a difference sum function between all sample data and actual data; determining the minimum value of the difference sum function through a gradient descent method, and determining a reference prediction model group based on the minimum value; obtaining the estimated values ​​corresponding to the reference prediction model group under different parameter conditions through a maximum likelihood method; wherein the different parameters include at least power value, indoor temperature value, outdoor temperature value, door and window opening degree, humidity value, air flow data, equipment operation time and equipment failure data; sorting the estimated values ​​corresponding to the different parameters, and screening the different parameters based on the sorting, so as to optimize the reference prediction model group based on the screened different parameters to obtain the preset prediction model group.

[0013] In one implementation of the present application, after obtaining the second reference environmental data, the method also includes: determining a prediction time period corresponding to the second reference environmental data, and dividing the prediction time period into multiple prediction segments; constructing an environmental transformation coordinate system based on the environmental information type corresponding to the second reference environmental data; wherein each horizontal axis of the environmental transformation coordinate system corresponds to a different environmental information type; determining multiple prediction segments in the environmental transformation coordinate system, and marking the prediction environmental data corresponding to the multiple prediction segments; determining a first difference between adjacent prediction environmental data, and comparing each prediction environmental data with a corresponding environmental data type threshold to determine a second difference; comparing the first difference with a preset adjacent data difference threshold to determine a first number of first differences greater than the preset adjacent data difference threshold; comparing the second difference with a preset environmental data difference threshold to determine a second number greater than the preset environmental data difference threshold; and determining the environmental stability state of the control room of the rubber production equipment to be tested based on the ratio corresponding to the first number and the second number.

[0014] In one implementation of the present application, before obtaining the actual power values ​​corresponding to each area to be tested of the control room of the rubber production equipment to be tested, the method also includes: obtaining an equipment layout diagram corresponding to the control room of the rubber production equipment to be tested, and performing an initial division of the control room of the rubber production equipment to be tested based on the corresponding physical obstacles in the equipment layout diagram; determining the equipment density based on the equipment position and equipment size; performing a secondary division of the control room of the rubber production equipment to be tested based on the equipment density to obtain multiple areas to be tested; monitoring the equipment information of the control room of the rubber production equipment to be tested in real time, and dynamically adjusting each divided area to be tested.

[0015] The embodiment of the present application provides an environment prediction device for a control room of a rubber production equipment, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain actual power values ​​corresponding to each area to be tested of the control room of the rubber production equipment to be tested, and actual environmental parameters corresponding to each area to be tested; based on the actual power values, actual environmental parameters and equipment operation data corresponding to each area to be tested, output first reference environmental data corresponding to each area to be tested through a preset prediction model group; based on the first reference environmental data, perform environmental distribution simulation on the control room of the rubber production equipment to be tested, and obtain an environmental distribution map of the control room of the rubber production equipment to be tested; based on the environmental distribution map of the control room of the rubber production equipment to be tested, obtain multiple linkage correlations corresponding to each area to be tested, and weight the multiple linkage correlations to determine the environmental linkage value between each area to be tested; based on the environmental linkage value, adjust the first reference environmental data to obtain second reference environmental data, and perform environmental alarm processing on the control room of the rubber production equipment to be tested based on the second reference environmental data.

[0016] A non-volatile computer storage medium provided by an embodiment of the present application stores computer executable instructions, wherein the computer executable instructions are configured to: obtain actual power values ​​corresponding to each tested area of ​​a control room of a rubber production equipment to be tested, and actual environmental parameters corresponding to each tested area; output first reference environmental data corresponding to each tested area through a preset prediction model group based on the actual power values, the actual environmental parameters, and the equipment operation data corresponding to each tested area; perform environmental distribution simulation on the control room of the rubber production equipment to be tested based on the first reference environmental data to obtain an environmental distribution map of the control room of the rubber production equipment to be tested; obtain multiple linkage correlations corresponding to each tested area based on the environmental distribution map of the control room of the rubber production equipment to be tested, and weight the multiple linkage correlations to determine an environmental linkage value between each tested area; adjust the first reference environmental data based on the environmental linkage value to obtain second reference environmental data, and perform environmental alarm processing on the control room of the rubber production equipment to be tested based on the second reference environmental data.

