CN118549035A - Pressure sensor fault testing system and method based on data fusion analysis - Google Patents

Abstract

The invention discloses a pressure sensor fault test system and a pressure sensor fault test method based on data fusion analysis, which relate to the technical field of pressure sensor fault test and solve the technical problem that in the prior art, the pressure sensor cannot be subjected to adaptive analysis and reliability analysis, so that the fault detection efficiency of the pressure sensor is low; and (3) carrying out reliability analysis on the test object in the adaptive analysis qualified period, and deducing whether the pressure data acquisition accuracy of the pressure sensor is qualified or not.

Description

Pressure sensor fault testing system and method based on data fusion analysis

Technical Field

The present invention relates to the technical field of pressure sensor fault testing, and in particular to a pressure sensor fault testing system and method based on data fusion analysis.

Background Art

Pressure sensor fault testing is an important task, which aims to detect and diagnose whether the pressure sensor is operating normally to ensure that it can accurately measure pressure and provide reliable data; common pressure sensor fault testing methods include: Appearance inspection: First check whether the appearance of the sensor has obvious physical problems such as damage, corrosion, loose wiring, etc.; Electrical performance test: Use professional testing equipment to measure the sensor's resistance, capacitance, inductance and other electrical parameters to determine whether its internal circuit is normal.

However, in the prior art, the pressure sensor cannot be tested for faults both internally and externally. Specifically, it is impossible to perform adaptability analysis and reliability analysis on the pressure sensor itself, resulting in low fault detection efficiency of the pressure sensor. At the same time, it is impossible to perform operation detection on the pressure sensor and it is impossible to combine it with maintenance evaluation, resulting in inaccurate fault testing of the pressure sensor.

In view of the above technical defects, a solution is now proposed.

Summary of the invention

The purpose of the present invention is to solve the above-mentioned problems and to propose a pressure sensor fault testing system and method based on data fusion analysis.

The purpose of the present invention can be achieved through the following technical solutions:

The pressure sensor fault test system based on data fusion analysis includes a fault test platform, and the fault test platform is communicatively connected with an internal analysis unit and an external analysis unit:

The internal analysis unit is connected with: an operation detection unit, which is used to set the pressure sensor as a test object, collect the operation buffer data and the operation delay data, and determine whether the operation detection analysis of the test object is abnormal according to the data comparison;

The maintenance capability evaluation unit is used to evaluate the maintenance capability of the test object's fault-free data monitoring, obtain the deviation floating information and deviation impact information, and infer whether the maintenance capability evaluation is normal based on the information analysis;

The external analysis unit is connected to: an adaptability analysis unit, which is used to perform adaptability analysis on the monitoring process of the test object, collect adaptability parameters, substitute them into the formula to obtain the adaptability analysis coefficients of different operating scenarios of the test object, and infer whether the adaptability is normal based on the coefficient comparison;

The reliability analysis unit is used to perform reliability analysis on the test object within the adaptability analysis qualified period, construct a pressure floating curve, and infer whether the reliability is qualified based on the curve analysis.

As a preferred embodiment of the present invention, the operation buffer data and the operation delay data are respectively the floating span value of the buffer time between the pressure perception moment and the pressure data collection moment during the test object data monitoring process, and the cumulative overlapping time of the critical value of the numerical pointer returning period after the pressure data perception collection at the previous moment during the test object data monitoring process.

As a preferred embodiment of the present invention, if the running buffer data exceeds the buffer duration floating span threshold, or the running delay data exceeds the cumulative overlap duration threshold, it is determined that the running detection analysis of the test object is abnormal;

If the running buffer data does not exceed the buffer duration floating span threshold, and the running delay data does not exceed the cumulative overlap duration threshold, then it is determined that the running detection and analysis of the test object is normal.

As a preferred embodiment of the present invention, the deviation floating information and the deviation impact information are respectively the peak growth rate of the test object pressure data numerical acquisition deviation within the qualified deviation range when the test object pressure data acquisition time is within the set time range, and the rising span of the test object data deviation mean when the pressure data numerical acquisition deviation peak increases during the test object pressure data acquisition process.

As a preferred embodiment of the present invention, if the deviation floating information exceeds the peak growth rate threshold, or the deviation impact information exceeds the mean rising span threshold, it is determined that the maintenance ability assessment of the test subject is abnormal;

If the deviation floating information does not exceed the peak growth rate threshold, and the deviation impact information does not exceed the mean rising span threshold, then the test subject's maintenance ability assessment is determined to be normal.

