Equipment operation monitoring system and method based on intelligent sensor

Abstract

The present invention discloses an equipment operation monitoring system and method based on intelligent sensors, belonging to the field of sensor technology. Construct a synchronous sub-monitoring sequence data cluster, the data cluster includes synchronous sub-sequence number information, intelligent sensor coding information and equipment operation parameter information; characterize the monitoring behavior curve of each intelligent sensor in the form of coordinate points, and the monitoring behavior curve records the synchronous sub-sequence number information and equipment operation parameter information; capture the jump point and jump value of the monitoring behavior curve, form a synchronous interval class, and generate a fuzzy object; form a synchronous perception fuzzy set, and analyze the membership of the fuzzy object based on the jump value; determine whether there is perception synergy between intelligent sensors, update the synchronous perception fuzzy set, analyze the perception synergy between intelligent sensors, and perform intelligent early warning; and then, based on the synchronous perception behavior sequence of the sensor, while performing fuzzy reasoning, it is possible to achieve early warning synergy between multiple sensors.

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and in particular to an equipment operation monitoring system and method based on intelligent sensors. Background Art

[0002] The process of sensor monitoring equipment operation is a complete process from sensing physical quantities to outputting processing results. In this process, the sensor converts various physical quantities in the equipment operation process into electrical signals through its built-in sensitive elements and conversion elements, and outputs them to the corresponding equipment for observation, recording or control after signal processing. At the same time, the feedback and adjustment mechanism can ensure the stable operation and safe production of the equipment;

[0003] In the process of industrialized intelligent production, multiple types of sensors are often involved to monitor different types of equipment and their operating parameters. Due to the complexity of coordination between devices, the perception feedback between sensors is also accompanied by the same complexity, which is not conducive to timely and effective early warning of equipment operation safety. Summary of the invention

[0004] The object of the present invention is to provide a device operation monitoring system and method based on intelligent sensors to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] An equipment operation monitoring system based on intelligent sensors, the system comprises: a collaborative perception module, a curve analysis module, a fuzzy generation module and a fuzzy processing module;

[0007] The collaborative sensing module is used to construct a synchronous sub-monitoring sequence data cluster, wherein the data cluster includes synchronous sub-sequence number information, coding information of the intelligent sensor and equipment operation parameter information;

[0008] The curve analysis module is used to describe the monitoring behavior curve of each smart sensor in the form of coordinate points, in which the synchronization sequence number information and equipment operation parameter information are recorded; capture the jump point and jump value of the monitoring behavior curve, and form a synchronization interval class through a timestamp to generate a fuzzy object;

[0009] The fuzzy generation module forms a synchronization-aware fuzzy set based on the synchronization interval class and analyzes the membership of the fuzzy object based on the jump value;

[0010] The fuzzy processing module determines whether there is perception synergy between smart sensors based on fuzzy objects, updates the synchronous perception fuzzy set, analyzes the perception synergy between smart sensors, and performs intelligent early warning.

[0011] Further, the collaborative perception module includes a timer unit and a curve characterization unit;

[0012] The timer unit is used to initialize the time timer and synchronously collect the equipment operation parameters monitored by each smart sensor; the smart sensors are uniformly coded, and one smart sensor is used to monitor one type of equipment operation parameter. Based on the coding information of the smart sensor, a set of equipment operation parameters is generated, which is recorded as PS i ={WP it |t∈[1,T]}, where PS i represents the equipment operation parameter set generated by the i-th smart sensor, WP it Indicates the equipment operating parameters monitored by the ith smart sensor at the tth synchronization time, and T indicates the current synchronization number;

[0013] The curve drawing unit is used to establish a two-dimensional coordinate system and generate coordinate points (t, WP it ), smoothly connect each coordinate point in turn, and describe the monitoring behavior curve of the i-th smart sensor, recorded as g(i), and calculate the jump boundary value of the monitoring behavior curve g(i). The jump boundary corresponding to the jump boundary value is recorded as G(i), and the jump boundary G(i) is a straight line parallel to the horizontal coordinate in the two-dimensional coordinate system.

