Workshop collaborative abnormality early warning system based on dynamic correlation of multi-source environment parameters

Abstract

The application discloses a workshop cooperative abnormality early warning system based on dynamic correlation of multi-source environment parameters, relates to the field of workshop safety management, and comprises a collection module, a visualization module and the like, wherein the collection module is used for collecting workshop environment parameters and storing the workshop environment parameters; the visualization module is used for receiving the workshop environment parameters collected by the collection module, and generating change trend graphs representing each type of workshop environment parameter based on the workshop environment parameters; through dynamic collection and correlation analysis of multiple types of environment parameters, the application can capture subtle changes of the workshop environment and equipment operation in real time, break through the limitation of traditional single parameter monitoring, improve the comprehensiveness and accuracy of abnormality identification, and intelligently optimize data collection density according to the difference of equipment vibration signals through a running frequency dynamic adjustment mechanism, so as to ensure accurate acquisition of key information under complex working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of workshop safety management, in particular to a workshop collaborative abnormal early warning system based on dynamic correlation of multi-source environment parameters. BACKGROUND

[0002] Workshop management is the system control of various elements in the production site, including personnel, equipment, materials, process, environment, etc. By formulating standardized processes, optimizing capacity configuration, monitoring progress and cost, production efficiency and order are ensured.

[0003] The invention patent application with application number 202310024270.5 discloses a workshop production safety early warning system, which comprises: a plurality of monitoring terminals and a monitoring platform, and each monitoring terminal is in wireless communication with the monitoring platform; the monitoring terminal is used to acquire real-time time domain data corresponding to each safety index related to the production safety status of different areas and real-time image data of different areas; the monitoring platform is used to form a training set by the time domain data corresponding to each safety index related to the production safety status of different areas when a safety accident occurs in different areas and the original image data when a safety accident occurs in different areas; to construct a safety early warning model; and to input the real-time time domain data and the real-time image data into the safety early warning model trained by the training set to obtain safety early warning information; wherein the safety early warning model comprises: a feature extraction module, a regional data correction module, a regional fusion module, a target segmentation module and an alarm module; the feature extraction module is used to extract smoke feature data from real-time image data of different areas, and to extract index feature data from real-time time domain data corresponding to each safety index of different areas; the regional data correction module is used to correct and compensate the smoke feature data according to a plurality of index feature data to obtain complete regional smoke feature data; the regional fusion module is used to restore the regional smoke feature data to the original image data and to splice and fuse the image data of each area; the target segmentation module is used to segment the complete global smoke target image data from the spliced and fused image data, and to obtain new smoke feature data of the global smoke target image data, which aims to solve the problem of how to obtain accurate small smoke concentration data at the beginning of the fire without affecting the normal operation of the workshop.

[0004] However, for the workshop production scene, the environmental information volume is huge, and the existing technology mostly monitors one environmental information, thereby carrying out distributed safety control for the workshop. This way, the early warning effect of the workshop is not comprehensive, the monitoring result is independent, and it is difficult to comprehensively evaluate the abnormal problems of the workshop.

[0005] Therefore, a workshop collaborative abnormal early warning system based on dynamic correlation of multi-source environment parameters is proposed. SUMMARY

[0006] In view of the above-mentioned defects of the prior art, the present application provides a workshop collaborative abnormal early warning system based on dynamic correlation of multi-source environment parameters, which can effectively solve the problems of the prior art.

[0007] To achieve the above-mentioned purposes, the present application is implemented by the following technical solutions:

[0008] The present application discloses a workshop collaborative abnormal early warning system based on dynamic correlation of multi-source environment parameters, comprising:

[0009] The collection module is used for collecting and storing the workshop environment parameters; the visualization module is used for receiving the workshop environment parameters collected by the collection module, and generating change trend graphs representing each type of workshop environment parameter based on the workshop environment parameters; the diagnosis module is used for traversing the change trend graphs generated by the visualization module, configuring an early warning threshold for each change trend graph, monitoring each corresponding change trend graph based on the early warning threshold configured for each change trend graph, and determining that there is a risk in the operation of the workshop when the value represented in the change trend graph exceeds the corresponding early warning threshold; the analysis module is used for receiving the change trend graphs generated by the visualization module, and analyzing the tendency of the workshop operation failure based on the change trend graphs; the determination module is used for receiving the tendency of the workshop operation failure analyzed by the analysis module, setting an operation failure determination threshold, comparing the tendency of the workshop operation failure with the operation failure determination threshold, and determining that there is a failure in the operation of the workshop when the tendency of the workshop operation failure is greater than the operation failure determination threshold; and the message module is used for obtaining the operation result data of the diagnosis module, the analysis module and the determination module, combining the operation result data of each module to generate a system operation message, and feeding back the system operation message to the system end user.