As a preferred embodiment of the present invention, the adaptability parameters include the numerical deviation span of the pressure data at the same time when the test object and the pressure collection body cooperate to collect data, the deviation of the pressure data value at the test object's location and the set qualified numerical threshold when the pressure data are different at different measurement positions of the test object, and the deviation excess value of the deviation of the pressure data value at any non-location and the set qualified numerical threshold, the error frequency of the pressure data value collection error frequency when the temperature of the pressure collection body is affected by the fluctuation of ambient temperature when the test object and the pressure collection body cooperate to collect data, and the error frequency ratio of the pressure data value collection error frequency when the temperature of the pressure collection body is not affected by the fluctuation of ambient temperature, and the maximum difference between any two moments of the pressure data value collection deviation of the test object under different ambient temperatures of the same pressure collection body.

As a preferred embodiment of the present invention, if the adaptability analysis coefficient of the test object in different operating scenarios exceeds the adaptability analysis coefficient threshold, the adaptability analysis of the test object is judged to be abnormal; if the adaptability analysis coefficient of the test object in different operating scenarios does not exceed the adaptability analysis coefficient threshold, the adaptability analysis of the test object is judged to be normal.

As a preferred embodiment of the present invention, a pressure collection period of the test object is set and marked as a pressure collection period, and the pressure collection period is divided into a number of collection time points according to the data collection time interval threshold of the test object. After the pressure collection period is set, the pressure values at each collection time point in the pressure collection period are collected and a pressure collection period set is constructed. As the number of subsets in the pressure collection period set increases, a pressure floating curve is constructed with the collection time point as the horizontal coordinate and the pressure value as the vertical coordinate. After the curve is constructed, the pressure floating curve is analyzed, and the pressure value in the pressure floating curve in the pressure collection period is calibrated from zero increase to zero decrease as a single collection cycle, and the pressure curve of the collection cycle is marked as a periodic curve.

As a preferred embodiment of the present invention, the peak value of any period curve in the pressure floating curve within the pressure collection period and the construction area area of the corresponding single period curve are obtained. If the peak value of any period curve in the pressure floating curve exceeds the peak value threshold, and the construction area area of the corresponding single period curve exceeds the regional area threshold, the current collection period is marked as a high floating span and low speed pressure collection period; if the peak value of any period curve in the pressure floating curve exceeds the peak value threshold, and the construction area area of the corresponding single period curve does not exceed the regional area threshold, the current collection period is marked as a high floating span and high speed pressure collection period; if the peak value of any period curve in the pressure floating curve does not exceed the peak value threshold, and the construction area area of the corresponding single period curve exceeds the regional area threshold, the current collection period is marked as a low floating span and low speed pressure collection period; if the peak value of any period curve in the pressure floating curve does not exceed the peak value threshold, and the construction area area of the corresponding single period curve does not exceed the regional area threshold, the current collection period is marked as a low floating span and high speed pressure collection period;

The actual pressure fluctuation trend of the pressure sampling subject corresponding to each sampling period is compared with the sampling period type. If the comparison is inconsistent, a reliability abnormal signal is generated; if the comparison is consistent, a reliability normal signal is generated.

As a preferred embodiment of the present invention, a pressure sensor fault testing method based on data fusion analysis, the specific testing method steps are as follows:

Step 1: Operation detection, which is used to set the pressure sensor as the test object, collect the operation buffer data and the operation delay data, and determine whether the operation detection analysis of the test object is abnormal based on data comparison;

Step 2: Maintenance capability assessment is used to assess the maintenance capability of the test object for fault-free data monitoring, obtain the deviation floating information and deviation impact information, and infer whether the maintenance capability assessment is normal based on information analysis;

Step 3: Adaptability analysis is used to conduct adaptability analysis on the monitoring process of the test object. The adaptability parameters are collected and substituted into the formula to obtain the adaptability analysis coefficients of different operating scenarios of the test object. Whether the adaptability is normal is inferred based on the coefficient comparison.