[0017] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: The embodiments of the present application predict the environmental change trend of the control room of the rubber production equipment in the future period of time based on historical data and real-time data through a preset model group, discover problems in advance and improve the accuracy and comprehensiveness of the prediction. By constructing an environmental distribution map, the environmental conditions of each area in the control room of the rubber production equipment are displayed in an intuitive way. By analyzing the environmental linkage relationship between each area, the areas that affect each other are identified, which helps to more comprehensively clarify the complexity of the environment of the control room of the rubber production equipment. Through the second reference environmental data and the adjustment of the first reference environmental data, the prediction results are dynamically adjusted according to the actual situation of the environmental changes to ensure the accuracy and timeliness of the alarm information. Through early warning and intervention, potential environmental problems can be discovered in time, equipment damage and downtime can be reduced, and the stable operation of the control room of the rubber production equipment can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0019] Figure 1 A flow chart of a method for predicting the environment of a control room of a rubber production equipment provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the structure of a control room environment prediction device for rubber production equipment provided in an embodiment of the present application.

[0021] Reference numerals:

[0022] 200: Rubber production equipment control room environment prediction equipment, 201: Processor, 202: Memory. DETAILED DESCRIPTION

[0023] The embodiments of the present application provide a method, device and medium for predicting the environment of a control room of a rubber production equipment.

[0024] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0025] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0026] Figure 1 A flow chart of a method for predicting the environment of a control room of a rubber production equipment provided in an embodiment of the present application is as follows: Figure 1 As shown, the method for predicting the environment of the control room of the rubber production equipment includes the following steps:

[0027] Step 101: Acquire actual power values ​​corresponding to each tested area of ​​the control room of the tested rubber production equipment, and actual environmental parameters corresponding to each tested area.

[0028] In one embodiment of the present application, an equipment layout diagram corresponding to the control room of the rubber production equipment to be tested is obtained, and the control room of the rubber production equipment to be tested is initially divided based on the corresponding physical obstacles in the equipment layout diagram. The equipment density is determined based on the equipment location and equipment size. The control room of the rubber production equipment to be tested is secondary divided based on the equipment density to obtain multiple areas to be tested. The equipment information of the control room of the rubber production equipment to be tested is monitored in real time, and each divided area to be tested is dynamically adjusted.

[0029] Specifically, obtain the equipment layout diagram of the control room of the rubber production equipment to be tested, which marks the location and size of all equipment in the control room of the rubber production equipment, as well as the physical obstacles in the control room of the rubber production equipment, such as walls, pillars, cable trays, etc. Based on the location of these physical obstacles, the control room of the rubber production equipment can be preliminarily divided into several larger areas, which will serve as the basis for subsequent divisions.

[0030] Furthermore, based on the initial division, according to the location and size of the equipment in the rubber production equipment control room, the ratio of the total floor space of the equipment in each area to the area of ​​the area is calculated to determine the equipment density of each area. According to the equipment density of each area, the space of the rubber production equipment control room is further divided. For example, high-density areas can perform more sophisticated temperature control and airflow management, while low-density areas can perform scalability management. Through secondary division, multiple areas to be tested with different characteristics and requirements can be obtained. According to the characteristics of different areas, the corresponding equipment operating environment reference value is set for each area to be tested.

[0031] Furthermore, through pre-installed sensors and monitoring systems, the operating status and environmental parameters of each device in the rubber production equipment control room, such as temperature, humidity, air flow rate, etc., are obtained in real time. According to the actual operating conditions and environmental changes of the rubber production equipment control room, the division and resource allocation of each area to be tested are dynamically adjusted. The dynamic adjustment in the embodiment of the present application helps to optimize the operating efficiency of the rubber production equipment control room, reduce energy consumption, and improve the reliability and stability of the equipment. For example, if the number of equipment in a low-density area is increased, it is recommended to adjust the reference value of the equipment operating environment in the area. As the number of equipment increases, the heat dissipation will increase, so it is recommended to lower the reference value of the ambient temperature in the area.

[0032] Step 102: Based on the actual power value, the actual environmental parameters and the equipment operation data corresponding to each area to be tested, the first reference environmental data corresponding to each area to be tested is output through a preset prediction model group.