[0014] Furthermore, the curve analysis module includes a jump capture unit and a fuzzy processing unit;

[0015] The jump capture unit is used to identify and mark the intersection formed between the monitoring behavior curve g(i) and the jump boundary G(i) in the two-dimensional coordinate system, record the intersection as the jump point, record any jump point as x, and record the ordinate value corresponding to the jump point x on the monitoring behavior curve g(i) as the jump value WP ix ;

[0016] The synchronization class generation unit, based on the synchronization acquisition timestamp, constructs the tth synchronization interval class between synchronization times t and synchronization times t+1, and a total of T-1 synchronization interval classes are obtained; if x∈[t, t+1], the jump point x is classified into the tth synchronization interval class, and a fuzzy object is generated, which is recorded as x|i.

[0017] Further, the fuzzy generation module includes a synchronous perception fuzzy set generation unit and a membership analysis unit;

[0018] The synchronous perception fuzzy set generation unit generates a synchronous perception fuzzy set based on the synchronous interval class, which is recorded as F(t)={[x|i, f(x|i, t)]|x∈[1, T], i∈[1, I]}, wherein [x|i, f(x|i, t)] represents a fuzzy term, I represents the total number of smart sensors, and f(x|i, t) represents the membership of the fuzzy object x|i relative to the synchronous perception fuzzy set F(t);

[0019] The membership analysis unit calculates the membership degree based on the jump point and the jump value under the jump point, in, NUM[F(t)] represents the total number of fuzzy terms contained in the synchronous perceptual fuzzy set F(t).

[0020] Furthermore, the fuzzy processing module includes a perception collaborative judgment unit and an intelligent analysis and early warning unit;

[0021] The perception synergy judgment unit analyzes and judges whether there is perception synergy between smart sensors based on fuzzy objects. The judgment formula is:

[0022]

[0023] Where y represents the number of the smart sensor. Indicates the presence of a symbol;

[0024] If the judgment formula is satisfied, it means that there is perception synergy between the i-th smart sensor and the y-th smart sensor;

[0025] The intelligent analysis and early warning unit updates the synchronous perception fuzzy set, retains all intelligent sensors with perception collaboration, and records the updated synchronous perception fuzzy set as

[0026] Based on the updated synchronous perception fuzzy set, the perception coordination degree between smart sensors is analyzed and calculated. The calculation formula is:

[0027]

[0028] in, It represents the perception coordination degree between smart sensors under the updated synchronous perception fuzzy set;

[0029] Represents the updated synchronous perception fuzzy set The number of fuzzy terms contained in ; L represents the preset perceptual collaborative expectation;

[0030] Under the current synchronization number T, the updated synchronization perception fuzzy set is output, denoted as If the perceived synergy If it is less than or equal to the coordination threshold, a monitoring warning prompt is issued, otherwise no monitoring warning prompt is issued.

[0031] A device operation monitoring method based on an intelligent sensor, the method comprising the following steps:

[0032] Step S100: constructing a synchronous secondary monitoring sequence data cluster, wherein the data cluster includes synchronous secondary sequence number information, coding information of the smart sensor and equipment operation parameter information;

[0033] Step S200: depicting the monitoring behavior curve of each smart sensor in the form of coordinate points, wherein the monitoring behavior curve records the synchronization sequence number information and the equipment operation parameter information; capturing the jump point and jump value of the monitoring behavior curve, and forming a synchronization interval class through a timestamp to generate a fuzzy object;

[0034] Step S300: Based on the synchronization interval class, a synchronization perception fuzzy set is constructed, and the membership of the fuzzy object is analyzed based on the jump value;

[0035] Step S400: Based on the fuzzy object, determine whether there is perception synergy between the smart sensors, update the synchronous perception fuzzy set, analyze the perception synergy between the smart sensors, and perform intelligent early warning.