Compared with the prior art, the present invention has the following beneficial effects:

1. In the present invention, an operation detection is performed on the pressure sensor, and the detection is performed based on the internal operation of the pressure sensor to infer whether the current operation efficiency of the pressure sensor meets the actual demand, so as to avoid the risk of abnormal data monitoring caused by operation deviation, resulting in low efficiency of fault testing of the pressure sensor and inability to predict faults in a timely manner; the maintenance capability of the pressure sensor for fault-free data monitoring is evaluated to ensure that the pressure sensor can collect pressure parameters in an efficient operating state during a fault-free period, thereby improving the operation efficiency of the pressure sensor; at the same time, the operation capability of the pressure sensor is accurately evaluated, so as to achieve timely monitoring of the operation efficiency and adjustment, and ensure the stability of pressure data collection.

2. In the present invention, an adaptability analysis is performed on the monitoring process of the test object, and the operating efficiency of the current test object is inferred through the adaptability analysis while the operating performance of the test object is tested. If the current adaptability analysis is abnormal and the environment is a non-adaptive environment, fault prediction is performed in a timely manner, thereby improving the accuracy and timeliness of fault testing; a reliability analysis is performed on the test object within the period of qualified adaptability analysis to infer whether the pressure data collection accuracy of the pressure sensor is qualified, thereby improving the fault testing efficiency of the pressure sensor and ensuring the collection efficiency of the pressure sensor and the accuracy of the collected data.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

FIG1 is an overall principle block diagram of the present invention;

FIG2 is a functional block diagram of a first embodiment of the present invention;

FIG3 is a principle block diagram of the second embodiment of the present invention.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

Please refer to FIG. 1 , a pressure sensor fault test system based on data fusion analysis includes a fault test platform, wherein the fault test platform is communicatively connected to an internal analysis unit and an external analysis unit;

Embodiment 1

The fault test platform generates an internal analysis instruction to the internal analysis unit for analysis, wherein the internal analysis unit is suitable for detecting the internal operation of the pressure sensor itself to infer the faults existing in the current operation stage of the pressure sensor and to give a timely warning; please refer to FIG2 , the fault test platform and the internal analysis unit are also communicatively connected with an operation detection unit and a maintenance capability evaluation unit;

The internal analysis unit generates an operation detection signal and sends the operation detection signal to the operation detection unit. After receiving the operation detection signal, the operation detection unit performs an operation detection on the pressure sensor. According to the internal operation of the pressure sensor, it is detected to infer whether the current operation efficiency of the pressure sensor meets the actual demand, so as to avoid the risk of abnormal data monitoring caused by operation deviation, resulting in low fault test efficiency of the pressure sensor and failure to predict faults in time.

The pressure sensor is set as the test object, and the buffer time floating span value between the pressure feeling moment and the pressure data collection moment in the test object data monitoring process is collected. At the same time, the cumulative overlapping time of the pressure data collection execution moment in the test object data monitoring process at the critical value of the numerical pointer returning to the position after the pressure data feeling collection at the previous moment is obtained, and the buffer time floating span value between the pressure feeling moment and the pressure data collection moment in the test object data monitoring process and the cumulative overlapping time of the pressure data collection execution moment in the test object data monitoring process at the critical value of the numerical pointer returning to the position after the pressure data feeling collection at the previous moment are marked as running buffer data and running delay data, respectively, and compared with the buffer time floating span threshold and the cumulative overlapping time threshold, respectively:

If the buffer time floating span value between the pressure feeling moment and the pressure data collection moment during the test object data monitoring process exceeds the buffer time floating span threshold, or the cumulative overlapping time of the pressure data collection execution moment at the critical value of the numerical pointer returning period after the pressure data feeling collection at the previous moment during the test object data monitoring process exceeds the cumulative overlapping time threshold, then the operation detection analysis of the test object is determined to be abnormal, an operation detection abnormality signal is generated and the operation detection abnormality signal is sent to the fault test platform. After receiving the operation detection abnormality signal, the fault test platform debugs the test object and increases the real-time data collection speed to reduce the delay error of data collection;

If the buffer time floating span value between the pressure feeling moment and the pressure data collection moment during the test object data monitoring process does not exceed the buffer time floating span threshold, and the cumulative overlap time of the critical value of the numerical pointer returning period after the pressure data feeling collection at the previous moment during the test object data monitoring process does not exceed the cumulative overlap time threshold, then it is determined that the operation detection analysis of the test object is normal, and a normal operation detection signal is generated and sent to the fault test platform;