[0010] Further, the collection module is integrated by sensing devices capable of sensing the dust content, temperature, humidity, equipment vibration signal in the workshop and the proportion of harmful gas components in the air, i.e. the workshop environment parameters; when the collection module stores the workshop environment parameters, each environment parameter is marked with a collection time stamp, and the environment parameters are stored separately based on the type of the environment parameters.

[0011] The collection module is provided with a plurality of collection modules, and the plurality of collection modules are distributed in the internal space of the workshop in a matrix shape, have equal spacing, the same operating frequency and synchronization.

[0012] Further, the operating frequency of the plurality of collection modules continuously changes with reference to the equipment vibration signal in the workshop within a preset change range during the operation process, and the change process is subject to:

[0013] Run continuously based on a preset initial operating frequency. After running continuously for at least two times, obtain the vibration signals of the equipment in the workshop sensed in each operation. Always use the latest two sets of vibration signals of the equipment in the workshop to identify the difference in vibration signals. Control the acquisition module to increase the operating frequency as the difference increases and decrease it as the difference decreases.

[0014] Among them, when there is more than one device in the workshop, the vibration signal difference used to control the operating frequency of the acquisition module is the average of the vibration signal differences of each device.

[0015] Furthermore, the difference in vibration signals of equipment in the workshop is expressed as:

[0016] ;

[0017] Where: is the difference between vibration signal X and vibration signal Y; is the number of sampling points; is the amplitude of signal X at the i-th sampling point, and the amplitude of signal Y at the i-th sampling point; is the amplitude of the jth frequency component in the spectrum of signal X and the amplitude of the jth frequency component in the spectrum of signal Y; 、 is the phase of signal X at the i-th sampling point, and the phase of signal Y at the i-th sampling point;

[0018] Among them, the positive frequency part of the spectrum of signal X is recorded as , the positive frequency part of the spectrum of signal Y is recorded as .

[0019] Furthermore, during the operation stage of the visualization module, the generated change trend graph corresponds one-to-one to each type of workshop environmental parameter, and the change trend graph representing each type of workshop environmental parameter is a line graph, and the workshop environmental parameters represented in the line graph are updated in real time based on the newly collected workshop environmental parameters when the acquisition module is running.

[0020] Furthermore, when the diagnostic module does not determine that the value represented by the change trend graph exceeds the corresponding warning threshold, it determines that the workshop operation is safe and synchronously refreshes the system operation;

[0021] The diagnostic module is provided with a monitoring unit and a picking unit at the lower level. The monitoring unit is used to monitor whether the value source change trend graph exceeding the corresponding warning limit value is unique when the diagnostic module determines that there is an operation risk in the workshop during the operation phase;

[0022] Unique: Trigger the picking unit to run and pick the trend graph that is most similar to the trend graph of the value source that exceeds the warning limit;

[0023] Not unique: workshop downtime, coordinate and maintain the environment and equipment in the workshop, and restore the environment and equipment in the workshop to the preset state;

[0024] Among them, the pickup unit runs, and the pickup target is the value source trend graph exceeding the early warning limit value, the trend graph representing the vibration signal of the equipment in the workshop, and the trend graph most similar to the value source trend graph exceeding the early warning limit value;

[0025] When the pickup target is the value source trend graph exceeding the early warning limit value and the trend graph representing the vibration signal of the equipment in the workshop, the pickup unit picks up only two sets of trend graphs, and the pickup unit runs the picked trend graphs synchronously to the analysis module.