[0036] Furthermore, the specific implementation process of step S100 includes:

[0037] Step S101: Initialize the timer and synchronously collect the equipment operation parameters monitored by each smart sensor; uniformly encode the smart sensors, and one smart sensor is used to monitor one type of equipment operation parameter. Based on the encoding information of the smart sensor, generate a set of equipment operation parameters, recorded as PS i ={WP it |t∈[1,T]}, where PS i represents the equipment operation parameter set generated by the i-th smart sensor, WP it Indicates the equipment operating parameters monitored by the ith smart sensor at the tth synchronization time, and T indicates the current synchronization number;

[0038] Step S102: Establish a two-dimensional coordinate system and generate coordinate points (t, WP it ), smoothly connect each coordinate point in turn, and describe the monitoring behavior curve of the i-th smart sensor, recorded as g(i), and calculate the jump boundary value of the monitoring behavior curve g(i). The jump boundary corresponding to the jump boundary value is recorded as G(i), and the jump boundary G(i) is a straight line parallel to the horizontal coordinate in the two-dimensional coordinate system;

[0039] According to the above method, in the equipment operation monitoring technology based on intelligent sensors, synchronous sub-feedback sequence data is used to facilitate the quantification of complex equipment operation coordination relationships on a unified scale.

[0040] Furthermore, the specific implementation process of step S200 includes:

[0041] Step S201: In a two-dimensional coordinate system, identify and mark the intersection between the monitoring behavior curve g(i) and the jump boundary G(i), record the intersection as the jump point, record any jump point as x, and record the ordinate value corresponding to the jump point x on the monitoring behavior curve g(i) as the jump value WP ix ;

[0042] Step S202: Based on the synchronization acquisition timestamp, during synchronization times t and t+1, the t-th synchronization interval class is formed, and a total of T-1 synchronization interval classes are obtained; if x∈[t, t+1], the jump point x is classified into the t-th synchronization interval class, and a fuzzy object is generated, which is recorded as x|i;

[0043] According to the above method, the frequency scale of the synchronization times should not be set too large or too small. If it is too large, irrelevant data will be too redundant, and if it is too small, the correlation of related data will fail. By analyzing the jump points through the curve, the scale can be controlled based on the actual operation of the equipment.

[0044] Furthermore, the specific implementation process of step S300 includes:

[0045] Step S301: Based on the synchronization interval class, generate a synchronization perception fuzzy set, denoted as F(t)={[x|i, f(x|i, t)]|x∈[1, T], i∈[1, I]}, where [x|i, f(x|i, t)] represents the fuzzy term, I represents the total number of smart sensors, and f(x|i, t) represents the membership of the fuzzy object x|i relative to the synchronization perception fuzzy set F(t);

[0046] Step S302: Calculate the membership degree based on the jump point and the jump value under the jump point. in, NUM[F(t)] represents the total number of fuzzy terms contained in the synchronous perception fuzzy set F(t);

[0047] According to the above method, the fuzzy reasoning method can combine the scale of synchronization times with the actual scale of equipment operation, and analyze the membership of fuzzy classes at different scales through fuzzy classes.

[0048] Furthermore, the specific implementation process of step S400 includes:

[0049] Step S401: Based on the fuzzy object, analyze and determine whether there is perception synergy between the smart sensors. The judgment formula is:

[0050]

[0051] Where y represents the number of the smart sensor. Indicates the presence of a symbol;

[0052] If the judgment formula is satisfied, it means that there is perception synergy between the i-th smart sensor and the y-th smart sensor;

[0053] Step S402: Update the synchronous perception fuzzy set, retain all the intelligent sensors with perception collaboration, and record the updated synchronous perception fuzzy set as

[0054] Based on the updated synchronous perception fuzzy set, the perception coordination degree between smart sensors is analyzed and calculated. The calculation formula is:

[0055]

[0056] in, It represents the perception coordination degree between smart sensors under the updated synchronous perception fuzzy set;

[0057] Represents the updated synchronous perception fuzzy set The number of fuzzy terms contained in ; L represents the preset perceptual collaborative expectation;

[0058] According to the above method, the judgment formula is used to compare the errors of each actual scale under the synchronization sub-scale, so that the capture of the jump point is more accurate; the perception synergy degree is calculated by improving the probability formula of the normal distribution. When the membership degree is closer to the preset perception synergy expectation, the feedback synergy is better;

[0059] Under the current synchronization number T, the updated synchronization perception fuzzy set is output, denoted as If the perceived synergy If it is less than or equal to the coordination threshold, a monitoring warning prompt is issued, otherwise no monitoring warning prompt is issued.