At the same time, a maintenance capability evaluation signal is generated and sent to the maintenance capability evaluation unit. After receiving the maintenance capability evaluation signal, the maintenance capability evaluation unit evaluates the maintenance capability of the pressure sensor for fault-free data monitoring, so as to ensure that the pressure sensor can collect pressure parameters in a highly efficient operating state during the fault-free period, thereby improving the operating efficiency of the pressure sensor. At the same time, the operating capability of the pressure sensor is accurately evaluated, so as to achieve timely monitoring of the operating efficiency and adjustment, thereby ensuring the stability of pressure data collection;

The peak growth rate of the pressure data value collection deviation of the test object is within the qualified deviation range when the pressure data collection time of the test object is within the set time range, and the rising span of the mean value of the data deviation of the test object is obtained when the peak value of the pressure data value collection deviation increases during the pressure data collection process of the test object. The peak growth rate of the pressure data value collection deviation of the test object is within the qualified deviation range when the pressure data collection time of the test object is within the set time range, and the rising span of the mean value of the data deviation of the test object when the peak value of the pressure data value collection deviation increases during the pressure data collection process of the test object are marked as deviation floating information and deviation impact information, respectively, and compared with the peak growth rate threshold and the mean rising span threshold, respectively:

If the peak growth rate of the pressure data value collection deviation of the test object within the qualified deviation range exceeds the peak growth rate threshold when the pressure data collection time of the test object is within the set time range, or the rising span of the mean value of the test object data deviation exceeds the mean rising span threshold when the peak value of the pressure data value collection deviation increases during the pressure data collection process of the test object, then it is determined that the maintenance capability assessment of the test object is abnormal, and an operation risk signal is generated and sent to the fault test platform. After receiving the operation risk signal, the fault test platform continuously monitors the pressure data fluctuation of the current test object, performs data collection control when the fluctuation is abnormal, and performs performance testing on the test object;

If the peak growth rate of the pressure data value collection deviation of the test object within the qualified deviation range does not exceed the peak growth rate threshold when the pressure data collection time of the test object is within the set time range, and the rising span of the mean value of the data deviation of the test object during the pressure data collection process of the test object when the peak value of the pressure data value collection deviation increases does not exceed the mean rising span threshold, then it is determined that the maintenance capability assessment of the test object is normal, and an operation safety signal is generated and sent to the fault test platform;

Embodiment 2

The fault test platform generates an external analysis instruction to the external analysis unit, and the external analysis unit performs an external influence analysis on the pressure sensor to infer whether the adaptability and reliability of the pressure sensor are qualified under the influence of the environment. Please refer to FIG3 , the fault test platform is connected to the external analysis unit in communication with the adaptability analysis unit and the reliability analysis unit;

After receiving the external analysis instruction, the external analysis unit generates an adaptability analysis signal and sends the adaptability analysis signal to the adaptability analysis unit. After receiving the adaptability analysis signal, the adaptability analysis unit performs adaptability analysis on the monitoring process of the test object, infers the current operating efficiency of the test object through the adaptability analysis, and performs operating performance detection on the test object. If the current adaptability analysis is abnormal and the environment is a non-adaptive environment, fault prediction is performed in time, thereby improving the accuracy and timeliness of fault testing;

The real-time pressure collection object of the test object is marked as a pressure collection subject, where the pressure collection subject can be liquid or gas, etc.; the numerical deviation span of the pressure data at the same time when the test object and the pressure collection subject cooperate in the collection is obtained, and at the same time, the deviation of the pressure data value at the test object's location and the set qualified value threshold value, and the deviation of the pressure data value at any non-location and the set qualified value threshold value are obtained, and the deviation value is calculated by difference, and the label PDC is set; and the numerical deviation span of the pressure data at the same time when the test object and the pressure collection subject cooperate in the collection is marked as PCK;