[0026] Further, the similarity calculation logic of the trend graph is represented as:

[0027] ;

[0028] In the formula: is the similarity of the trend graph a and the trend graph b; 、 is the total number of nodes on the multi-segment broken line representing the value in the trend graph a and the trend graph b; is the coordination factor; is the total number of intersection nodes on the multi-segment broken line representing the value in the trend graph a and the trend graph b from the origin of the trend graph to the horizontal axis direction; 、 is the length of the representation line segment from q node to q+1 node in the trend graph a, and the length of the representation line segment from q node to q+1 node in the trend graph b; 、 is the slope of the representation line segment from q node to q+1 node in the trend graph a, and the slope of the representation line segment from q node to q+1 node in the trend graph b;

[0029] Among them, represents the mean operation of The coordination factor takes the value of 1 or -1. If the numerator of the coordination factor is less than or equal to the denominator, the coordination factor takes the value of 1, otherwise, the coordination factor takes the value of -1.

[0030] Further, the workshop running fault tendency analysis logic in the analysis module is represented as:

[0031] ;

[0032] In the formula: is the workshop running fault tendency; The difference of parameters in the trend graph of the value source that exceeds the warning limit; The difference of the parameters in the trend graph that is most similar to the trend graph of the value source that exceeds the warning limit; To remove and The total number of nodes in the change trend graph outside the change trend graph pointed to; To remove and In addition to the change trend graph, the warning threshold value configured in the change trend graph; is the representation value of the vth node;

[0033] When the picking unit picks up only two groups of trend graphs, find The third product term in the logic formula is replaced by a constant 1. When the difference calculation target is the vibration signal of equipment outside the workshop, the difference calculation logic is expressed as follows:

[0034] Get the latest two values ​​in the trend graph, and record them as MAX(I) and MIN(I) based on the size of the two values. The difference calculation logic is expressed as ;

[0035] Among them, the fault judgment threshold set in the judgment module corresponds one-to-one to each workshop environmental parameter. When the judgment module applies the fault judgment threshold and compares it with the workshop operation fault tendency, the diagnosis module operation stage is selected, and the workshop environmental parameter corresponding to the change trend graph of the value source that exceeds the warning limit value is selected to perform a comparison operation on the fault judgment threshold.

[0036] Furthermore, when the determination module determines that there is a fault in the operation of the workshop, the workshop is shut down, and the environment and equipment in the workshop are coordinated and maintained to restore the environment and equipment in the workshop to a preset state;

[0037] When the determination module determines that there is no fault in the workshop operation, the workshop environment parameter management device corresponding to the value source change trend graph picked up by the picking unit that exceeds the warning limit value is taken as a maintenance target and offline maintenance is performed.

[0038] Furthermore, the acquisition module is interactively connected with the visualization module and the diagnosis module through a wireless network, the lower level of the diagnosis module is interactively connected with the monitoring unit and the picking unit through a wireless network, the diagnosis module is interactively connected with the analysis module and the judgment module through a wireless network, and the judgment module is interactively connected with the message module through a wireless network.

[0039] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0040] The application provides a workshop collaborative abnormality early warning system based on dynamic correlation of multi-source environment parameters. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0042] Figure 1 FIG. 1 is a structural schematic diagram of the workshop collaborative abnormality early warning system based on dynamic correlation of multi-source environment parameters. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0044] The present application will be further described below in combination with the embodiments.

[0045] Embodiment:

[0046] The workshop collaborative abnormality early warning system based on dynamic correlation of multi-source environment parameters in the embodiment, as shown in FIG. 1, comprises: Figure 1

[0047] The acquisition module is used for acquiring the workshop environment parameters and storing the workshop environment parameters.

[0048] ​The collection module is integrated by sensing devices capable of sensing the dust content, temperature, humidity, equipment vibration signal in the workshop, and the proportion of harmful gas components in the air. The dust content, temperature, humidity, equipment vibration signal in the workshop, and the proportion of harmful gas components in the air are the workshop environment parameters. When the collection module stores the workshop environment parameters, each environment parameter is marked with a collection time stamp, and the environment parameters are stored based on the type of the environment parameters;

[0049] The collection module is integrated by sensing devices capable of sensing the dust content, temperature, humidity, equipment vibration signal in the workshop, and the proportion of harmful gas components in the air. The dust content, temperature, humidity, equipment vibration signal in the workshop, and the proportion of harmful gas components in the air are the workshop environment parameters. When the collection module stores the workshop environment parameters, each environment parameter is marked with a collection time stamp, and the environment parameters are stored based on the type of the environment parameters;