[0060] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a smart sensor-based equipment operation monitoring system and method provided by the present invention, a synchronous sub-monitoring sequence data cluster is constructed, and the data cluster includes synchronous sub-sequence number information, coding information of the smart sensor and equipment operation parameter information; the monitoring behavior curve of each smart sensor is characterized in the form of coordinate points, and the monitoring behavior curve records the synchronous sub-sequence number information and equipment operation parameter information; the jump points and jump values ​​of the monitoring behavior curve are captured to form a synchronous interval class and generate a fuzzy object; a synchronous perception fuzzy set is formed, and the membership of the fuzzy object is analyzed based on the jump value; it is determined whether there is perception synergy between the smart sensors, the synchronous perception fuzzy set is updated, the perception synergy between the smart sensors is analyzed, and intelligent early warning is performed; further, based on the synchronous perception behavior sequence of the sensor, fuzzy reasoning can be performed while realizing early warning synergy between multiple sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0062] Figure 1 It is a structural schematic diagram of an equipment operation monitoring system based on an intelligent sensor of the present invention;

[0063] Figure 2 It is a schematic diagram of the steps of a device operation monitoring method based on an intelligent sensor of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] See also Figure 1 , In the first embodiment of the present invention: a device operation monitoring system based on an intelligent sensor is provided, the system comprising: a collaborative perception module, a curve analysis module, a fuzzy generation module and a fuzzy processing module;

[0066] The collaborative sensing module is used to construct a synchronous sub-monitoring sequence data cluster, which includes synchronous sub-sequence number information, coding information of smart sensors and equipment operation parameter information;

[0067] Preferably, the collaborative sensing module includes a timer unit and a curve depiction unit;

[0068] The timer unit is used to initialize the time timer and synchronously collect the equipment operation parameters monitored by each smart sensor; the smart sensors are uniformly coded, and one smart sensor is used to monitor one type of equipment operation parameter. Based on the coding information of the smart sensor, the equipment operation parameter set is generated, which is recorded as PS i ={WP it |t∈[1,T]}, where PS i represents the equipment operation parameter set generated by the i-th smart sensor, WP it Indicates the equipment operating parameters monitored by the ith smart sensor at the tth synchronization time, and T indicates the current synchronization number;

[0069] Curve drawing unit, used to establish a two-dimensional coordinate system and generate coordinate points (t, WP it ), smoothly connect each coordinate point in turn, and describe the monitoring behavior curve of the i-th smart sensor, recorded as g(i), and calculate the jump boundary value of the monitoring behavior curve g(i). The jump boundary corresponding to the jump boundary value is recorded as G(i), and the jump boundary G(i) is a straight line parallel to the horizontal axis in the two-dimensional coordinate system;

[0070] The curve analysis module is used to describe the monitoring behavior curve of each smart sensor in the form of coordinate points. The monitoring behavior curve records the synchronization sequence number information and equipment operation parameter information; capture the jump point and jump value of the monitoring behavior curve, and form a synchronization interval class through timestamps to generate fuzzy objects;

[0071] Preferably, the curve analysis module includes a jump capture unit and a fuzzy processing unit;

[0072] The jump capture unit is used to identify and mark the intersection between the monitoring behavior curve g(i) and the jump boundary G(i) in the two-dimensional coordinate system, and record the intersection as the jump point. Any jump point is recorded as x, and the corresponding ordinate value of the jump point x on the monitoring behavior curve g(i) is recorded as the jump value WP. ix ;