Obtain the error frequency numerical ratio of the pressure data value acquisition error frequency when the temperature of the pressure collection body is affected by the ambient temperature fluctuation when the test object cooperates with the pressure collection body for acquisition, and the error frequency numerical ratio of the pressure data value acquisition error frequency when the temperature of the pressure collection body is affected by the ambient temperature fluctuation when the test object cooperates with the pressure collection body for acquisition, and the error frequency numerical ratio of the pressure data value acquisition error frequency when the temperature of the pressure collection body is affected by the ambient temperature fluctuation when the test object cooperates with the pressure collection body for acquisition, and the error frequency numerical ratio of the pressure data value acquisition error frequency when the temperature of the pressure collection body is affected by the ambient temperature fluctuation when the temperature of the pressure collection body is not affected by the ambient temperature fluctuation is marked as SVZ; at the same time, obtain the maximum difference between any two moments of the pressure data value acquisition deviation of the test object at different ambient temperatures of the same pressure collection body, and mark the maximum difference between any two moments of the pressure data value acquisition deviation of the test object at different ambient temperatures of the same pressure collection body as ZDC;

The above collected data are uniformly marked as adaptability parameters and substituted into the formula to obtain the adaptability analysis coefficient SY of different operating scenarios of the test object. The formula is:

, where gh1, gh2, gh3, and gh4 are respectively the numerical deviation span of the pressure data at the same moment, the numerical deviation span of the corresponding pressure data at the same moment, the numerical ratio of the error frequency, and the preset proportional coefficient of the maximum difference between any two moments, and β is used as the error correction factor, and its value is 0.98;

Compare the adaptability analysis coefficient SY of the test object in different operating scenarios with the adaptability analysis coefficient threshold:

If the adaptability analysis coefficient SY of different operating scenarios of the test object exceeds the adaptability analysis coefficient threshold, the adaptability analysis of the test object is determined to be abnormal, and an adaptability warning signal is generated and sent to the fault test platform. After receiving the adaptability warning signal, the fault test platform controls the operating environment of the test object and performs environmental monitoring when the test object is running. If abnormalities occur, timely operation adjustments are made;

If the adaptability analysis coefficient SY of different operation scenarios of the test object does not exceed the adaptability analysis coefficient threshold, the adaptability analysis of the test object is determined to be normal, and an adaptability qualified signal is generated and sent to the fault test platform;

At the same time, a reliability analysis signal is generated and sent to the reliability analysis unit. After receiving the reliability analysis signal, the reliability analysis unit performs reliability analysis on the test object within the qualified period of adaptability analysis to infer whether the pressure data acquisition accuracy of the pressure sensor is qualified, thereby improving the fault test efficiency of the pressure sensor and ensuring the acquisition efficiency of the pressure sensor and the accuracy of the acquired data;

The pressure collection period of the test object is set and marked as the pressure collection period. The pressure collection period is divided into several collection time points according to the data collection time interval threshold of the test object. After the pressure collection period is set, the pressure values of each collection time point in the pressure collection period are collected and a pressure collection period set is constructed. As the number of subsets in the pressure collection period set increases, a pressure floating curve is constructed with the collection time point as the horizontal coordinate and the pressure value as the vertical coordinate;

After completing the curve construction, the pressure floating curve is analyzed, and the pressure value in the pressure floating curve during the pressure collection period from zero increase to zero decrease is calibrated as a single acquisition cycle, and the pressure curve of the acquisition period is marked as a periodic curve, and the peak value of any periodic curve in the pressure floating curve during the pressure collection period and the construction area area of the corresponding single periodic curve are obtained. If the peak value of any periodic curve in the pressure floating curve exceeds the peak threshold, and the construction area area of the corresponding single periodic curve exceeds the regional area threshold, the current acquisition cycle is marked as a high floating span and low speed pressure acquisition cycle; if the peak value of any periodic curve in the pressure floating curve exceeds the peak threshold, and the construction area area of the corresponding single periodic curve does not exceed the regional area threshold, the current acquisition cycle is marked as a high floating span and high speed pressure acquisition cycle; if the peak value of any periodic curve in the pressure floating curve does not exceed the peak threshold, and the construction area area of the corresponding single periodic curve exceeds the regional area threshold, the current acquisition cycle is marked as a low floating span and low speed pressure acquisition cycle; if the peak value of any periodic curve in the pressure floating curve does not exceed the peak threshold, and the construction area area of the corresponding single periodic curve does not exceed the regional area threshold, the current acquisition cycle is marked as a low floating span and high speed pressure acquisition cycle;