[0050] The running frequency of the plurality of groups of collection modules changes continuously within the preset change range during the running process, and the change process is subject to:

[0051] Based on the preset initial running frequency, after continuous running for not less than two times, the equipment vibration signals in the workshop sensed each time are obtained, the latest two groups of equipment vibration signals in the workshop are always applied to identify the vibration signal difference, and the running frequency of the collection module is controlled to increase with the increase of the difference and to decrease with the decrease of the difference;

[0052] When the equipment in the workshop is not unique, the vibration signal difference for controlling the running frequency of the collection module is the mean value of the vibration signal differences of each equipment;

[0053] The vibration signal difference in the workshop is represented as:

[0054] ;

[0055] In the formula: is the difference between the vibration signal X and the vibration signal Y; is the number of sampling points; is the amplitude of signal X at the i-th sampling point, and the amplitude of signal Y at the i-th sampling point; is the amplitude of the j-th frequency component in the spectrum of signal X, and the amplitude of the j-th frequency component in the spectrum of signal Y; 、 is the phase of signal X at the i-th sampling point, and the phase of signal Y at the i-th sampling point;

[0056] In the formula: is the positive frequency part of the spectrum of signal X, and is the positive frequency part of the spectrum of signal Y;

[0057] The vibration signal difference in the workshop is calculated by the above logical formula, which is used to control the running frequency of the collection module;

[0058] a visualization module configured to receive the workshop environment parameters collected by the collection module, and generate a change trend graph representing each type of workshop environment parameter based on the workshop environment parameters;

[0059] In the visualization module running stage, the generated change trend graphs correspond to each type of workshop environment parameter one by one, and the change trend graph representing each type of workshop environment parameter is a line graph, and the workshop environment parameter represented in the line graph is updated in real time based on the newly collected workshop environment parameters collected by the collection module;

[0060] a diagnosis module configured to traverse the change trend graphs generated by the visualization module, configure a warning threshold for each change trend graph, and monitor each corresponding change trend graph based on the warning threshold configured for each change trend graph, and determine that the workshop has a running risk when the value represented in the change trend graph exceeds the corresponding warning threshold;

[0061] When the diagnosis module determines that the value represented in the change trend graph does not exceed the corresponding warning threshold, it determines that the workshop is running safely and synchronously refreshes the system running;

[0062] The diagnosis module is provided with a monitoring unit and a pickup unit. When the diagnosis module determines that the workshop has a running risk, the monitoring unit is configured to monitor whether the value exceeding the corresponding warning threshold is unique in the change trend graph;

[0063] Unique: triggering the pickup unit to run and picking up the change trend graph most similar to the change trend graph from which the value exceeding the warning threshold originates;

[0064] Not unique: shutting down the workshop, and coordinating and maintaining the environment and equipment in the workshop to restore the environment and equipment in the workshop to a preset state;

[0065] In the pickup unit running stage, the pickup target is the change trend graph from which the value exceeding the warning threshold originates, the change trend graph representing the vibration signal of the equipment in the workshop, and the change trend graph most similar to the change trend graph from which the value exceeding the warning threshold originates;

[0066] When the pickup target is the change trend graph from which the value exceeding the warning threshold originates and the change trend graph representing the vibration signal of the equipment in the workshop, the pickup unit picks up only two sets of change trend graphs, and the pickup unit running picks up the change trend graphs and synchronously forwards them to the analysis module;

[0067] The similarity calculation logic of the change trend graph is represented as:

[0068] ;

[0069] In the formula: is the similarity of the change trend graph a and the change trend graph b. , total quantity of nodes on the multi-segment broken line representing the numerical value in the change trend graph a and the change trend graph b; is a coordination factor; total quantity of intersection nodes on the multi-segment broken line representing the numerical value in the change trend graph a and the change trend graph b, starting from the origin of the change trend graph to the horizontal axis direction; , length of the representation line segment from the q node to the q+1 node in the change trend graph a, length of the representation line segment from the q node to the q+1 node in the change trend graph b; , slope of the representation line segment from the q node to the q+1 node in the change trend graph a, slope of the representation line segment from the q node to the q+1 node in the change trend graph b;