[0033] In one embodiment of the present application, a preset sample data set is used as input, and an environmental information sample data set and an equipment operation status data set corresponding to the preset sample data set are used as outputs to train a preset neural network model group. Based on a preset loss function, the difference between the input sample data and the output data is determined, and the preset loss function is added to obtain a difference sum function between all sample data and actual data. The minimum value of the difference sum function is determined by the gradient descent method, and a reference prediction model group is determined based on the minimum value. By the maximum likelihood method, the estimated values ​​corresponding to the reference prediction model group under different parameter conditions are obtained; wherein the different parameters at least include power value, indoor temperature value, outdoor temperature value, door and window opening degree, humidity value, air flow data, equipment operation time and equipment failure data. The estimated values ​​corresponding to different parameters are sorted, and different parameters are screened based on the sorting, so as to optimize the reference prediction model group based on the screened different parameters to obtain a preset prediction model group.

[0034] Specifically, first, preset sample data sets are used as inputs of the neural network. These data sets contain historical operating data of the control room of the rubber production equipment, such as power consumption, temperature, humidity, etc. At the same time, the corresponding environmental information sample data sets and equipment operating status data sets are used as outputs. These data sets are the environmental status and equipment status that the neural network model group is expected to predict. The preset neural network model group is trained using these sample data sets. Among them, the neural network model group in the embodiment of the present application includes an equipment operating status prediction model and an environmental information prediction model. During the training process, the neural network will learn the mapping relationship between input samples and output samples.

[0035] Furthermore, in order to evaluate the prediction performance of the neural network, the embodiment of the present application sets a preset loss function, which is used to calculate the difference between the predicted output and the actual output. For each sample in the training set, the corresponding loss value is calculated, and the sum of the loss functions is obtained by adding the errors of all samples, that is, the objective function of optimizing the neural network is obtained. The weights and biases of the neural network are adjusted using the gradient descent method to minimize the sum of the loss functions, that is, the difference sum function is minimized using the gradient descent method. The loss value is gradually reduced by calculating the gradient of the loss function with respect to the neural network parameters and updating the parameters in the opposite direction of the gradient. This process is iterated until a preset stop condition is reached, such as the loss value is less than a certain threshold or the number of iterations reaches an upper limit. When the loss function reaches the minimum value, the obtained neural network model group is the reference prediction model group, which can accurately predict the environmental information and equipment status information of the control room of the rubber production equipment to a certain extent.

[0036] Furthermore, the maximum likelihood method is used to evaluate the performance of the reference prediction model group under different parameters, such as power value, indoor and outdoor temperature, humidity, etc. By adjusting these parameters, the changes in the model prediction results are observed to obtain the estimated values ​​under different parameters. According to the results of the maximum likelihood estimation, the estimated values ​​corresponding to different parameters are sorted. Based on the sorting results, the key parameters with a greater impact on the prediction results and the non-key parameters with a smaller impact can be identified. Then, according to actual needs and application scenarios, these parameters are screened, and only the key parameters are retained for subsequent model optimization. Based on the different parameters after screening, the reference prediction model group is optimized to further improve the prediction accuracy and generalization ability of the model. The optimized model is the preset prediction model group.

[0037] In one embodiment of the present application, based on the historical operation data in the equipment operation data, the historical power values ​​and historical environmental parameters corresponding to each area to be tested are determined. The historical power values ​​and the actual power values ​​are input into a preset equipment operation status prediction model to output the first predicted equipment operation information corresponding to the area to be tested through the preset equipment operation status prediction model; wherein the first predicted equipment operation information includes at least one of the equipment heat dissipation, the equipment humidity, and the equipment cooling system efficiency. The first predicted equipment operation information is integrated with the actual environmental parameters to obtain reference predicted environmental information. Based on the future task operation data in the equipment operation data, the future power values ​​corresponding to each area to be tested are determined. The future power value is compared with the actual power value to determine the power difference corresponding to each preset time period. The power difference, the actual power value, and the reference predicted environmental information corresponding to each preset time period are input into the preset environmental information prediction model to output the first reference environmental data through the preset environmental information prediction model.

[0038] Specifically, historical operation data is extracted from the equipment operation data. These data record the operation status of each area to be tested in the past period of time. For each area to be tested, its corresponding historical power value and historical environmental parameters, such as temperature and humidity, need to be determined. Similarly, future task operation data is obtained from the equipment operation data. These data describe the expected workload of each area to be tested in the future period of time, so that future power requirements can be inferred.

[0039] Furthermore, the historical power value and the current actual power value are input into the preset device operation status prediction model. Through model calculation, the first predicted device operation information corresponding to the tested area is output. Such information includes but is not limited to the reference power value, device heat dissipation, device humidity, and device cooling system efficiency, etc., reflecting the operation status of the device under a given power.

