Intelligent interaction method and system for aviation test data based on arinc429 bus

Abstract

This invention relates to the field of avionics test data processing technology, and discloses an intelligent interaction method and system for avionics test data based on the ARINC429 bus. The method includes parsing bus data words, matching them with a tag rule base to complete adaptive code restoration; comparing pressure thresholds and determining the duration to generate anomaly markers, binding serial numbers to a connector location database; constructing a reverse mapping chain, injecting abnormal values ​​to form an associated structure; retrieving projected values ​​from a 3D base map and enhancing anomaly visualization, integrating and generating monitoring view data; extracting the 3D coordinates of the anomaly and tracing them along the mapping chain, matching them with a fault type table to obtain the location result. This method constructs a full-link collaborative mechanism from data parsing to physical location, effectively solving the problems of difficult anomaly tracing and low interaction efficiency, and significantly improving the accuracy and fault diagnosis efficiency of avionics stress testing.

Description

Technical Field

[0001] This invention relates to the field of avionics test data processing technology, and in particular to an intelligent interaction method and system for avionics test data based on the ARINC429 bus. Background Technology

[0002] With the rapid development of the aviation industry, the requirements for the real-time performance and accuracy of pressure parameters in aircraft health monitoring are increasing. As a core transmission link in avionics, the ARINC429 bus's data parsing and anomaly identification capabilities are directly related to flight safety and maintenance efficiency.

[0003] Existing technologies lack unified adaptation rules for parsing ARINC429 bus data. Two's complement restoration is prone to accuracy deviations, and anomaly detection relies heavily on single-frame threshold comparisons, making it susceptible to false alarms due to transient interference. Furthermore, data acquisition and control are not directly related to physical connector locations or aircraft partitions, making it impossible to map numerical anomalies to specific spatial locations. The visualization is simplistic and makes it difficult to quickly pinpoint the source of the fault.

[0004] Therefore, existing technologies suffer from the problem of being unable to accurately trace the source of pressure anomalies due to the disconnect between data processing and spatial positioning. Summary of the Invention

[0005] This invention provides an intelligent interaction method and system for aviation test data based on the ARINC429 bus, in order to solve the problem in the prior art that the disconnect between data processing and spatial positioning makes it impossible to achieve accurate tracing and visualization interaction of pressure data anomalies.

[0006] Firstly, to address the aforementioned technical problems, this invention provides a method for intelligent interaction of aerospace test data based on the ARINC429 bus, comprising:

[0007] Data words are obtained from the ARINC429 bus, and bit segmentation and masking are performed on the data words to obtain the tag identifier and the complement value. The label identifier is matched against a preset label rule library, and the sensor function category and effective length of data bits are extracted based on the matching result. Based on the sensor function category and the effective length of the data bits, the two's complement value is truncated by bit masking and then the sign is restored to obtain the pressure engineering quantity value. Compare the pressure engineering quantity value with the preset pressure threshold, mark the excess value that meets the abnormal duration judgment as an abnormal mark, assign a tag serial number to the abnormal mark, and integrate the abnormal mark and the tag serial number to obtain the abnormal engineering quantity value. The abnormal engineering quantity values ​​are matched with a preset joint location database to obtain joint attribute features. Based on the joint attribute features, the joint number, aircraft partition index and joint coordinate index are integrated to obtain the abnormal joint location. Construct a reverse mapping chain from the abnormal connector location to the tag serial number, and inject the reverse mapping chain into the abnormal engineering quantity value to obtain the associated corresponding structure; The three-dimensional model base map is retrieved according to the associated structure, and the abnormal engineering quantity value is projected onto the three-dimensional model base map to obtain the abnormal projection feature. The abnormal projection feature is integrated with the abnormal engineering quantity value to obtain the monitoring view data. Extract the abnormal three-dimensional coordinate features from the monitoring view data, trace the joint and partition features along the reverse mapping chain, match the joint and partition features with a preset fault type table, and obtain the fault location result.

[0008] Secondly, the present invention provides an intelligent interaction system for aviation test data based on the ARINC429 bus, comprising: The data parsing module is used to obtain data words from the ARINC429 bus, perform bit segmentation and masking processing on the data words, and obtain tag identifiers and complement values; The tag matching module is used to match the tag identifier with a preset tag rule library, and extract the sensor function category and the effective length of the data bits based on the matching result; The numerical conversion module is used to perform bit masking and sign restoration on the two's complement value according to the sensor function category and the effective length of the data bits to obtain the pressure engineering quantity value. An anomaly determination module is used to compare the pressure engineering quantity value with a preset pressure threshold, mark the out-of-limit value that meets the anomaly duration determination as an anomaly mark, assign a tag serial number to the anomaly mark, and integrate the anomaly mark and the tag serial number to obtain the abnormal engineering quantity value. The location index module is used to match the abnormal engineering quantity values ​​with a preset joint location database to obtain joint attribute features, and to integrate the joint number, aircraft partition index and joint coordinate index according to the joint attribute features to obtain the abnormal joint location. The mapping construction module is used to construct a reverse mapping chain from the abnormal connector location to the tag sequence number, and inject the reverse mapping chain into the abnormal engineering quantity value to obtain the associated corresponding structure; An anomaly visualization module is used to retrieve a 3D model base map based on the associated corresponding structure, project the abnormal engineering quantity values ​​onto the 3D model base map to obtain anomaly projection features, and integrate the anomaly projection features with the abnormal engineering quantity values ​​to obtain monitoring view data. The fault location module is used to extract the abnormal three-dimensional coordinate features of the monitoring view data, trace the joint and partition features along the reverse mapping chain, and match the joint and partition features with a preset fault type table to obtain the fault location result.

[0009] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention achieves precise decoupling of 32-bit data words through bit segmentation and masking, and combines preset label rule library to match and extract sensor parameters and complete adaptive restoration of complement symbol, breaking through the accuracy deviation and adaptation limitations of traditional segmented parsing, and realizing efficient and accurate parsing of ARINC429 bus pressure data.

[0010] (2) The present invention introduces an abnormal duration determination mechanism to filter out transient interference and bind the tag serial number. Combined with the abnormal joint position, a reverse mapping chain from the tag identifier to the three-dimensional coordinate is constructed, breaking the disconnect between data and physical space, and realizing the physical space accurate traceability of pressure abnormal data.