[0070] wherein, represents the mean operation on , the coordination factor takes a value of 1 or -1, if the numerator of the coordination factor is less than or equal to the denominator, the coordination factor takes a value of 1, otherwise, the coordination factor takes a value of -1;

[0071] The similarity calculation logic between the change trend graphs is limited through the above logical formula;

[0072] The analysis module is configured to receive the change trend graphs generated in the visualization module, and analyze the workshop operation fault tendency based on the change trend graphs;

[0073] The workshop operation fault tendency analysis logic in the analysis module is represented as:

[0074] ;

[0075] In the formula, is the workshop operation fault tendency; is the difference of the parameter in the numerical source change trend graph exceeding the early warning threshold value; is the difference of the parameter in the most similar change trend graph to the numerical source change trend graph exceeding the early warning threshold value; is the difference of the parameter in the change trend graph other than and pointed change trend graph; is the early warning threshold value configured in the change trend graph other than and pointed change trend graph; is the representation value of the vth node;

[0076] When the change trend graphs picked up by the pickup unit are only two groups, the The third product term in the logical formula is replaced by a constant 1, and the difference calculation target is a non-plant internal equipment vibration signal, and the difference calculation logic is represented as:

[0077] The latest two values in the change trend graph are obtained, and the size of the two values is recorded as MAX(I) and MIN(I). The difference calculation logic is represented as ;

[0078] The above logical formula is used to analyze the plant running fault tendency, which provides support for the further operation of the system determination module;

[0079] The fault determination threshold set in the determination module corresponds to each plant environment parameter. When the determination module compares the fault determination threshold with the plant running fault tendency, it selects the diagnostic module running stage, and the value exceeding the warning threshold is compared with the fault determination threshold corresponding to the plant environment parameter of the change trend graph;

[0080] The determination module is used to receive the plant running fault tendency analyzed by the analysis module, set the running fault determination threshold, and compare the plant running fault tendency with the running fault determination threshold. When the plant running fault tendency is greater than the running fault determination threshold, it is determined that the plant running has a fault;

[0081] When the determination module determines that the plant running has a fault, the plant is shut down, and the environment and equipment in the plant are coordinated and maintained to restore the environment and equipment in the plant to the preset state;

[0082] When the determination module determines that the plant running does not have a fault, the plant environment parameter management equipment corresponding to the change trend graph of the value exceeding the warning threshold picked up by the pickup unit is taken as the maintenance target for offline maintenance;

[0083] The message module is used to obtain the diagnostic module, analysis module and determination module running result data, combine the module running result data to generate system running messages, and feedback to the system end user;

[0084] The acquisition module is connected to the visualization module and the diagnostic module through a wireless network. The diagnostic module is connected to the monitoring unit and the pickup unit through a wireless network. The diagnostic module is connected to the analysis module and the determination module through a wireless network. The determination module is connected to the message module through a wireless network.

[0085] In the embodiment, the acquisition module runs to acquire the workshop environment parameters, and stores the workshop environment parameters. The visualization module synchronously receives the workshop environment parameters acquired by the acquisition module, and generates trend graphs representing changes in each type of workshop environment parameter based on the workshop environment parameters. The diagnosis module runs to traverse the trend graphs generated by the visualization module, configures an early warning threshold for each trend graph, monitors each corresponding trend graph based on the early warning threshold configured for each trend graph, and determines that the workshop has a running risk when a value represented in a trend graph exceeds the corresponding early warning threshold. The monitoring unit synchronously monitors whether the value exceeding the corresponding early warning threshold in the determination that the workshop has a running risk is unique, picks up the trend graph most similar to the trend graph from which the value exceeding the early warning threshold is derived when the diagnosis result is yes, and receives the trend graphs generated in the visualization module by the analysis module, analyzes the workshop running failure tendency based on the trend graphs, further receives the workshop running failure tendency analyzed in the analysis module by the determination module, sets a running failure determination threshold, compares the workshop running failure tendency with the running failure determination threshold, and determines that the workshop has a running failure when the workshop running failure tendency is greater than the running failure determination threshold. Finally, the message module obtains the running result data of the diagnosis module, the analysis module, and the determination module, combines the running result data of each module to generate a system running message, and feeds back the system running message to the system end user.