[0040] Furthermore, the first predicted device operation information is integrated with actual environmental parameters, such as current indoor temperature and humidity, to obtain reference predicted environmental information. For example, if the humidity in the current area is high, the device humidity in the first predicted device operation information is numerically increased based on the humidity in the current area. By considering the equipment operating status and current environmental conditions, a preliminary prediction of future environmental changes is formed. Based on future task operation data, the future power values ​​corresponding to each area to be tested in each preset time period in the future are determined. The future power value in each preset time period is compared with the current actual power, and the power difference in the time period is calculated. The difference reflects the change in future power demand relative to the current state.

[0041] Furthermore, the power difference corresponding to each preset time period, the current actual power, and the generated reference predicted environmental information are input into the preset environmental information prediction model. The environmental information prediction model uses these input information, combined with the rules and relationships learned within the model, to predict the first reference environmental data, that is, predict the environmental information such as indoor temperature and humidity in each future time period.

[0042] Step 103: Based on the first reference environmental data, perform environmental distribution simulation on the control room of the rubber production equipment to be tested, and obtain an environmental distribution map of the control room of the rubber production equipment to be tested.

[0043] In one embodiment of the present application, based on the first reference environmental data, the environmental data corresponding to each area to be tested is determined; wherein the environmental data includes at least regional temperature, regional humidity and regional air flow information. Based on the air flow information, the linkage relationship between each area to be tested is determined, and the areas to be tested with linkage relationships are associated and connected. Multiple regional nodes are determined through each area to be tested, and the regional nodes are connected based on the associated connection. Based on the environmental data corresponding to the multiple regional nodes, the multiple regional nodes are morphologically adjusted, and data is displayed to obtain an environmental distribution map of the control room of the rubber production equipment to be tested.

[0044] Specifically, based on the first reference environmental data, the corresponding environmental data is determined for each area in the control room of the rubber production equipment to be tested. These data include at least regional temperature, regional humidity, and regional air flow information. Assuming that the control room of the rubber production equipment to be tested is divided into three areas A, B, and C, it is known from the first reference environmental data that the temperature of area A is 25°C, the humidity is 50%, and the air flow speed is 0.5m / s; the temperature of area B is 24°C, the humidity is 55%, and the air flow speed is 0.3m / s; the temperature of area C is 26°C, the humidity is 48%, and the air flow speed is 0.6m / s.

[0045] Furthermore, the linkage relationship between them is determined based on the air flow information of each area. The linkage relationship in the embodiment of the present application refers to the relationship of mutual influence between different areas to be tested due to air flow. The areas to be tested with linkage relationships are associated and connected. For example, the air flow direction in area A points to area B, and the temperature in area B is higher than that in area A. It can be inferred that area B may be affected by the cold air in area A, forming a certain linkage relationship. Similarly, if the air flow speed in area C is faster and the direction may affect area A or area B, then area C also has a linkage relationship with area A or area B.

[0046] Furthermore, each area to be tested is regarded as a regional node, and based on the previously determined inter-regional linkage relationship, these regional nodes are connected to represent the mutual influence relationship between them. According to the environmental data corresponding to each regional node, such as temperature, humidity, air flow information, etc., the shape of the regional node is adjusted. For example, color depth, size changes or graphic textures can be used to intuitively represent the high and low temperature, humidity changes, and the speed of air flow. By adding legends, labels, annotations and other elements to display data, it is ensured that the environmental distribution map of the rubber production equipment control room can clearly convey the environmental conditions of each area and their relationship.

[0047] Step 104 , based on the control room environment distribution map of the rubber production equipment to be tested, a plurality of linkage correlation degrees corresponding to each area to be tested are obtained, and the plurality of linkage correlation degrees are weighted and combined to determine the environmental linkage value between each area to be tested.

[0048] In one embodiment of the present application, based on the node connection relationship in the environmental distribution map of the control room of the rubber production equipment to be tested, the group of tested areas with environmental linkage is determined. The distance between different tested areas in each tested area group is determined, and the first linkage degree corresponding to each tested area group is determined based on the distance. In the case where the tested area group is an adjacent area, the second linkage degree corresponding to each tested area group is determined based on the air flow speed and air flow direction in the air flow information. In the case where the tested area group is a non-adjacent area, the weight factor is determined based on the number of interval areas and the interval distance corresponding to the tested area group, and the second linkage degree corresponding to each tested area group is determined based on the weight factor, the air flow speed and the air flow direction in the air flow information. Based on the first linkage degree and the second linkage degree, the third linkage degree corresponding to each tested area is obtained. The reference tested area group corresponding to the current tested area is determined, and the third linkage degrees corresponding to each reference tested area group are integrated to obtain the environmental linkage value corresponding to the current tested area.