[0073] The synchronization class generation unit, based on the synchronization acquisition timestamp, constructs the t-th synchronization interval class during synchronization time t and synchronization time t+1, and a total of T-1 synchronization interval classes are obtained; if x∈[t, t+1], the jump point x is classified into the t-th synchronization interval class, and a fuzzy object is generated, which is recorded as x|i;

[0074] The fuzzy generation module constructs synchronization-aware fuzzy sets based on synchronization interval classes and analyzes the membership of fuzzy objects based on jump values;

[0075] Preferably, the fuzzy generation module includes a synchronous perceptual fuzzy set generation unit and a membership analysis unit;

[0076] The synchronous sensing fuzzy set generation unit generates a synchronous sensing fuzzy set based on the synchronous interval class, which is denoted as F(t) = {[x|i, f(x|i, t)]|x∈[1, T], i∈[1, I]}, where [x|i, f(x|i, t)] represents the fuzzy term, I represents the total number of smart sensors, and f(x|i, t) represents the membership degree of the fuzzy object x|i relative to the synchronous sensing fuzzy set F(t);

[0077] The membership analysis unit calculates the membership degree based on the jump point and the jump value under the jump point. in, NUM[F(t)] represents the total number of fuzzy terms contained in the synchronous perception fuzzy set F(t);

[0078] The fuzzy processing module determines whether there is perception synergy between smart sensors based on fuzzy objects, updates the synchronous perception fuzzy set, analyzes the perception synergy between smart sensors, and performs intelligent early warning.

[0079] Preferably, the fuzzy processing module includes a perception collaborative judgment unit and an intelligent analysis and early warning unit;

[0080] The perception collaboration judgment unit analyzes and judges whether there is perception collaboration between smart sensors based on fuzzy objects. The judgment formula is:

[0081]

[0082] Where y represents the number of the smart sensor. Indicates the presence of a symbol;

[0083] If the judgment formula is satisfied, it means that there is perception synergy between the i-th smart sensor and the y-th smart sensor;

[0084] The intelligent analysis and early warning unit updates the synchronous perception fuzzy set, retains all intelligent sensors with perception collaboration, and records the updated synchronous perception fuzzy set as

[0085] Based on the updated synchronous perception fuzzy set, the perception coordination degree between smart sensors is analyzed and calculated. The calculation formula is:

[0086]

[0087] in, It represents the perception coordination degree between smart sensors under the updated synchronous perception fuzzy set;

[0088] Represents the updated synchronous perception fuzzy set The number of fuzzy terms contained in ; L represents the preset perceptual collaborative expectation;

[0089] Under the current synchronization number T, the updated synchronization perception fuzzy set is output, denoted as If the perceived synergy If it is less than or equal to the coordination threshold, a monitoring warning prompt is issued, otherwise no monitoring warning prompt is issued.

[0090] See also Figure 2 In the second embodiment, a method for monitoring equipment operation based on an intelligent sensor is provided, the method comprising the following steps:

[0091] Step S100: constructing a synchronous sub-monitoring sequence data cluster, the data cluster including synchronous sub-sequence number information, coding information of the smart sensor and equipment operation parameter information;

[0092] Exemplarily, a timer is initialized, and the equipment operating parameters monitored by each smart sensor are collected synchronously; the smart sensors are uniformly coded, and one smart sensor is used to monitor a type of equipment operating parameter. Based on the coding information of the smart sensor, a set of equipment operating parameters is generated, which is recorded as PS i ={WP it |t∈[1,T]}, where PS i represents the equipment operation parameter set generated by the i-th smart sensor, WP it Indicates the equipment operating parameters monitored by the ith smart sensor at the tth synchronization time, and T indicates the current synchronization number;

[0093] Establish a two-dimensional coordinate system and generate coordinate points (t, WP it ), smoothly connect each coordinate point in turn, and describe the monitoring behavior curve of the i-th smart sensor, recorded as g(i), and calculate the jump boundary value of the monitoring behavior curve g(i). The jump boundary corresponding to the jump boundary value is recorded as G(i), and the jump boundary G(i) is a straight line parallel to the horizontal axis in the two-dimensional coordinate system;

[0094] Step S200: Describe the monitoring behavior curve of each smart sensor in the form of coordinate points. The monitoring behavior curve records the synchronization sequence number information and equipment operation parameter information; capture the jump point and jump value of the monitoring behavior curve, and form a synchronization interval class through the timestamp to generate a fuzzy object.