[0086] Through the system running in the above embodiment, a system for applying multiple environment parameters to cooperate with abnormal early warning and failure diagnosis is provided for the workshop to serve the daily running safety management of the workshop.

[0087] The following gives an example of application of the system in the above embodiment:

[0088] The xx automobile parts manufacturing workshop has an area of 5000 square meters, is equipped with more than 80 production devices of multiple types such as stamping, welding, and assembly, the workshop environment parameters include dust content, temperature, humidity, device vibration signal, and harmful gas (such as carbon monoxide generated by welding) composition ratio, etc. To ensure production safety and efficiency, a “workshop cooperative abnormal early warning system based on dynamic correlation of multiple source environment parameters” is introduced, 20 acquisition modules are uniformly distributed in a matrix shape in the workshop, the module spacing is 5 meters, and the initial running frequency is set to 1 acquisition per minute.

[0089] (I) Data acquisition and storage

[0090] The acquisition module continuously senses the workshop environment parameters, adds an acquisition time stamp accurate to seconds to each parameter, and stores the parameters in the system database by type such as dust, temperature, etc. For example, the acquisition module in the welding area acquires welding dust concentration, device vibration frequency, and other data in real time, and each type of data is stored independently for subsequent analysis.

[0091] (II) Visual analysis

[0092] The visualization module converts various environmental parameters into real-time updated line graphs. Taking the device vibration signal as an example, the system generates an independent line graph for each device, with the horizontal axis representing time and the vertical axis representing vibration amplitude, frequency, etc. The vibration signal line graph of a certain punch press can display the vibration fluctuation during its operation in real time, making it easy for operators to observe the device's running state intuitively.

[0093] (III) Abnormal diagnosis and risk judgment

[0094] The diagnosis module sets warning thresholds for each line graph, such as setting the device vibration amplitude warning threshold to 80 dB. When the amplitude of a certain welding robot's vibration signal line graph exceeds 80 dB for three consecutive times, the diagnosis module determines that there is a risk of operation. At this time, the monitoring unit checks whether the source of the value exceeding the warning threshold is unique, and if only the robot's vibration signal is abnormal, the pickup unit is triggered to pick up the vibration signal line graph of the most similar other device (such as the vibration graph of another robot of the same type) and the vibration graph of the robot, and synchronously forward them to the analysis module.

[0095] (IV) Fault tendency analysis and disposal

[0096] The analysis module calculates the fault tendency value according to the formula. Assuming that the welding robot's vibration signal difference (DIFFOUT) is 0.6, the difference of the similar line graph (DIFFSIMM|MAX) is 0.4, the total number of other environmental parameter nodes is 50, and the average of the warning threshold is 0.5, then the fault tendency F = 0.6 x 0.4 x (50 x 0.5) = 6. The judgment module compares the F value with the fault judgment threshold corresponding to the device (set to 5), and since 6 > 5, it is determined that there is a fault, the workshop immediately stops the robot, and the mechanical arm, transmission components, etc. are comprehensively checked, the gear wear problem is found and replaced in time, and the device is restored to the preset state.

[0097] (V) Daily maintenance and system optimization

[0098] When the diagnosis module does not detect abnormalities, the system automatically refreshes the operation. If multiple collection modules detect that the workshop dust concentration is close to the warning threshold (such as reaching 50 mg / m³, and the warning threshold is 60 mg / m³) at the same time during a certain detection, but does not exceed it, the diagnosis module sets the dust filtering device as the maintenance target and arranges the staff to replace the filter element and other offline maintenance to prevent potential abnormalities.

[0099] In addition, the running frequency of the acquisition module is dynamically adjusted according to the equipment vibration signal. When multiple devices are running simultaneously, causing the vibration signal difference to increase (for example, when the production line is in full production), the acquisition frequency is automatically increased to 1.5 times per minute, ensuring that subtle abnormalities are captured in a timely manner; when the production is slack and the equipment is running less, the vibration signal difference decreases, and the frequency is reduced to the initial value, saving system resources.