[0011] (3) The present invention retrieves the pressure values ​​of the three-dimensional model base map based on the associated corresponding structure and enhances the visualization of abnormal areas. It integrates multi-sensor data to form a converged monitoring view, which overcomes the defects of traditional single visualization and lagging fault location, and realizes the visualization interaction of pressure anomalies and precise fault location at the joint level. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the intelligent interaction method for aviation test data based on the ARINC429 bus provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the intelligent interactive system for aviation test data based on the ARINC429 bus provided in the second embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Reference Figure 1 The first embodiment of the present invention provides an intelligent interaction method for aviation test data based on the ARINC429 bus, comprising the following steps: S1. Obtain a data word from the ARINC429 bus, perform bit segmentation and masking on the data word to obtain the tag identifier and complement value; S2, match the label identifier with a preset label rule library, and extract the sensor function category and effective length of data bits based on the matching result; S3. Based on the sensor function category and the effective length of the data bits, the two's complement value is truncated by bit masking and then the sign is restored to obtain the pressure engineering quantity value. S4. Compare the pressure engineering quantity value with the preset pressure threshold, mark the over-limit value that meets the abnormal duration judgment as an abnormal mark, assign a tag serial number to the abnormal mark, and integrate the abnormal mark and the tag serial number to obtain the abnormal engineering quantity value. S5, match the abnormal engineering quantity value with the preset joint location database to obtain joint attribute features, and integrate the joint number, aircraft partition index and joint coordinate index according to the joint attribute features to obtain the abnormal joint location. S6, construct a reverse mapping chain from the abnormal connector location to the tag serial number, inject the reverse mapping chain into the abnormal engineering quantity value, and obtain the associated corresponding structure; S7. Retrieve the three-dimensional model base map according to the associated corresponding structure, project the abnormal engineering quantity value onto the three-dimensional model base map to obtain abnormal projection features, and integrate the abnormal projection features with the abnormal engineering quantity value to obtain monitoring view data. S8. Extract the abnormal three-dimensional coordinate features of the monitoring view data, trace the joint and partition features along the reverse mapping chain, match the joint and partition features with a preset fault type table, and obtain the fault location result.

[0015] In step S1, a data word is obtained from the ARINC429 bus, and bit segmentation and masking are performed on the data word to obtain the tag identifier and complement value, including: S11, Receive a differential voltage pulse sequence from the ARINC429 bus, and perform sampling and assembly processing on the differential voltage pulse sequence to obtain data words; S12, perform masking on the tag identifier bit field of the data word to obtain tag mask features, and perform shift processing according to the tag mask features to obtain tag identifier; S13, apply mask extraction processing to the two's complement value bit segment of the data word and mask the parity check bit to obtain the two's complement value.

[0016] It should be noted that the differential voltage pulse sequence is acquired using a differential receiver conforming to the ARINC429 standard. This receiver has a differential input voltage range of -10V to +10V, a standard sampling bit rate of 100kbps, and is compatible with extended rates of 50kbps and 12.5kbps. The sampling clock is provided by a 40MHz crystal oscillator frequency divider. The sampling logic uses an 8-point majority voting method to determine the level of each bit, eliminating erroneous sampling caused by electromagnetic interference. Masking and shifting processes right-shift the high-order bits of the 32-bit data word by 29 bits and performing a bitwise AND operation with 0x7 to extract a 3-bit tag identifier. For the low-order bits, a 0x0FFFFFFF mask is applied to mask the tag bits (bits 31-29) and the parity bit (bit 0) to extract a 28-bit two's complement value (bits 28 to 1). The 32-bit data word set is stored in the structure of microsecond-level timestamp - 3-bit tag identifier - 28-bit two's complement value. The parameter settings are based on the mainstream transmission rate of avionics systems, which is matched to 100kbps. The 8-point majority voting method per bit can reduce the bit error rate to below 10^-9 in the complex electromagnetic environment of aviation. The three-dimensional structure can realize accurate data traceability and subsequent correlation processing.

[0017] For example, the differential voltage pulse sequence received by the ARINC429 bus channel of the aircraft pressurization system is sampled and assembled to obtain a 32-bit data word in binary format: 10110001001011011001011001101001 (bits 31-29 are 3-bit tag bits, bits 28-1 are 28-bit complement bits, and bit 0 is a parity bit). After masking and shifting, the 3-bit tag identifier 101 and the 28-bit complement value 100010010110110010110011010 are separated. Combined with the microsecond-level timestamp 1718956324892μs, the data word is stored in a three-dimensional structure of microsecond-level timestamp - 3-bit tag identifier - 28-bit complement value and incorporated into the 32-bit data word set.

[0018] It should be noted that the mask uses the hexadecimal 0x7 corresponding to binary 111. The shifting process involves shifting the entire 32-bit data word 29 bits to the right, moving bits 31 to 29 to the lowest three bits, and then performing a bitwise AND operation with the mask 0x7 to remove interference from other bit segments and retain only the tag identification bit segment. The ARINC429 bus standard clearly specifies that bits 31 to 29 are the tag identification bits. This operation can accurately extract the tag bit information defined by the standard.

[0019] For example, if the high-order bit of a 32-bit data word is 101xxxxxx……, after shifting it 29 bits to the right and performing a bitwise AND operation with 0x7, we get the tag identifier 101 in binary, which corresponds to 5 in decimal, providing a unique identifier for subsequent parameter matching.

[0020] It should be noted that the mask used for extraction is hexadecimal 0x0FFFFFFF. Performing a bitwise AND operation between this mask and the 32-bit data word can directly mask the parity bit at bit 0 and the tag bits from bits 31 to 29, retaining only the 28-bit two's complement value segment from bits 28 to 1. The ARINC429 bus standard divides the data bits and parity bits. Masking the parity bit can prevent the parity information from interfering with subsequent value parsing and ensure the purity of the two's complement value.

[0021] It should be noted that a key-value pair data structure is used to complete the association. The extracted tag identifier is used as the key and the corresponding complement value is used as the value. This achieves a one-to-one mapping between tags and values ​​within a single frame of data. The ARINC429 bus has a one-to-one correspondence between tags and values ​​in a single frame of 32-bit data. This structure can avoid tracing errors caused by mixing multiple tags and values, and provides a data association basis for subsequent tag matching and value restoration.

[0022] In step S2, the tag identifier is matched against a preset tag rule base, and the sensor function category and effective data bit length are extracted based on the matching result, including: S21, perform bit-by-bit binary encoding matching between the tag identifier and the device identifier entry in the preset tag rule base to obtain the matching result; S22, if the matching results are consistent, lock the metadata storage address of the device identifier table entry, and extract the function category field and resolution bit parameter of the metadata storage address; if the matching results are inconsistent, terminate the extraction operation. S23, determine the sensor function category according to the function category field, and determine the effective length of the data bits according to the resolution bit length parameter; S24, integrate the sensor function category and the effective length of the data bits to obtain the sensor function category and the effective length of the data bits.

[0023] It should be noted that the default tag rule base is a locally stored offline structured database. Device identifier entries within the database are all stored using 3-bit binary encoding, perfectly matching the bit width of the tag identifier. The bit-by-bit matching operation compares each binary value of the tag identifier with the corresponding entry from the most significant bit to the least significant bit. If all three bits are the same, the matching result is consistent; if any bit is different, the matching result is inconsistent. This method avoids misidentification of devices due to partial matching.

[0024] For example, if the tag identifier is 3-bit binary 101, and a certain device identifier entry in the preset tag rule library is also 101, after comparing the 3-bit values ​​one by one, the matching results are consistent.