The actual pressure fluctuation trend of the pressure collection subject corresponding to each collection cycle is compared with the collection cycle type. If the comparison is inconsistent, a reliability abnormality signal is generated and sent to the fault test platform, and the fault test platform performs equipment maintenance on the test object; the specific phenomenon of inconsistent comparison is: when the collection cycle has a high floating span, the peak value of the actual pressure floating trend does not exceed the set threshold, and when the speed of the actual pressure floating trend is high, the collection cycle is a low speed type, etc.;

If the comparison is consistent, a normal reliability signal is generated and sent to the fault test platform;

The pressure sensor fault testing method based on data fusion analysis, the specific testing method steps are as follows:

Step 1: Operation detection, which is used to set the pressure sensor as the test object, collect the operation buffer data and the operation delay data, and determine whether the operation detection analysis of the test object is abnormal based on data comparison;

Step 2: Maintenance capability assessment is used to assess the maintenance capability of the test object for fault-free data monitoring, obtain the deviation floating information and deviation impact information, and infer whether the maintenance capability assessment is normal based on information analysis;

Step 3: Adaptability analysis is used to conduct adaptability analysis on the monitoring process of the test object, collect adaptability parameters, substitute them into the formula to obtain the adaptability analysis coefficients of different operating scenarios of the test object, and infer whether the adaptability is normal based on the coefficient comparison;

Step 4: Reliability analysis is used to conduct reliability analysis on the test object within the qualified period of adaptability analysis, construct a pressure floating curve, and infer whether the reliability is qualified based on the curve analysis;

The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions;

When the present invention is in use, the operation detection unit is used to set the pressure sensor as the test object, collect the operation buffer data and the operation delay data, and determine whether the operation detection analysis of the test object is abnormal based on the data comparison; the maintenance capability evaluation unit is used to evaluate the maintenance capability of the fault-free data monitoring of the test object, obtain the deviation floating information and the deviation impact information, and infer whether the maintenance capability evaluation is normal based on the information analysis; the adaptability analysis unit is used to perform adaptability analysis on the monitoring process of the test object, collect the adaptability parameters, substitute them into the formula to obtain the adaptability analysis coefficients of different operation scenarios of the test object, and infer whether the adaptability is normal based on the coefficient comparison; the reliability analysis unit is used to perform reliability analysis on the test object within the adaptability analysis qualified period, construct a pressure floating curve, and infer whether the reliability is qualified based on the curve analysis.

The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims (10)