[0100] In summary, in the above-mentioned embodiments, the system can capture subtle changes in the workshop environment and equipment operation in real time through dynamic acquisition and correlation analysis of multiple types of environmental parameters, breaking through the limitations of traditional single parameter monitoring, improving the comprehensiveness and accuracy of abnormal identification, and intelligently optimizing the data acquisition density according to the difference in equipment vibration signals through a dynamic adjustment mechanism of the running frequency, ensuring accurate acquisition of key information under complex working conditions. Through similarity calculation and fault tendency analysis model, the potential correlation between parameters can be deeply mined, and the development trend of the fault can be predicted in advance, realizing intelligent management of the whole process from risk early warning to fault judgment. The system can not only quickly locate the source of the abnormality and trigger a graded response when the risk occurs, but also can develop targeted maintenance strategies based on the analysis results, effectively reducing unnecessary downtime, and improving the safety, stability and maintenance efficiency of the workshop operation.

[0101] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters, characterized by: include: The acquisition module is used to collect and store workshop environmental parameters; A visualization module is used to receive the workshop environmental parameters collected by the collection module and generate graphs representing the changing trends of various types of workshop environmental parameters based on the workshop environmental parameters; The diagnostic module is used to traverse the trend graphs generated by the visualization module, configure a warning threshold for each trend graph, monitor each corresponding trend graph based on the warning threshold configured for each trend graph, and determine that there is an operation risk in the workshop when the value represented in the trend graph exceeds the corresponding warning threshold; An analysis module is used to receive the change trend graph generated by the visualization module and analyze the workshop operation failure tendency based on the change trend graph; The analysis logic of workshop operation failure tendency in the analysis module is expressed as follows: ； Where: Failure tendency for workshop operation; The difference of parameters in the trend graph of the value source that exceeds the warning limit; The difference of the parameters in the trend graph that is most similar to the trend graph of the value source that exceeds the warning limit; To remove and The total number of nodes in the change trend graph outside the change trend graph pointed to; To remove and In addition to the change trend graph, the warning threshold value configured in the change trend graph; is the representation value of the vth node; When there are only two groups of trend graphs picked up by the picking unit, find The third product term in the logic formula is replaced by a constant 1. When the difference calculation target is the vibration signal of equipment outside the workshop, the difference calculation logic is expressed as follows: Get the latest two values ​​in the trend graph, and record them as MAX(I) and MIN(I) based on the size of the two values. The difference calculation logic is expressed as ; Among them, the fault judgment thresholds set in the judgment module correspond one-to-one with each workshop environmental parameter. When the judgment module compares the fault judgment thresholds with the workshop operation fault tendency, it selects the workshop environmental parameter corresponding to the fault judgment threshold corresponding to the change trend graph of the value source exceeding the warning limit during the operation stage of the diagnosis module and performs the comparison operation; a determination module, configured to receive the workshop operation fault tendency analyzed by the analysis module, set an operation fault determination threshold, and determine that a workshop operation fault exists when the workshop operation fault tendency is greater than the operation fault determination threshold based on a comparison between the workshop operation fault tendency and the operation fault determination threshold; The message module is used to obtain the operation result data of the diagnosis module, analysis module and judgment module, combine the operation result data of each module to generate the system operation message, and provide feedback to the system end user.

2. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 1 is characterized in that: The acquisition module is integrated with sensor devices that can sense the workshop dust content, temperature, humidity, vibration signals of equipment in the workshop, and the proportion of harmful gas components in the air. The workshop dust content, temperature, humidity, vibration signals of equipment in the workshop, and the proportion of harmful gas components in the air are workshop environmental parameters. When the acquisition module stores the workshop environmental parameters, it marks the collection timestamp of each environmental parameter and stores the environmental parameters separately based on the type of environmental parameter; Among them, there are several collection modules, which are distributed in the internal space of the workshop in a matrix shape. The collection modules are equally spaced and have the same and synchronous operating frequencies.

3. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 2 is characterized in that: During the operation of the plurality of acquisition modules, the operating frequencies are continuously changed within a preset change range with reference to the vibration signals of the equipment in the workshop. The change process is subject to: Run continuously based on a preset initial operating frequency. After running continuously for at least two times, obtain the vibration signals of the equipment in the workshop sensed in each operation. Always use the latest two sets of vibration signals of the equipment in the workshop to identify the difference in vibration signals. Control the acquisition module to increase the operating frequency as the difference increases and decrease it as the difference decreases. Among them, when there is more than one device in the workshop, the vibration signal difference used to control the operating frequency of the acquisition module is the average of the vibration signal differences of each device.

4. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 3 is characterized in that: The difference in vibration signals of the equipment in the workshop is expressed as: ； Where: is the difference between vibration signal X and vibration signal Y; is the number of sampling points; is the amplitude of signal X at the i-th sampling point, and the amplitude of signal Y at the i-th sampling point; is the amplitude of the jth frequency component in the spectrum of signal X and the amplitude of the jth frequency component in the spectrum of signal Y; 、 is the phase of signal X at the i-th sampling point, and the phase of signal Y at the i-th sampling point; Among them, the positive frequency part of the spectrum of signal X is recorded as , the positive frequency part of the spectrum of signal Y is recorded as .

5. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 1 is characterized in that: During the operation stage of the visualization module, the generated change trend graph corresponds one-to-one to each type of workshop environmental parameter, and the change trend graph representing each type of workshop environmental parameter is a line graph, and the workshop environmental parameters represented in the line graph are updated in real time based on the newly collected workshop environmental parameters when the acquisition module is running.

6. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 1 is characterized in that: When the diagnostic module does not determine that the value represented by the change trend graph exceeds the corresponding warning threshold, it determines that the workshop operation is safe and synchronously refreshes the system operation; The diagnostic module is provided with a monitoring unit and a picking unit at the lower level. The monitoring unit is used to monitor whether the value source change trend graph exceeding the corresponding warning limit value is unique when the diagnostic module determines that there is an operation risk in the workshop during the operation phase; Unique: Trigger the picking unit to run and pick the trend graph that is most similar to the trend graph of the value source that exceeds the warning limit; Not unique: When the workshop stops operating, coordinate and maintain the environment and equipment in the workshop to restore the environment and equipment in the workshop to the preset state; Among them, during the operation phase of the picking unit, the picking targets are the trend graph of the value source exceeding the warning threshold, the trend graph representing the vibration signal of the equipment in the workshop, and the trend graph that is most similar to the trend graph of the value source exceeding the warning threshold; When the picking target is the numerical source change trend graph that exceeds the warning limit value and the change trend graph representing the vibration signal of the equipment in the workshop, the picking unit picks up only two groups of change trend graphs, and the change trend graphs picked up by the picking unit are synchronously forwarded to the analysis module.

7. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 6 is characterized in that: The similarity calculation logic of the change trend graph is expressed as: ； Where: is the similarity between the change trend graph a and the change trend graph b; 、 The total number of nodes on the multi-segment broken line representing the numerical value in the change trend graph a and the change trend graph b; is the coordination factor; The total number of intersection nodes on the multi-segment broken lines representing values ​​in the trend graph a and the trend graph b, starting from the origin of the trend graph toward the horizontal axis; 、 is the length of the line segment from node q to node q+1 in the trend graph a, and the length of the line segment from node q to node q+1 in the trend graph b; 、 is the slope of the line segment from node q to node q+1 in the trend graph a, and the slope of the line segment from node q to node q+1 in the trend graph b; in, Express In the averaging operation, the coordination factor takes the value of 1 or -1. If the numerator of the fraction containing the coordination factor is less than or equal to the denominator, the coordination factor takes the value of 1. Otherwise, the coordination factor takes the value of -1.

8. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 1 is characterized in that: When the determination module determines that there is a fault in the workshop operation, the workshop stops operation, coordinates and maintains the environment and equipment in the workshop, and restores the environment and equipment in the workshop to a preset state; When the determination module determines that there is no fault in the workshop operation, the workshop environment parameter management device corresponding to the value source change trend graph picked up by the picking unit that exceeds the warning limit value is taken as a maintenance target and offline maintenance is performed.

9. The workshop collaborative abnormality warning system based on dynamic association of multi-source environmental parameters according to claim 1 is characterized in that: The acquisition module is interactively connected to the visualization module and the diagnosis module through a wireless network. The lower level of the diagnosis module is interactively connected to the monitoring unit and the picking unit through a wireless network. The diagnosis module is interactively connected to the analysis module and the judgment module through a wireless network. The judgment module is interactively connected to the message module through a wireless network.

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