[0025] It should be noted that each device identifier entry in the preset tag rule base is associated with a unique metadata storage address, which is the logical storage index within the base. Sensor function category codes are 2-bit binary codes, and the effective data length is a 4-bit decimal number segment. The extraction operation directly reads the values ​​of the two fields pointed to by the locked storage address. When the extraction operation is terminated, a tag mismatch message is generated to ensure that subsequent processes do not receive invalid parameters.

[0026] For example, if the matching result is consistent, the locked metadata storage address is 0015, the sensor function category is read as binary 10, the data bit effective length is 24, and the field and parameter extraction is completed.

[0027] It should be noted that the sensor function category code is matched with a preset aviation sensor category code lookup table. This lookup table is preset according to aviation measurement and control requirements, and the binary code of the field corresponds one-to-one with the major sensor categories such as pressure, temperature, and angular velocity. The specific sensor function category can be determined by direct matching. The effective length of the data bit is a decimal number, which is directly used as the value of the effective length of the data bit. This value corresponds to the effective bit width in the two's complement value that actually participates in the conversion of pressure engineering quantity. The padding bits or reserved bits in the two's complement value can be filtered out, which meets the resolution design requirements of different sensors on the ARINC429 bus.

[0028] For example, the sensor function category is encoded as binary 01, and after matching the encoding lookup table, it is determined to be a static pressure sensor; the effective length of the data bits is 24, so the effective length of the data bits is directly determined to be 24, and only the 24 effective bits of the complement value are extracted for subsequent processing.

[0029] It should be noted that the integration is completed using structured binary data, with the determined sensor function category as the first element of the binary and the effective length of the data bits as the second element, forming indivisible feature data. This structure ensures that the sensor function category and the effective length of the data bits are transmitted synchronously and correspond one-to-one in subsequent processing, avoiding parameter misalignment during the restoration of the two's complement value. Furthermore, the binary can be directly passed to the subsequent sign bit judgment stage, meeting the real-time requirements of aviation data processing.

[0030] For example, by taking the static pressure sensor as the first element and 24 bits as the second element, and integrating the binary tuple (static pressure sensor, 24 bits), we can obtain the sensor function category and the effective length of the data bits.

[0031] In step S3, based on the sensor function category and the effective length of the data bits, the two's complement value is truncated using a bit mask and then the sign is restored to obtain the pressure engineering quantity value, including: S31, Generate a corresponding bit mask according to the effective length of the data bits, and use the bit mask to perform truncation processing on the two's complement value to filter out invalid padding bits and status bits, and obtain the effective two's complement value; S32, lock the last bit of the effective two's complement value as the sign bit, collect the level logic state of the sign bit, and obtain the sign bit level characteristics; S33, if the sign bit level feature is 0, then the effective two's complement value is retained; if the sign bit level feature is 1, then the effective two's complement value is restored by adding one to the one's complement to obtain the restored sign value. S34, convert the effective two's complement value or the symbol restored value with the symbol bit level characteristic of 0 into aviation pressure engineering measurement units to obtain the pressure engineering quantity value.

[0032] It should be noted that the bitmask is generated according to the effective length of the data bits. The rule is to construct a binary number with the lower N bits all 1 and the remaining higher bits all 0, where N is the effective length of the data bits, and then convert it to hexadecimal format. The truncation process performs a bitwise AND operation between the two's complement value and the bitmask. This operation accurately filters out the padding bits and status bits other than the effective bits, retaining only the core data bits actually output by the sensor. This adapts to the resolution design requirements of different pressure sensors and avoids invalid bits interfering with subsequent value interpretation.

[0033] For example, if the effective data bit length is 14 bits, the generated binary bitmask is 0011111111111111, which corresponds to 0x3FFF in hexadecimal. By performing a bitwise AND operation between the two's complement value (binary 1011010010111100) and the bitmask, the effective two's complement value (0011010010111100) is obtained, filtering out invalid padding bits at higher levels. It should be noted that the sign bit is fixed as the highest bit of the effective two's complement value, counted from 0 as the effective length - 1 bit. This setting follows the ARINC429 bus two's complement encoding standard. The acquisition operation directly reads the binary logic value of the sign bit; a value of 0 indicates a positive pressure value, and a value of 1 indicates a negative pressure value. The acquired level characteristics provide a direct basis for subsequent sign reconstruction.

[0034] For example, the binary representation of a 14-bit effective two's complement value is 11001011001101, with its highest bit (13th bit) being 1, and the acquired sign bit level characteristic is 1; if the effective two's complement value is 011110100101 (12 bits), with its highest bit being 0, the sign bit level characteristic is 0.

[0035] It should be noted that adding one to the inverse code is the standard method for converting two's complement to original code in the aviation field. The specific operation is to first perform a bitwise inversion on all the binary bits of the effective two's complement value, and then perform arithmetic addition on the inverted value to restore the absolute value of the original code of the negative number. When the sign bit is 0, the effective two's complement value is directly retained. Since the two's complement is consistent with the original code in this state, no additional processing is required. This process is suitable for the sign restoration requirements of aviation bipolar pressure parameters.

[0036] For example, the 14-bit effective two's complement value is 11001011001101, with a sign bit level characteristic of 1. First, invert each bit to get 00110100110010, then add one arithmetically to get 00110100110011. This value is the sign-restored value, which, combined with the sign bit, is determined to be a negative voltage value. If the sign bit level characteristic is 0, the effective two's complement value 011110100101 is directly retained.

[0037] It should be noted that the conversion adopts the linear conversion method calibrated by the manufacturer of the aviation pressure sensor. First, the effective two's complement value or the sign-restored value of the binary number is converted into a decimal absolute value. Then, it is substituted into the formula for calculation. The decimal value is multiplied by the sensor's calibrated range coefficient and the sensor's zero-point offset is added. The engineering measurement unit is selected from aviation standard kilopascals or pounds per square inch. The range coefficient and zero-point offset are determined according to the sensor's installation location, aircraft system design specifications, and manufacturer calibration parameters. After conversion, the positive and negative signs are restored to obtain the actual interpretable pressure engineering quantity value.

[0038] For example, the absolute decimal value of the sign-restored value is 3037, corresponding to a range coefficient of 0.001 kPa and a zero offset of 0 kPa for the pressure sensor. Combined with the negative sign determination, the calculated pressure engineering quantity is -3.037 kPa. If it is a directly retained valid two's complement value, the decimal value is 748, the range coefficient is 0.001 kPa, and the calculated pressure engineering quantity is 0.748 kPa.

[0039] In step S4, the pressure engineering quantity value is compared with a preset pressure threshold. Values ​​exceeding the limit that meet the abnormal duration determination are marked as abnormal. A tag serial number is assigned to each abnormal tag. The abnormal tags and the tag serial numbers are then integrated to obtain the abnormal engineering quantity value, including: S41, compare the pressure engineering quantity value with the preset pressure threshold to obtain pressure comparison characteristics; S42, if the pressure comparison feature exceeds the limit, start the abnormal duration counter, accumulate the frame time interval of the sampling period to obtain the abnormal duration feature, and if it does not exceed the limit, terminate this step. S43, compare the abnormal duration feature with the preset frame conversion time threshold to obtain the duration determination feature; S44, if the duration determination feature exceeds the threshold, an anomaly marker is generated, the anomaly marker is bound to a tag sequence number, and the anomaly marker bound to the tag sequence number is integrated with the pressure engineering quantity value to obtain the anomaly engineering quantity value.