1. A pressure sensor fault testing system based on data fusion analysis, characterized in that it includes a fault testing platform, which is communicatively connected to an internal analysis unit and an external analysis unit: The internal analysis unit is connected with: an operation detection unit, which is used to set the pressure sensor as a test object, collect the operation buffer data and the operation delay data, and determine whether the operation detection analysis of the test object is abnormal according to the data comparison; The maintenance capability evaluation unit is used to evaluate the maintenance capability of the test object's fault-free data monitoring, obtain the deviation floating information and deviation impact information, and infer whether the maintenance capability evaluation is normal based on the information analysis; The external analysis unit is connected to: an adaptability analysis unit, which is used to perform adaptability analysis on the monitoring process of the test object, collect adaptability parameters, substitute them into the formula to obtain the adaptability analysis coefficients of different operating scenarios of the test object, and infer whether the adaptability is normal based on the coefficient comparison; The reliability analysis unit is used to perform reliability analysis on the test object within the adaptability analysis qualified period, construct a pressure floating curve, and infer whether the reliability is qualified based on the curve analysis. 2. The pressure sensor fault testing system based on data fusion analysis according to claim 1 is characterized in that the running buffer data and the running delay data are respectively the floating span value of the buffer time between the pressure perception moment and the pressure data collection moment in the test object data monitoring process, and the cumulative overlapping time of the critical value of the numerical pointer returning period after the pressure data perception collection at the previous moment when the pressure data collection execution moment in the test object data monitoring process is at. 3. The pressure sensor fault testing system based on data fusion analysis according to claim 2 is characterized in that if the running buffer data exceeds the buffer time floating span threshold, or the running delay data exceeds the cumulative overlap time threshold, it is determined that the running detection analysis of the test object is abnormal; If the running buffer data does not exceed the buffer duration floating span threshold, and the running delay data does not exceed the cumulative overlap duration threshold, then it is determined that the running detection and analysis of the test object is normal. 4. The pressure sensor fault testing system based on data fusion analysis according to claim 1 is characterized in that the deviation floating information and the deviation impact information are respectively the peak growth rate of the test object pressure data numerical acquisition deviation within the qualified deviation range when the test object pressure data acquisition time is within the set time range, and the rising span of the test object data deviation mean when the pressure data numerical acquisition deviation peak increases during the test object pressure data acquisition process. 5. The pressure sensor fault testing system based on data fusion analysis according to claim 4 is characterized in that if the deviation floating information exceeds the peak growth rate threshold, or the deviation impact information exceeds the mean rising span threshold, it is determined that the maintenance capability assessment of the test object is abnormal; If the deviation floating information does not exceed the peak growth rate threshold, and the deviation impact information does not exceed the mean rising span threshold, then the test subject's maintenance ability assessment is determined to be normal. 6. The pressure sensor fault testing system based on data fusion analysis according to claim 1 is characterized in that the adaptability parameters include the numerical deviation span of the pressure data at the same time when the test object and the pressure sampling body cooperate to collect data, the deviation of the pressure data value at the test object's location and the set qualified numerical threshold when the pressure data are different when the test object's measurement positions are different, and the deviation excess value of the deviation of the pressure data value at any non-location and the set qualified numerical threshold, the error frequency of the pressure data value collection error frequency when the temperature of the pressure sampling body is affected by the fluctuation of the ambient temperature when the test object and the pressure sampling body cooperate to collect data, and the error frequency ratio of the pressure data value collection error frequency when the temperature of the pressure sampling body is not affected by the fluctuation of the ambient temperature, and the maximum difference between any two moments of the pressure data value collection deviation of the test object under different ambient temperatures of the same pressure sampling body. 7. The pressure sensor fault testing system based on data fusion analysis according to claim 6 is characterized in that if the adaptability analysis coefficient of different operating scenarios of the test object exceeds the adaptability analysis coefficient threshold, the adaptability analysis of the test object is judged to be abnormal; if the adaptability analysis coefficient of different operating scenarios of the test object does not exceed the adaptability analysis coefficient threshold, the adaptability analysis of the test object is judged to be normal. 8. The pressure sensor fault testing system based on data fusion analysis according to claim 1 is characterized in that a pressure collection period of the test object is set and marked as a pressure collection period, the pressure collection period is divided into a number of collection time points according to the data collection time interval threshold of the test object, after the pressure collection period is set, the pressure values at each collection time point in the pressure collection period are collected and a pressure collection period set is constructed, and as the number of subsets in the pressure collection period set increases, a pressure floating curve is constructed with the collection time point as the horizontal coordinate and the pressure value as the vertical coordinate; after the curve construction is completed, the pressure floating curve is analyzed, and the pressure value in the pressure floating curve in the pressure collection period is calibrated from zero increase to zero drop as a single collection cycle, and the pressure curve of the collection cycle is marked as a periodic curve. 9. The pressure sensor fault testing system based on data fusion analysis according to claim 8 is characterized in that the peak value of any period curve in the pressure floating curve within the pressure collection period and the construction area area of the corresponding single period curve are obtained. If the peak value of any period curve in the pressure floating curve exceeds the peak value threshold, and the construction area area of the corresponding single period curve exceeds the area area threshold, the current collection period is marked as a high floating span and low speed pressure collection period; if the peak value of any period curve in the pressure floating curve exceeds the peak value threshold, and the construction area area of the corresponding single period curve does not exceed the area area threshold, the current collection period is marked as a high floating span and high speed pressure collection period; if the peak value of any period curve in the pressure floating curve does not exceed the peak value threshold, and the construction area area of the corresponding single period curve exceeds the area area threshold, the current collection period is marked as a low floating span and low speed pressure collection period; if the peak value of any period curve in the pressure floating curve does not exceed the peak value threshold, and the construction area area of the corresponding single period curve does not exceed the area area threshold, the current collection period is marked as a low floating span and high speed pressure collection period; The actual pressure fluctuation trend of the pressure sampling subject corresponding to each sampling period is compared with the sampling period type. If the comparison is inconsistent, a reliability abnormal signal is generated; if the comparison is consistent, a reliability normal signal is generated. 10. A pressure sensor fault testing method based on data fusion analysis, characterized by being a pressure sensor fault testing system based on data fusion analysis as described in any one of claims 1 to 9.