[0049] Specifically, based on the node connection relationship in the environmental distribution map of the control room of the rubber production equipment to be tested, the group of areas to be tested that affect each other through air flow, heat transfer or other environmental factors are identified. There is a significant environmental linkage between the members in these area groups. For each area group to be tested, the distance between different areas to be tested in the group is first calculated. These distances are effective distances based on the airflow path. Based on these distances, the first linkage degree of each area group to be tested is determined. The first linkage degree mainly reflects the direct influence between areas based on distance. For example, the farther the distance, the smaller the first linkage degree. Assuming that the distance between area A and area B is close and the areas of the two are similar, the first linkage degree between them may be high. Although area C is connected to area A and area B, its first linkage degree may be relatively low due to the long distance or the presence of obstacles.

[0050] Furthermore, for adjacent groups of areas to be tested, the air flow speed and direction in the air flow information are used to further determine the second degree of linkage. The faster the air flow and the more direct the direction, the higher the degree of linkage between areas. Specifically, a coordinate system is constructed with the current area to be tested as the origin, and the air flow direction is determined in the coordinate system. Secondly, the position direction of other areas in the coordinate system is determined. The smaller the angle between the air flow direction and the position direction, the higher the degree of linkage between areas. For non-adjacent groups of areas to be tested, the number and distance of the interval areas between them need to be considered. These interval areas will weaken the linkage effect that may have existed, and the weight factor is determined based on the number and distance of the interval areas. For example, the more the number of interval areas and the longer the interval distance, the lower the weight.

[0051] Further, the first linkage degree and the second linkage degree are combined to obtain the third linkage degree corresponding to each area to be tested, for example, this can be achieved by an overweighted or merging algorithm. For the current area to be tested, its reference area group to be tested is determined. These reference area groups to be tested are all area groups that have direct or indirect environmental linkage with the current area. The third linkage degrees generated by each reference area group to be tested for the current area are integrated, such as summing and integrating, to obtain the environmental linkage value corresponding to the current area to be tested.

[0052] Step 105: adjusting the first reference environmental data based on the environmental linkage value to obtain second reference environmental data, and performing environmental alarm processing on the control room of the rubber production equipment to be tested based on the second reference environmental data.

[0053] In one embodiment of the present application, the first reference environment data is used to generate an environment parameter matrix according to each environment perception device in the area to be tested. And, the environment linkage type corresponding to the environment linkage value is determined. Based on the historical environment linkage type reliability reference table, the reliability corresponding to each environment linkage type is determined. The environment linkage type is matched with the environment perception device to determine the parameters to be adjusted in the environment parameter matrix. The reliability, environment linkage value and the parameters to be adjusted are integrated to obtain the second reference environment data.

[0054] Specifically, the environmental information collected by various environmental sensing devices in the test area, such as temperature sensors, humidity sensors, airflow velocity meters, etc., is first organized and converted into an environmental parameter matrix. Each row of the environmental parameter matrix represents an environmental sensing device, and each column represents a different environmental parameter, such as temperature, humidity, airflow velocity, etc.

[0055] Based on the calculated environmental linkage value, the corresponding type of the environmental linkage value is determined. These types include but are not limited to temperature linkage, humidity linkage, airflow linkage, etc. The embodiment of the present application is provided with a historical environmental linkage type reliability reference table, which records the reliability of different environmental linkage types when they occurred in the past. According to the currently determined environmental linkage type, the corresponding reliability is searched in the table.

[0056] Further, the determined environment linkage type is matched with each environment sensing device. Based on the matching result, the parameters to be adjusted that are directly related to the current environment linkage type are identified in the environment parameter matrix. The reliability of the environment linkage type, the environment linkage value, and the parameters to be adjusted are integrated to form the second reference environment data.