[0040] It should be noted that the preset pressure thresholds are set based on the sensor installation location, aircraft system design specifications, and aviation measurement and control industry standards. These thresholds are divided into an upper pressure threshold and a lower pressure threshold. Different aircraft zones and different functional categories of pressure sensors correspond to different threshold ranges, ensuring that the thresholds match the actual working scenarios of the sensors. The comparison operation directly compares the pressure engineering value with the upper and lower threshold values. Pressure comparison characteristics are divided into two categories: exceeding the limit and not exceeding the limit. Exceeding the limit includes both values ​​exceeding the upper threshold and values ​​falling below the lower threshold. Not exceeding the limit means the value is between the upper and lower thresholds. This comparison logic can quickly determine the normal state of the pressure value.

[0041] For example, the preset threshold of the pressure sensor in the aircraft pressurized cabin is -0.5 kPa at the lower limit and 8.0 kPa at the upper limit. The pressure engineering value is 9.2 kPa. The pressure comparison feature obtained by comparison is that it exceeds the limit, while the value is 0.3 kPa and it does not exceed the limit.

[0042] It should be noted that the abnormal pressure over-limit duration counter is a frame-level cumulative counter, accumulating the frame time interval, which is the sampling period of the ARINC429 bus. This interval is set according to the bus standard refresh rate, mainly 20ms per frame, to ensure that the accumulation duration is synchronized with the data frame transmission rhythm. After the counter is started, for each frame of pressure engineering quantity value exceeding the limit received, the counter accumulates the frame time interval once, and the total accumulated duration is the abnormal duration characteristic. If the pressure comparison characteristic does not exceed the limit, this step of processing is immediately terminated and the counter is cleared to avoid the accumulation error of non-continuous over-limit duration.

[0043] For example, if the bus frame interval is 20ms and the pressure value exceeds the limit for 3 consecutive frames, the abnormal duration characteristic obtained after the counter is accumulated is 60ms. If a frame does not exceed the limit, the counter is cleared and the processing is terminated.

[0044] It should be noted that the preset frame conversion time threshold is obtained by multiplying a predefined continuous frame number by the single-frame time interval. The predefined continuous frame number is set at 10-25 frames based on the safety requirements of avionics systems, balancing the need to filter transient interference with the need to promptly capture real anomalies. This threshold is the core basis for determining whether an anomaly is a persistent fault. The comparison operation compares the anomaly duration feature with the preset frame conversion time threshold. The duration feature is divided into two categories: exceeding the threshold and not exceeding the threshold. If the cumulative anomaly duration feature is greater than the threshold, it is considered exceeding the threshold; otherwise, it is considered not exceeding the threshold.

[0045] For example, the predefined continuous frame count is 15 frames, the single frame time interval is 20ms, the preset frame conversion time threshold is 300ms, the abnormal duration feature is 320ms, and the duration judgment feature obtained by comparison is exceeding the threshold. If it is 280ms, it is not exceeding the threshold.

[0046] It should be noted that the anomaly markers adopt the binary identifier format of the aviation data processing standard, with 1 indicating a persistent anomaly in the pressure value and 0 indicating normal operation. This marker format is concise and facilitates rapid identification by downstream modules. The tag serial number is a unique hexadecimal code corresponding to the data frame, enabling precise tracing of the data frame's transmission link and sensor source. The binding operation establishes a one-to-one mapping relationship between the anomaly marker and the tag serial number. The integration operation uses a structured data format, combining the anomaly marker, tag serial number, and pressure engineering quantity value into an inseparable whole, ensuring that anomaly information, numerical values, and tracing information are transmitted synchronously without misalignment or loss.

[0047] For example, when the duration determination feature exceeds the threshold, an anomaly marker 1 is generated, the tag sequence number 0xA7B9C2D4 is bound, and it is integrated with the pressure engineering quantity value of 105.7kPa to obtain a structured abnormal engineering quantity value (1, 0xA7B9C2D4, 105.7kPa).

[0048] In step S5, the abnormal engineering quantity values ​​are matched against a preset joint location database to obtain joint attribute features. Based on these joint attribute features, the joint number, aircraft partition index, and joint coordinate index are integrated to obtain the abnormal joint location, including: S51, parse the abnormal engineering quantity value and extract the tag serial number from the abnormal engineering quantity value; S52, match the tag serial number with the preset connector location database to obtain matching result features. If the matching result features are consistent, extract connector attribute features from the preset connector location database. If the matching result features are inconsistent, terminate this step. S53, extract the connector number, aircraft partition index and connector coordinate index according to the connector attribute features, and perform structured integration of the connector number, aircraft partition index and connector coordinate index to obtain the abnormal connector location.

[0049] It should be noted that the abnormal engineering quantity values ​​are structured data, containing three fixed fields: anomaly marker, tag serial number, and pressure engineering quantity value. These fields are arranged in a predefined logical order. The parsing operation identifies each field sequentially according to this order, extracting the tag serial number field from the structured data. The tag serial number is a fixed-length hexadecimal code from aviation telemetry and control standards, possessing global uniqueness across the entire aircraft system. The extracted serial number directly serves as the tag serial number, which is the unique primary key for subsequent matching of the connector location database, ensuring the accuracy of physical location tracing.

[0050] For example, the structured data of abnormal engineering quantity values ​​is (1, 0xA7B9C2D4, 105.7kPa). After parsing according to the field order, the tag serial number 0xA7B9C2D4 is separated out, and the tag serial number is directly sent to the subsequent database matching process.

[0051] It should be noted that the preset connector location database is a locally pre-built aviation-specific structured relational database. It uses the tag serial number as the primary key to establish a hash index and stores the complete attribute information of the connectors corresponding to all pressure sensors of the aircraft. The matching operation performs a full-character exact match between the tag serial number and the database primary key. The matching result is divided into two categories: consistent and inconsistent. A consistent match means that the attribute record of the corresponding connector has been found, while an inconsistent match means that there is no matching record. When the match is consistent, the complete connector attribute features associated with the primary key are directly extracted, including core information such as connector number, aircraft partition index, and connector coordinate index. When the match is inconsistent, this step is terminated immediately and a prompt message indicating that the serial number matching failed is generated to avoid invalid subsequent data processing.

[0052] For example, if the tag serial number 0xA7B9C2D4 completes a full character exact match with the database primary key and the matching result features are consistent, the corresponding connector attribute features can be extracted; if the tag serial number 0x11223344 has no matching primary key in the database and the matching result features are inconsistent, this step of the process will be terminated immediately and a prompt will be generated.