[0095] For example, in a two-dimensional coordinate system, the intersection formed between the monitoring behavior curve g(i) and the jump boundary G(i) is identified and marked, and the intersection is recorded as the jump point. Any jump point is recorded as x, and the ordinate value corresponding to the jump point x on the monitoring behavior curve g(i) is recorded as the jump value WP ix ;

[0096] Based on the synchronization acquisition timestamp, during synchronization times t and t+1, the t-th synchronization interval class is formed, and a total of T-1 synchronization interval classes are obtained; if x∈[t, t+1], the jump point x is classified into the t-th synchronization interval class, and a fuzzy object is generated, which is recorded as x|i;

[0097] Step S300: Based on the synchronization interval class, a synchronization perception fuzzy set is constructed, and the membership of the fuzzy object is analyzed based on the jump value;

[0098] Exemplarily, based on the synchronization interval class, a synchronization perception fuzzy set is generated, which is denoted as F(t)={[x|i, f(x|i, t)]|x∈[1, T], i∈[1, I]}, where [x|i, f(x|i, t)] represents the fuzzy term, I represents the total number of smart sensors, and f(x|i, t) represents the membership of the fuzzy object x|i with respect to the synchronization perception fuzzy set F(t);

[0099] Based on the jump point and the jump value under the jump point, the membership degree is calculated. in, NUM[F(t)] represents the total number of fuzzy terms contained in the synchronous perception fuzzy set F(t);

[0100] Step S400: Based on the fuzzy object, determine whether there is perception synergy between the smart sensors, update the synchronous perception fuzzy set, analyze the perception synergy between the smart sensors, and perform intelligent early warning;

[0101] Exemplarily, based on the fuzzy object, it is analyzed and judged whether there is perception synergy between the smart sensors, and the judgment formula is:

[0102]

[0103] Where y represents the number of the smart sensor. Indicates the presence of a symbol;

[0104] If the judgment formula is satisfied, it means that there is perception synergy between the i-th smart sensor and the y-th smart sensor;

[0105] The synchronous perception fuzzy set is updated, and all intelligent sensors with perception collaboration are retained. The updated synchronous perception fuzzy set is recorded as

[0106] Based on the updated synchronous perception fuzzy set, the perception coordination degree between smart sensors is analyzed and calculated. The calculation formula is:

[0107]

[0108] Among them, S[F(t)] represents the perception coordination degree between smart sensors under the updated synchronous perception fuzzy set;

[0109] NUM[F(t)] represents the number of fuzzy items contained in the updated synchronous perception fuzzy set F(t); L represents the preset perception synergy expectation;

[0110] Under the current synchronization number T, the updated synchronization perception fuzzy set is output, denoted as F(T). If the perception coordination degree S[F(t)] is less than or equal to the coordination threshold, a monitoring warning prompt is issued, otherwise no monitoring warning prompt is issued.