[0057] In one embodiment of the present application, a prediction time period corresponding to the second reference environmental data is determined, and the prediction time period is divided into a plurality of prediction segments. Based on the environmental information type corresponding to the second reference environmental data, an environmental transformation coordinate system is constructed; wherein each horizontal axis of the environmental transformation coordinate system corresponds to a different environmental information type. A plurality of prediction segments are determined in the environmental transformation coordinate system, and the prediction environmental data corresponding to the plurality of prediction segments are marked. A first difference between adjacent prediction environmental data is determined, and each prediction environmental data is compared with a corresponding environmental data type threshold to determine a second difference. The first difference is compared with a preset adjacent data difference threshold to determine a first quantity of first differences greater than the preset adjacent data difference threshold. The second difference is compared with a preset environmental data difference threshold to determine a second quantity greater than the preset environmental data difference threshold. Based on the ratio corresponding to the first quantity and the second quantity, the environmental stability state of the control room of the rubber production equipment to be tested is determined.

[0058] Specifically, after obtaining the second reference environment data, it is first necessary to determine the prediction time period corresponding to this information. This time period can be based on historical data, current environmental change trends, or user settings. This prediction time period is divided into multiple smaller prediction segments. These segments can be of equal length or of unequal length according to the expected rate of environmental change.

[0059] Furthermore, an environmental transformation coordinate system is constructed based on the environmental information type involved in the second reference environmental data, such as temperature, humidity, air flow velocity, etc. In this coordinate system, multiple horizontal axes are included, and each horizontal axis represents a type of environmental information, and the points on the vertical axis represent specific values ​​of the environmental information type. In the constructed environmental transformation coordinate system, the corresponding predicted environmental data is marked on each predicted segment according to the prediction results.

[0060] Furthermore, the differences of each type of environmental information between adjacent prediction segments are calculated, and these differences reflect the rate of environmental change. The environmental data of each prediction segment is compared with the corresponding environmental data type threshold, and the obtained difference reflects the degree to which the environmental data deviates from the threshold. The first difference is compared with the preset adjacent data difference threshold, and the number of first differences greater than the threshold is counted, that is, the first number. The second difference is compared with the preset environmental data difference threshold, and the number of second differences greater than the threshold is counted, that is, the second number. Based on the ratio of the first number to the second number, the environmental stability state of the control room of the rubber production equipment to be tested is obtained. A high ratio value indicates that the environment changes violently and the stability is poor; a low ratio value indicates that the environment changes gently and the stability is good.

[0061] Furthermore, if the environmental data corresponding to the second reference environmental data does not meet the equipment operating conditions, or the environmental stability is poor, an environmental alarm is issued to process the area to be tested in advance to prevent shutdown processing from occurring.

[0062] Figure 2 This is a schematic diagram of a rubber production equipment control room environment prediction device provided in an embodiment of the present application. Figure 2 As shown, the rubber production equipment control room environment prediction device 200 includes: at least one processor 201; and a memory 202 that is communicatively connected to the at least one processor 201; wherein the memory 202 stores instructions that can be executed by the at least one processor 201, and the instructions are executed by the at least one processor 201 so that the at least one processor 201 can: obtain the actual power values ​​corresponding to each tested area of ​​the control room of the rubber production equipment to be tested, and the actual environmental parameters corresponding to each tested area; based on the actual power values, the actual environmental parameters and the equipment operation data corresponding to each tested area, Through the preset prediction model group, the first reference environmental data corresponding to each area to be tested is output; based on the first reference environmental data, the environmental distribution simulation of the control room of the rubber production equipment to be tested is performed to obtain the environmental distribution map of the control room of the rubber production equipment to be tested; based on the environmental distribution map of the control room of the rubber production equipment to be tested, multiple linkage correlation degrees corresponding to each area to be tested are obtained, and the multiple linkage correlation degrees are weighted to determine the environmental linkage value between each area to be tested; based on the environmental linkage value, the first reference environmental data is adjusted to obtain the second reference environmental data, and based on the second reference environmental data, environmental alarm processing is performed on the control room of the rubber production equipment to be tested.