[0053] It should be noted that from the extracted joint attribute features, three core pieces of information—joint number, aircraft partition index, and joint coordinate index—are precisely filtered out according to preset field names. Among them, the joint number is a unique component code of aviation equipment maintenance standards, the aircraft partition index is the standardized code for dividing the aircraft fuselage area, and the joint coordinate index is the specific value of the X, Y, and Z axes based on the aircraft fuselage coordinate system, with units following aviation design standards. The structured integration adopts a triplet data structure, which combines the joint number, aircraft partition index, and joint coordinate index as elements in sequence to form an indivisible abnormal joint location, ensuring that each information dimension corresponds one-to-one, and providing complete and accurate physical location data for the subsequent construction of a reverse mapping chain.

[0054] For example, the joint number Pneu-Valve-Left-047 is extracted from the joint attribute features, which is simplified to "PVL047" for convenience. The aircraft partition index Cabin-Upper-Left-03 is simplified to "CUL03" for convenience. The joint coordinate index (X=124.5, Y=78.2, Z=15.8) is integrated into a triplet structure as (PVL047, CUL03, X=124.5, Y=78.2, Z=15.8) to obtain the location of the abnormal joint.

[0055] In step S6, a reverse mapping chain from the abnormal connector location to the tag serial number is constructed, and the reverse mapping chain is injected into the abnormal engineering quantity value to obtain the associated corresponding structure, including: S61, The tag serial number is processed using a hash algorithm to obtain the identification feature; S62, Based on the identification feature as the root node, and combined with the abnormal joint position, construct a three-level topology tree from the label identification to the three-dimensional coordinates to obtain the topology tree feature; S63, after performing path locking processing on the topology tree features, the source path information is extracted and integrated to obtain the reverse mapping chain; S64, Inject the reverse mapping chain into the abnormal engineering quantity value to obtain the associated corresponding structure.

[0056] It should be noted that the hash algorithm used is the SHA-256 algorithm, a standard for aviation data processing. The tag serial number is a fixed-length hexadecimal code, which is directly used as the input value to the algorithm. After the algorithm completes the calculation, it generates a 256-bit fixed-length hash value, which serves as the unique identifier and identification feature. The SHA-256 algorithm has high collision resistance and uniqueness, which can prevent different tag serial numbers from generating the same identifier, meeting the uniqueness requirements of aviation data traceability. Furthermore, the fixed length of the hash value facilitates the unified storage, retrieval, and hierarchical association of subsequent data structures.

[0057] It should be noted that the three-level topology tree is constructed according to a hierarchical structure of root node - second-level node - third-level node. The root node is the generated identification feature, the second-level nodes are dedicated to the connector number in the abnormal connector location, and the third-level nodes are further divided into the aircraft partition index and connector coordinate index under the second-level nodes. A unique mapping relationship is established between the levels to ensure that the data can be accurately traced back to the specific physical three-dimensional coordinates layer by layer from the root node. This three-level topology tree, which fully reflects the association link between the label identification and the physical spatial location, is the topology tree feature, which is adapted to the hierarchical traceability and management needs of aviation data.

[0058] For example, given the identifier e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855, which is simplified to "e3b" for convenience, with e3b as the root node, the second-level node is attached to the connector number PVL047, and the third-level node is attached to the aircraft partition index CUL03 and the connector coordinate index (X=124.5, Y=78.2, Z=15.8). The complete three-level topology tree constructed according to the hierarchy is the topology tree feature.

[0059] It should be noted that the path locking process starts from the leaf nodes of the three-level topology tree (aircraft partition index, connector coordinate index) and traces back layer by layer to the root node (identification feature). The node information and hierarchical relationships at each level are recorded synchronously. Then, the complete tracing path is structurally encapsulated in reverse order of connector coordinate index - aircraft partition index - connector number - identification feature, forming a chain-like data structure, which is the reverse mapping chain. This process enables rapid reverse tracing from physical spatial location to tag identification, solving the problem of tracing efficiency from abnormal values ​​to source tags in aviation data. Furthermore, the chain structure facilitates rapid data parsing and transmission.

[0060] For example, path locking is performed on the topology tree features constructed above. Starting from the joint coordinate index (X=124.5, Y=78.2, Z=15.8) and the aircraft partition index CUL03, the path is traced back to the joint number PVL047, and then back to the identification feature e3b. The information of each node is encapsulated into a chain data structure in reverse order to obtain the reverse mapping chain. The specific content of this chain data is [(X=124.5, Y=78.2, Z=15.8), CUL03, PVL047, e3b].

[0061] It should be noted that the injection operation embeds the reverse mapping chain as an independent structured field into the original structured data of the abnormal engineering quantity values. This forms a unified, integrated structured data body with the existing anomaly markers, tag serial numbers, and pressure engineering quantity value fields within the abnormal engineering quantity values. Each field maintains a one-to-one correspondence, with no misalignments or separations. This integrated, complete structured data body, known as the associated structure, achieves a deep binding between abnormal engineering quantity values ​​and the reverse mapping chain. This allows for direct invocation of the reverse mapping chain when querying abnormal values, quickly tracing back to the corresponding physical location, meeting the rapid tracing requirements of real-time aviation monitoring.

[0062] For example, the original structured data of abnormal engineering quantity values ​​is (1, 0xA7B9C2D4, 105.7kPa). The reverse mapping chain obtained in the previous step is embedded as a new field into this structure and integrated into (1, 0xA7B9C2D4, 105.7kPa, [(X=124.5, Y=78.2, Z=15.8), CUL03, PVL047, e3b]). This integrated structured data body is the associated corresponding structure.

[0063] In step S7, the 3D model base map is retrieved according to the associated corresponding structure, and the abnormal engineering quantity values ​​are projected onto the 3D model base map to obtain abnormal projection features. The abnormal projection features are then integrated with the abnormal engineering quantity values ​​to obtain monitoring view data, including: S71, parse the associated structure, extract the aircraft partition index, the abnormal engineering quantity value and the joint coordinate index and encode them to obtain the structural parsing features; S72, retrieve the three-dimensional model base map according to the aircraft partition index, project the abnormal engineering quantity values ​​onto the three-dimensional model base map, and perform visualization enhancement processing on the region of the joint coordinate index in the structural analysis features to obtain abnormal visualization features; S73, the abnormal visualization features and the abnormal engineering quantity values ​​are structured and encapsulated to obtain monitoring view data.

[0064] It should be noted that the associated structure is an integrated structured data that combines a reverse mapping chain, abnormal parameters, and physical spatial location. Internally, the storage area is divided according to predefined fields, corresponding to core information such as aircraft partitions, pressure values, and 3D coordinates. The parsing operation accurately identifies and splits three types of information according to field names: aircraft partition index, pressure engineering quantity value extracted from abnormal engineering quantity value, and joint coordinate index. These are then integrated into a structured parsing feature in the form of triples, ensuring that the three types of information are bound one by one without misalignment or separation. This provides a structured basic data support for subsequent visualization processing and meets the precise field retrieval requirements of aviation monitoring.