[0111] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0112] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A device operation monitoring method based on intelligent sensors, characterized in that: The method comprises the following steps: Step S100: constructing a synchronous monitoring sequence data cluster, wherein the data cluster includes synchronous sequence number information, coding information of the smart sensor and equipment operation parameter information; Step S200: depicting the monitoring behavior curve of each smart sensor in the form of coordinate points, wherein the monitoring behavior curve records the synchronization sequence number information and the equipment operation parameter information; capturing the jump point and jump value of the monitoring behavior curve, and forming a synchronization interval class through a timestamp to generate a fuzzy object; Step S300: Based on the synchronization interval class, a synchronization perception fuzzy set is constructed, and the membership of the fuzzy object is analyzed based on the jump value; Step S400: Based on the fuzzy object, determine whether there is perception synergy between the smart sensors, update the synchronous perception fuzzy set, analyze the perception synergy between the smart sensors, and perform intelligent early warning; The specific implementation process of step S100 includes: Step S101: Initialize the timer and synchronously collect the equipment operation parameters monitored by each smart sensor; uniformly encode the smart sensors, and one smart sensor is used to monitor one type of equipment operation parameter. Based on the encoding information of the smart sensor, generate a set of equipment operation parameters, recorded as PS i ={WP it |t∈[1,T]}, where PS i represents the equipment operation parameter set generated by the i-th smart sensor, WP it It indicates the equipment operation parameters monitored by the ith smart sensor at the tth synchronization, and T indicates the current synchronization number; Step S102: Establish a two-dimensional coordinate system and generate coordinate points (t, WP it ), smoothly connect each coordinate point in turn, and describe the monitoring behavior curve of the i-th smart sensor, recorded as g(i), and calculate the jump boundary value of the monitoring behavior curve g(i). The jump boundary corresponding to the jump boundary value is recorded as G(i), and the jump boundary G(i) is a straight line parallel to the horizontal coordinate in the two-dimensional coordinate system; The specific implementation process of step S200 includes: Step S201: In a two-dimensional coordinate system, identify and mark the intersection between the monitoring behavior curve g(i) and the jump boundary G(i), record the intersection as the jump point, record any jump point as x, and record the ordinate value corresponding to the jump point x on the monitoring behavior curve g(i) as the jump value WP ix ; Step S202: Based on the synchronous acquisition timestamp, during the period between t and t+1, the t-th synchronous interval class is formed, and a total of T-1 synchronous interval classes are obtained; if x∈[t, t+1], the jump point x is classified into the t-th synchronous interval class, and a fuzzy object is generated, which is recorded as x|i; The specific implementation process of step S300 includes: Step S301: Based on the synchronization interval class, a synchronization perception fuzzy set is generated, which is recorded as F(t)={[x|i, f(x|i, t)]|x∈[1, T], i∈[1, I]}, where [x|i, f(x|i, t)] represents a fuzzy term, I represents the total number of smart sensors, and f(x|i, t) represents the membership of the fuzzy object x|i with respect to the synchronization perception fuzzy set F(t); Step S302: Calculate the membership degree based on the jump point and the jump value under the jump point. in, NUM[F(t)] represents the total number of fuzzy terms contained in the synchronous perception fuzzy set F(t); The specific implementation process of step S400 includes: Step S401: Based on the fuzzy object, analyze and determine whether there is perception synergy between the smart sensors. The judgment formula is: Where y represents the number of the smart sensor. Indicates the presence of a symbol; If the judgment formula is satisfied, it means that there is perception synergy between the i-th smart sensor and the y-th smart sensor; Step S402: Update the synchronous perception fuzzy set, retain all the intelligent sensors with perception collaboration, and record the updated synchronous perception fuzzy set as Based on the updated synchronous perception fuzzy set, the perception coordination degree between smart sensors is analyzed and calculated. The calculation formula is: in, It represents the perception coordination degree between smart sensors under the updated synchronous perception fuzzy set; Represents the updated synchronous perception fuzzy set The number of fuzzy terms contained in ; L represents the preset perceptual collaborative expectation; Under the current synchronization number T, the updated synchronization perception fuzzy set is output, denoted as If the perceived synergy If it is less than or equal to the coordination threshold, a monitoring warning prompt is issued, otherwise no monitoring warning prompt is issued.