[0063] The embodiment of the present application also provides a non-volatile computer storage medium, storing computer executable instructions, wherein the computer executable instructions are configured to: obtain actual power values ​​corresponding to each area to be tested of the control room of the rubber production equipment to be tested, and actual environmental parameters corresponding to each area to be tested; based on the actual power values, actual environmental parameters and equipment operation data corresponding to each area to be tested, output first reference environmental data corresponding to each area to be tested through a preset prediction model group; based on the first reference environmental data, perform environmental distribution simulation on the control room of the rubber production equipment to be tested, and obtain an environmental distribution map of the control room of the rubber production equipment to be tested; based on the environmental distribution map of the control room of the rubber production equipment to be tested, obtain multiple linkage correlations corresponding to each area to be tested, and weight the multiple linkage correlations to determine the environmental linkage value between each area to be tested; adjust the first reference environmental data based on the environmental linkage value to obtain second reference environmental data, and perform environmental alarm processing on the control room of the rubber production equipment to be tested based on the second reference environmental data.

[0064] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0065] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and changes. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for predicting the environment of a control room of a rubber production equipment, characterized in that: The method comprises: Obtaining actual power values ​​corresponding to each area to be tested in the control room of the rubber production equipment to be tested, and actual environmental parameters corresponding to each area to be tested; Based on the actual power value, the actual environmental parameter and the equipment operation data corresponding to each of the areas to be tested, outputting the first reference environmental data corresponding to each of the areas to be tested through a preset prediction model group; Based on the first reference environmental data, performing environmental distribution simulation on the control room of the rubber production equipment to be tested to obtain an environmental distribution map of the control room of the rubber production equipment to be tested; Based on the control room environment distribution map of the rubber production equipment to be tested, a plurality of linkage correlation degrees corresponding to each of the areas to be tested are obtained, and a plurality of linkage correlation degrees are weighted and combined to determine the environmental linkage value between the areas to be tested; The first reference environmental data is adjusted based on the environmental linkage value to obtain second reference environmental data, and environmental alarm processing is performed on the control room of the rubber production equipment to be tested based on the second reference environmental data; The outputting of first reference environment data corresponding to each of the areas to be tested respectively based on the actual power value, the actual environment parameter and the equipment operation data corresponding to each of the areas to be tested respectively through a preset prediction model group specifically includes: Based on the historical operation data in the equipment operation data, determining the historical power value and the historical environmental parameter corresponding to each of the areas to be tested; Input the historical power value and the actual power value into a preset equipment operation status prediction model, so as to output first predicted equipment operation information corresponding to the tested area through the preset equipment operation status prediction model; wherein the first predicted equipment operation information includes at least one of equipment heat dissipation, equipment humidity, and equipment cooling system efficiency; Integrating the first predicted device operation information with the actual environmental parameters to obtain reference predicted environmental information; Determine the future power values ​​corresponding to the areas to be tested based on the future task operation data in the equipment operation data; Comparing the future power value with the actual power value to determine the power difference corresponding to each preset time period; The power difference values, the actual power values ​​and the reference predicted environmental information respectively corresponding to the preset time periods are input into a preset environmental information prediction model, so as to output the first reference environmental data through the preset environmental information prediction model.

2. A method for predicting the control room environment of a rubber production equipment according to claim 1, characterized in that: The step of performing environmental distribution simulation on the control room of the rubber production equipment to be tested based on the first reference environmental data to obtain an environmental distribution map of the control room of the rubber production equipment to be tested specifically includes: Based on the first reference environmental data, determining the environmental data corresponding to each of the test areas; wherein the environmental data at least includes regional temperature, regional humidity and regional air flow information; Based on the air flow information, determining the linkage relationship between the areas to be tested, and associating and connecting the areas to be tested that have the linkage relationship; Determine a plurality of regional nodes through each of the regions to be tested, and connect each of the regional nodes based on associated connections; Based on the environmental data respectively corresponding to the multiple regional nodes, the multiple regional nodes are morphologically adjusted and data is displayed to obtain an environmental distribution map of the control room of the rubber production equipment to be tested.

3. A method for predicting the environment of a control room of a rubber production equipment according to claim 2, characterized in that: The step of obtaining a plurality of linkage correlations corresponding to each of the tested areas based on the control room environment distribution map of the tested rubber production equipment, and weighting and combining the plurality of linkage correlations to determine the environmental linkage value between the tested areas specifically includes: Based on the node connection relationship in the control room environment distribution map of the rubber production equipment to be tested, determining the area group to be tested with environmental linkage; Determine the distance between different areas to be tested in each of the areas to be tested groups, and determine the first linkage degrees corresponding to each of the areas to be tested groups based on the distance; In the case where the to-be-detected area groups are adjacent areas, determining the second linkage degrees corresponding to the to-be-detected area groups respectively based on the air flow speed and the air flow direction in the air flow information; In the case where the area groups to be measured are non-adjacent areas, a weight factor is determined based on the number and distance of the interval areas corresponding to the area groups to be measured, and a second linkage degree corresponding to each of the area groups to be measured is determined based on the weight factor, the air flow speed and the air flow direction in the air flow information; Based on the first linkage degree and the second linkage degree, obtaining a third linkage degree corresponding to each of the to-be-tested areas; A reference to-be-tested area group corresponding to the current to-be-tested area is determined, and the third linkage degrees corresponding to each of the reference to-be-tested area groups are integrated to obtain the environmental linkage value corresponding to the current to-be-tested area.