[0065] For example, after parsing a certain associated structure, the aircraft partition index Cabin-Mid-Right-07 is extracted, which is simplified to "CMR07" for convenience. The pressure engineering quantity value of 112.4 kPa and the joint coordinate index (X=198.3, Y=65.1, Z=9.4) extracted from the abnormal engineering quantity values ​​are integrated into a triple to form the structural analysis feature, which is directly sent to the subsequent three-dimensional visualization stage.

[0066] It should be noted that the aircraft partition 3D model base map is a standardized model pre-installed in the aviation digital model library of the system, containing precise spatial information such as the fuselage structure, pressure pipeline routing, and key joint distribution of the corresponding partition; numerical projection uses the joint coordinate index as the spatial anchor point, and the pressure engineering quantity values ​​extracted from abnormal engineering quantity values ​​are superimposed on the corresponding physical location of the model base map in the form of dynamic numerical labels; the abnormal area visualization enhancement uses the anchor point as the center, sets a spherical influence range with a radius of 0.8 meters, and performs a gradient color enhancement process from dark red to orange-yellow on the model area within the range to highlight the abnormal hot areas. The resulting 3D model base map with highlighted abnormal areas and numerical labels is the abnormal visualization feature, which meets the visual rapid identification requirements of aviation monitoring.

[0067] For example, by retrieving the corresponding 3D model base map based on the partition index CMR07, the 112.4 kPa pressure engineering quantity value extracted from the abnormal engineering quantity value is projected to the coordinates (X=198.3, Y=65.1, Z=9.4), and a red-orange gradient color enhancement is applied to the 0.8-meter range around this location to form a 3D model base map with highlighted marks and numerical labels, thus obtaining the abnormal visualization features.

[0068] It should be noted that the abnormal visualization features are integrated and encapsulated with the pressure engineering quantity values, abnormal markers, tag serial numbers, and frame timestamps extracted from the abnormal engineering quantity values ​​to form an integrated data structure containing graphics and attributes, which is the view integration feature. The view data generation involves adapting the view integration feature to the display format of the aviation monitoring terminal, supplementing the basic parameters such as coordinate scaling and area positioning required for view interaction, and finally obtaining integrated monitoring view data that can be directly displayed and interacted with on-board or ground monitoring terminals, supporting operations such as viewing abnormal values, locating three-dimensional positions, and scaling partition models.

[0069] For example, the CMR07 partition 3D model base map with highlighted markings (anomaly visualization feature) is integrated with information such as the 112.4 kPa pressure engineering quantity value extracted from the abnormal engineering quantity value, anomaly mark, tag serial number 0xA7B9C2D4, and frame timestamp 1718956324892μs into a view integration feature. After format adaptation, the visualization data that can be displayed on the monitoring terminal is generated, which is the monitoring view data. The terminal can directly display the highlighted abnormal area of ​​the partition and the corresponding 112.4 kPa pressure value.

[0070] In step S8, the abnormal three-dimensional coordinate features of the monitoring view data are extracted, and the joint and partition features are traced along the reverse mapping chain. The joint and partition features are then matched with a preset fault type table to obtain the fault location result, including: S81, extract the spatial coordinate information of the abnormal area from the monitoring view data to obtain the abnormal three-dimensional coordinate features; S82, based on the abnormal three-dimensional coordinate features, perform reverse tracing along the reverse mapping chain to extract the connector information and aircraft partition information, and integrate and encapsulate the connector information and aircraft partition information to obtain connector and partition features; S83, Match the connector and partition features with the fault matching items in the preset fault type table to obtain the fault matching result features; S84, if the fault matching result feature matching is unsuccessful, output no matching fault type information; if the fault matching result feature matching is successful, aggregate the fault type and associated location information to obtain the fault location result.

[0071] It should be noted that the monitoring view data is a 3D model visualization of aircraft partitions with color-enhanced anomaly areas and dynamic numerical labels. The anomaly areas are highlighted with the physical coordinates of the connector as the core anchor point. The extraction operation identifies the core anchor point coordinates of all highlighted anomaly areas in the view. These coordinates are completely consistent with the 3D index value of the physical coordinates of the connector. The 3D coordinates of one or more anomaly points can be extracted and integrated into a coordinate set. This set is the anomaly 3D coordinate feature. The uniqueness of the anchor point coordinates ensures that the extracted coordinates correspond one-to-one with the actual faulty connector.

[0072] For example, from the monitoring view data of CMR07 partition, two red-highlighted abnormal areas were identified, and their core anchor point coordinates (X=198.3, Y=65.1, Z=9.4) and (X=201.7, Y=67.2, Z=10.1) were extracted. The two were integrated into a coordinate set to obtain the abnormal three-dimensional coordinate features.

[0073] It should be noted that the reverse tracing starts with the abnormal 3D coordinates as the leaf node, and searches for matching layer by layer upwards along the constructed reverse mapping chain. Relying on the path locking characteristics of the mapping chain, it traces back to the corresponding unique connector number, and then further traces back to the aircraft partition index to which the connector belongs. The connector information conforms to the unique connector code of aviation equipment maintenance standards, and the aircraft partition information is standardized aircraft partition identifier. After the two are structured and integrated, the connector and partition characteristics are obtained. The entire tracing process ensures no misalignment and no confusion.

[0074] For example, by performing reverse tracing along the reverse mapping chain on the abnormal three-dimensional coordinates (X=198.3, Y=65.1, Z=9.4), the connector information JNT-4567-02 is first matched, which is simplified to "J402" for convenience. Then, the aircraft partition information CMR07 to which the connector belongs is traced. The two are then integrated to obtain the connector and partition features.

[0075] It should be noted that the preset fault type table is a structured fault knowledge base specifically for aviation pressure systems. The fault matching items in the table are combinations of joint type, aircraft partition location, and pressure anomaly type (overpressure / underpressure). Each matching item is associated with a corresponding fault classification code and fault description text. The matching operation adopts full-condition precise comparison, which combines the joint and partition characteristics with the anomaly type of the current pressure engineering quantity and compares them one by one with the fault matching items in the table. The fault matching result is divided into two categories: successful match and unsuccessful match, to ensure the accuracy of fault type matching.

[0076] For example, the connector and partition features (J402, CMR07) are combined with the current pressure overpressure anomaly type and compared one by one with the fault matching items in the preset fault type table. The resulting fault matching result is characterized as a successful match.

[0077] It should be noted that if the fault matching result indicates that the match is unsuccessful, the system outputs a standardized message indicating that there is no matching fault type, which facilitates subsequent manual intervention to troubleshoot unknown faults. If the match is successful, the system extracts the corresponding fault classification code and fault description text from the preset fault type table, and then integrates them with the connector number, abnormal three-dimensional coordinates, and aircraft partition information into associated location information. The fault type and associated location information are combined into structured data, which is the fault location result and can be directly pushed to the onboard diagnostic system or ground maintenance terminal.