2. An equipment operation monitoring system based on intelligent sensors, characterized in that: The system comprises: a collaborative perception module, a curve analysis module, a fuzzy generation module and a fuzzy processing module; The collaborative sensing module is used to construct a synchronous monitoring sequence data cluster, wherein the data cluster includes synchronous sequence number information, coding information of the intelligent sensor and equipment operation parameter information; The curve analysis module is used to describe the monitoring behavior curve of each smart sensor in the form of coordinate points, in which the synchronization sequence number information and equipment operation parameter information are recorded; capture the jump point and jump value of the monitoring behavior curve, and form a synchronization interval class through a timestamp to generate a fuzzy object; The fuzzy generation module forms a synchronization-aware fuzzy set based on the synchronization interval class and analyzes the membership of the fuzzy object based on the jump value; The fuzzy processing module determines whether there is perception synergy between smart sensors based on fuzzy objects, updates the synchronous perception fuzzy set, analyzes the perception synergy between smart sensors, and performs intelligent early warning; The collaborative sensing module includes a timer unit and a curve drawing unit; The timer unit is used to initialize the time timer and synchronously collect the equipment operation parameters monitored by each smart sensor; the smart sensors are uniformly coded, and one smart sensor is used to monitor one type of equipment operation parameter. Based on the coding information of the smart sensor, a set of equipment operation parameters is generated, which is recorded as PS i ={WP it |t∈[1,T]}, where PS i represents the equipment operation parameter set generated by the i-th smart sensor, WP it It indicates the equipment operation parameters monitored by the ith smart sensor at the tth synchronization, and T indicates the current synchronization number; The curve drawing unit is used to establish a two-dimensional coordinate system and generate coordinate points (t, WP it ), smoothly connect each coordinate point in turn, and describe the monitoring behavior curve of the i-th smart sensor, recorded as g(i), and calculate the jump boundary value of the monitoring behavior curve g(i). The jump boundary corresponding to the jump boundary value is recorded as G(i), and the jump boundary G(i) is a straight line parallel to the horizontal coordinate in the two-dimensional coordinate system; The curve analysis module includes a jump capture unit and a fuzzy processing unit; The jump capture unit is used to identify and mark the intersection formed between the monitoring behavior curve g(i) and the jump boundary G(i) in the two-dimensional coordinate system, record the intersection as the jump point, record any jump point as x, and record the ordinate value corresponding to the jump point x on the monitoring behavior curve g(i) as the jump value WP ix ; The fuzzy class processing unit, based on the synchronous acquisition timestamp, forms the t-th synchronous interval class during the period t and t+1, and a total of T-1 synchronous interval classes are obtained; if x∈[t, t+1], the jump point x is classified into the t-th synchronous interval class, and a fuzzy object is generated, which is recorded as x|i; The fuzzy generation module includes a synchronous perception fuzzy set generation unit and a membership analysis unit; The synchronous perception fuzzy set generation unit generates a synchronous perception fuzzy set based on the synchronous interval class, which is recorded as F(t)={[x|i, f(x|i, t)]|x∈[1, T], i∈[1, I]}, wherein [x|i, f(x|i, t)] represents a fuzzy term, I represents the total number of smart sensors, and f(x|i, t) represents the membership degree of the fuzzy object x|i relative to the synchronous perception fuzzy set F(t); The membership analysis unit calculates the membership degree based on the jump point and the jump value under the jump point, in, NUM[F(t)] represents the total number of fuzzy terms contained in the synchronous perception fuzzy set F(t); The fuzzy processing module includes a perception collaborative judgment unit and an intelligent analysis and early warning unit; The perception synergy judgment unit analyzes and judges whether there is perception synergy between smart sensors based on fuzzy objects. The judgment formula is: Where y represents the number of the smart sensor. Indicates the presence of a symbol; If the judgment formula is satisfied, it means that there is perception synergy between the i-th smart sensor and the y-th smart sensor; The intelligent analysis and early warning unit updates the synchronous perception fuzzy set, retains all intelligent sensors with perception collaboration, and records the updated synchronous perception fuzzy set as Based on the updated synchronous perception fuzzy set, the perception coordination degree between smart sensors is analyzed and calculated. The calculation formula is: in, It represents the perception coordination degree between smart sensors under the updated synchronous perception fuzzy set; Represents the updated synchronous perception fuzzy set The number of fuzzy terms contained in ; L represents the preset perceptual collaborative expectation; Under the current synchronization number T, the updated synchronization perception fuzzy set is output, denoted as If the perceived synergy If it is less than or equal to the coordination threshold, a monitoring warning prompt is issued, otherwise no monitoring warning prompt is issued.

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