4. The method for predicting the environment of a control room of a rubber production equipment according to claim 1, characterized in that: The adjusting the first reference environment data based on the environment linkage value to obtain second reference environment data specifically includes: According to each environmental sensing device in the area to be tested, generating an environmental parameter matrix from the first reference environmental data; and, determining an environment linkage type corresponding to the environment linkage value; Based on the historical environmental linkage type reliability reference table, determine the reliability corresponding to each of the environmental linkage types; Matching the environment linkage type with the environment perception device to determine the parameters to be adjusted in the environment parameter matrix; The reliability, the environmental linkage value and the parameter to be adjusted are integrated to obtain the second reference environmental data.

5. The method for predicting the environment of a control room of a rubber production equipment according to claim 1, characterized in that: Before outputting the first reference environment data corresponding to each of the areas to be tested respectively through the preset prediction model group, the method further includes: Taking a preset sample data set as input, and taking an environmental information sample data set and a device operation status data set corresponding to the preset sample data set as output, training a preset neural network model group; Based on a preset loss function, determining the difference between the input sample data and the output data, and performing an addition process on the preset loss function to obtain a sum function of the difference between all sample data and the actual data; Determine the minimum value of the sum of difference function by gradient descent method, and determine the reference prediction model group based on the minimum value; By using the maximum likelihood method, the estimated values ​​corresponding to the reference prediction model group under different parameter conditions are obtained; wherein the different parameters at least include power value, indoor temperature value, outdoor temperature value, door and window opening degree, humidity value, air flow data, equipment operation time and equipment failure data; The estimated values ​​corresponding to the different parameters are sorted, and the different parameters are screened based on the sorting, so as to optimize the reference prediction model group based on the screened different parameters to obtain the preset prediction model group.

6. The method for predicting the environment of a control room of a rubber production equipment according to claim 1, characterized in that: After obtaining the second reference environment data, the method further includes: Determine a prediction time period corresponding to the second reference environment data, and divide the prediction time period into a plurality of prediction segments; Based on the environment information type corresponding to the second reference environment data, construct an environment transformation coordinate system; wherein each horizontal axis of the environment transformation coordinate system corresponds to a different environment information type; Determining the plurality of prediction segments in the environmental transformation coordinate system, and marking the prediction environmental data respectively corresponding to the plurality of prediction segments; Determine a first difference between adjacent predicted environmental data, and compare each predicted environmental data with a corresponding environmental data type threshold to determine a second difference; Comparing the first difference with a preset adjacent data difference threshold, and determining a first number of first difference values ​​greater than the preset adjacent data difference threshold; Comparing the second difference with a preset environment data difference threshold, and determining a second number that is greater than the preset environment data difference threshold; Based on the ratio value corresponding to the first number and the second number, the environmental stability state of the control room of the rubber production equipment to be tested is determined.

7. The method for predicting the environment of a control room of a rubber production equipment according to claim 1, characterized in that: Before obtaining the actual power values ​​corresponding to the respective areas to be tested in the control room of the rubber production equipment to be tested, the method further includes: Obtaining an equipment layout diagram corresponding to the control room of the rubber production equipment to be tested, and performing a preliminary division of the control room of the rubber production equipment to be tested based on corresponding physical obstacles in the equipment layout diagram; Determine device density based on device location and device size; Based on the equipment density, the control room of the rubber production equipment to be tested is divided twice to obtain a plurality of the areas to be tested; The equipment information of the control room of the rubber production equipment to be tested is monitored in real time, and each of the divided areas to be tested is dynamically adjusted.

8. A rubber production equipment control room environment prediction device, characterized in that: The device comprises a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the device is triggered to execute the method according to any one of claims 1 to 7.

9. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions can execute the method according to any one of claims 1 to 7.

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