[0078] For example, if the fault matching result is successful, the fault type FAULT-PRES-OVR-01 (overpressure fault in the pressurization system) and associated location information (connector number J402, abnormal three-dimensional coordinates X=198.3Y=65.1Z=9.4, aircraft partition CMR07) will be output. The two will be integrated to obtain a structured fault location result. If the matching is unsuccessful, the fault type information with no matching will be output directly, and manual investigation is required.

[0079] In summary, this invention discloses an intelligent interaction method for aviation test data based on the ARINC429 bus. This invention achieves the following: precise separation of bit segments and tag identifiers and two's complement values ​​in the 32-bit raw data words of the ARINC429 bus; accurate tag matching based on a pre-built tag rule base and determination of sensor function categories and effective data bit lengths; sign bit judgment of two's complement values ​​and sign restoration by adding one to the one's complement, and acquisition of pressure engineering quantity values; threshold comparison of pressure engineering quantity values, counting of abnormal durations, generation of abnormal markers, and binding of tag serial numbers; index matching of a pre-built joint location database and extraction of abnormal physical joint location information; and construction of a reverse mapping chain from tag identifiers to aircraft partition three-dimensional coordinates, as well as the association and correspondence between abnormal parameters and physical spatial locations. The entire process, including the formation of the structure, the integration of multi-sensor pressure engineering quantity values ​​into the aircraft partition view, the color enhancement of abnormal areas, the generation of real-time monitoring view data for anomaly aggregation, the scanning of anomaly markers in the monitoring view, the reverse tracing of the reverse mapping chain, and the acquisition of joint position-level fault location results, realizes the real-time identification of aerospace pressure system anomalies from the original ARINC429 bus complement data, the precise spatial positioning of abnormal parameters to physical joints, the visualized aggregation of multi-source anomaly information, and the intelligent tracing of fault descriptions. It effectively improves the real-time detection capability, spatial positioning accuracy, and visualization interaction efficiency of aerospace pressure system anomalies, solves the industry problems of vague positioning and delayed response of traditional monitoring methods, and provides efficient technical support for intelligent health management of aviation equipment.

[0080] Reference Figure 2 The second embodiment of the present invention provides an intelligent interactive system for aviation test data based on the ARINC429 bus, comprising: The data parsing module is used to obtain data words from the ARINC429 bus, perform bit segmentation and masking processing on the data words, and obtain tag identifiers and complement values; The tag matching module is used to match the tag identifier with a preset tag rule library, and extract the sensor function category and the effective length of the data bits based on the matching result; The numerical conversion module is used to perform bit masking and sign restoration on the two's complement value according to the sensor function category and the effective length of the data bits to obtain the pressure engineering quantity value. An anomaly determination module is used to compare the pressure engineering quantity value with a preset pressure threshold, mark the out-of-limit value that meets the anomaly duration determination as an anomaly mark, assign a tag serial number to the anomaly mark, and integrate the anomaly mark and the tag serial number to obtain the abnormal engineering quantity value. The location index module is used to match the abnormal engineering quantity values ​​with a preset joint location database to obtain joint attribute features, and to integrate the joint number, aircraft partition index and joint coordinate index according to the joint attribute features to obtain the abnormal joint location. The mapping construction module is used to construct a reverse mapping chain from the abnormal connector location to the tag sequence number, and inject the reverse mapping chain into the abnormal engineering quantity value to obtain the associated corresponding structure; An anomaly visualization module is used to retrieve a 3D model base map based on the associated corresponding structure, project the abnormal engineering quantity values ​​onto the 3D model base map to obtain anomaly projection features, and integrate the anomaly projection features with the abnormal engineering quantity values ​​to obtain monitoring view data. The fault location module is used to extract the abnormal three-dimensional coordinate features of the monitoring view data, trace the joint and partition features along the reverse mapping chain, and match the joint and partition features with a preset fault type table to obtain the fault location result.

[0081] It should be noted that the intelligent interaction system for aviation test data based on the ARINC429 bus provided in this embodiment of the invention is used to execute all the process steps of the intelligent interaction method for aviation test data based on the ARINC429 bus in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0082] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0083] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An ARINC 429 bus-based intelligent interaction method for aviation test data, characterized in that, include: Data words are obtained from the ARINC429 bus, and bit segmentation and masking are performed on the data words to obtain the tag identifier and the complement value. The label identifier is matched against a preset label rule library, and the sensor function category and effective length of data bits are extracted based on the matching result. Based on the sensor function category and the effective length of the data bits, the two's complement value is truncated by bit masking and then the sign is restored to obtain the pressure engineering quantity value. Compare the pressure engineering quantity value with the preset pressure threshold, mark the excess value that meets the abnormal duration judgment as an abnormal mark, assign a tag serial number to the abnormal mark, and integrate the abnormal mark and the tag serial number to obtain the abnormal engineering quantity value. The abnormal engineering quantity values ​​are matched with a preset joint location database to obtain joint attribute features. Based on the joint attribute features, the joint number, aircraft partition index and joint coordinate index are integrated to obtain the abnormal joint location. Construct a reverse mapping chain from the abnormal connector location to the tag serial number, and inject the reverse mapping chain into the abnormal engineering quantity value to obtain the associated corresponding structure; The three-dimensional model base map is retrieved according to the associated structure, and the abnormal engineering quantity value is projected onto the three-dimensional model base map to obtain the abnormal projection feature. The abnormal projection feature is integrated with the abnormal engineering quantity value to obtain the monitoring view data. Extract the abnormal three-dimensional coordinate features from the monitoring view data, trace the joint and partition features along the reverse mapping chain, match the joint and partition features with a preset fault type table, and obtain the fault location result.

2. The ARINC 429 bus based intelligent interaction method for avionics test data according to claim 1, wherein, The process of acquiring data words from the ARINC429 bus, performing bit segmentation and masking on the data words to obtain tag identifiers and complement values ​​includes: The differential voltage pulse sequence is received from the ARINC429 bus, and the differential voltage pulse sequence is sampled and assembled to obtain data words. The tag identifier bit field of the data word is masked to obtain the tag mask feature. The tag identifier is obtained by performing a shift operation based on the tag mask feature. The two's complement value segments of the data word are extracted using a mask, and the parity bits are masked to obtain the two's complement value.

3. The ARINC 429 bus based intelligent interactive method for avionics test data according to claim 1, wherein, The step of matching the label identifier with a preset label rule base and extracting the sensor function category and effective data bit length based on the matching result includes: The tag identifier is matched bit by bit with the device identifier entry in the preset tag rule base using binary encoding structure to obtain the matching result; If the matching results are consistent, the metadata storage address of the device identifier table entry is locked, and the function category field and resolution bit parameter of the metadata storage address are extracted. If the matching results are inconsistent, the extraction operation is terminated. The sensor function category is determined based on the function category field, and the effective length of the data bits is determined based on the resolution bit length parameter.

4. The ARINC 429 bus based intelligent interactive method for avionics test data according to claim 1, wherein, The step of truncating the two's complement value using a bit mask and then restoring the sign based on the sensor function category and the effective length of the data bits to obtain the pressure engineering quantity value includes: Generate a corresponding bitmask based on the effective length of the data bits, and use the bitmask to perform truncation processing on the two's complement value to filter out invalid padding bits and status bits, thereby obtaining the effective two's complement value. The last bit of the valid two's complement value is locked as the sign bit, and the level logic state of the sign bit is collected to obtain the sign bit level characteristics; If the sign bit level characteristic is 0, the effective two's complement value is retained; if the sign bit level characteristic is 1, the effective two's complement value is restored by adding one to the one's complement to obtain the restored sign value. The effective two's complement value or the symbol restored value with the symbol bit level characteristic of 0 is converted into aerospace pressure engineering measurement units to obtain the pressure engineering quantity value.

5. The ARINC 429 bus based intelligent interactive method for avionics test data according to claim 1, wherein, The process involves comparing the pressure quantity value with a preset pressure threshold, marking the exceeding values ​​that meet the abnormal duration determination criteria as abnormal markers, assigning a tag serial number to each abnormal marker, and integrating the abnormal marker and the tag serial number to obtain the abnormal quantity value, including: By comparing the pressure engineering quantity with the preset pressure threshold, pressure comparison characteristics are obtained; If the pressure comparison feature exceeds the limit, an abnormal duration counter is started to accumulate the frame time interval of the sampling period to obtain the abnormal duration feature. If it does not exceed the limit, the processing of this step is terminated. By comparing the abnormal duration characteristics with a preset frame conversion time threshold, duration determination characteristics are obtained; If the duration determination feature exceeds the threshold, an anomaly marker is generated, the anomaly marker is bound to a tag serial number, and the anomaly marker bound to the tag serial number is integrated with the pressure engineering quantity value to obtain the anomaly engineering quantity value.

6. The ARINC 429 bus based intelligent interactive method for avionics test data according to claim 1, wherein, The process of matching the abnormal engineering quantity values ​​with a preset joint location database to obtain joint attribute features, and then integrating the joint number, aircraft partition index, and joint coordinate index based on the joint attribute features to obtain the abnormal joint location, includes: Analyze the abnormal project quantity values ​​and extract the tag serial number from the abnormal project quantity values; The tag serial number is matched with a preset connector location database to obtain matching result features. If the matching result features are consistent, connector attribute features are extracted from the preset connector location database. If the matching result features are inconsistent, this step is terminated. Based on the joint attribute features, the joint number, aircraft partition index, and joint coordinate index are extracted. The joint number, aircraft partition index, and joint coordinate index are then structurally integrated to obtain the location of the abnormal joint.

7. The ARINC 429 bus based intelligent interactive method for avionics test data according to claim 1, wherein, The process of constructing a reverse mapping chain from the location of the abnormal connector to the tag serial number, injecting the reverse mapping chain into the abnormal engineering quantity value, and obtaining the associated corresponding structure includes: The tag serial number is processed using a hash algorithm to obtain the identification features; Based on the identification feature as the root node, a three-level topology tree from the label identification to the three-dimensional coordinates is constructed in combination with the abnormal joint position. The hierarchical association information of the three-level topology tree is extracted and integrated to obtain the topology tree feature. After performing path locking processing on the topology tree features, the source path information is extracted and integrated to obtain the reverse mapping chain; The reverse mapping chain is injected into the abnormal engineering quantity value to obtain the associated corresponding structure.

8. The intelligent interaction method for aviation test data based on the ARINC429 bus according to claim 1, characterized in that, The process involves retrieving a 3D model base map based on the associated structure, projecting the abnormal engineering quantity values ​​onto the 3D model base map to obtain abnormal projection features, and integrating the abnormal projection features with the abnormal engineering quantity values ​​to obtain monitoring view data, including: The associated structure is parsed, the aircraft partition index, the abnormal engineering quantity value and the joint coordinate index are extracted and encoded to obtain the structural parsing features; The three-dimensional model base map is retrieved according to the aircraft partition index. The abnormal engineering quantity values ​​are projected onto the three-dimensional model base map. The region of the joint coordinate index in the structural analysis features is subjected to visualization enhancement processing to obtain abnormal visualization features. The abnormal visualization features and the abnormal engineering quantity values ​​are structured and encapsulated to obtain monitoring view data.

9. The intelligent interaction method for aerospace test data based on the ARINC429 bus according to claim 1, characterized in that, The process involves extracting abnormal three-dimensional coordinate features from the monitoring view data, tracing back along the reverse mapping chain to obtain joint and partition features, and matching the joint and partition features against a preset fault type table to obtain fault location results, including: The spatial coordinate information of the abnormal area is extracted from the monitoring view data to obtain the three-dimensional coordinate features of the abnormality. Based on the abnormal three-dimensional coordinate features, reverse tracing is performed along the reverse mapping chain to extract the connector information and aircraft partition information. The connector information and aircraft partition information are then integrated and encapsulated to obtain connector and partition features. The joint and partition features are matched with the fault matching items in the preset fault type table to obtain the fault matching result features; If the fault matching result feature matching is unsuccessful, then no matching fault type information is output. If the fault matching result feature matching is successful, then the fault type and associated location information are aggregated to obtain the fault location result.

10. An intelligent interactive system for aviation test data based on the ARINC429 bus, characterized in that, include: The data parsing module is used to obtain data words from the ARINC429 bus, perform bit segmentation and masking processing on the data words, and obtain tag identifiers and complement values; The tag matching module is used to match the tag identifier with a preset tag rule library, and extract the sensor function category and the effective length of the data bits based on the matching result; The numerical conversion module is used to perform bit masking and sign restoration on the two's complement value according to the sensor function category and the effective length of the data bits to obtain the pressure engineering quantity value. An anomaly determination module is used to compare the pressure engineering quantity value with a preset pressure threshold, mark the out-of-limit value that meets the anomaly duration determination as an anomaly mark, assign a tag serial number to the anomaly mark, and integrate the anomaly mark and the tag serial number to obtain the abnormal engineering quantity value. The location index module is used to match the abnormal engineering quantity values ​​with a preset joint location database to obtain joint attribute features, and to integrate the joint number, aircraft partition index and joint coordinate index according to the joint attribute features to obtain the abnormal joint location. The mapping construction module is used to construct a reverse mapping chain from the abnormal connector location to the tag sequence number, and inject the reverse mapping chain into the abnormal engineering quantity value to obtain the associated corresponding structure; An anomaly visualization module is used to retrieve a 3D model base map based on the associated corresponding structure, project the abnormal engineering quantity values ​​onto the 3D model base map to obtain anomaly projection features, and integrate the anomaly projection features with the abnormal engineering quantity values ​​to obtain monitoring view data. The fault location module is used to extract the abnormal three-dimensional coordinate features of the monitoring view data, trace the joint and partition features along the reverse mapping chain, and match the joint and partition features with a preset fault type table to obtain the fault location result.

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