Fracturing equipment multi-dimensional matrix type adaptive analysis system and analysis method

Abstract

The invention discloses a fracturing equipment multidimensional matrix type adaptive analysis system and analysis method, the system comprises an industrial personal computer, the industrial personal computer comprises an analysis unit, the analysis unit is connected with a static array storage area and a fault data dynamic storage area, and the static array storage area and the fault data dynamic storage area are respectively connected with an arithmetic unit and an output unit. The output unit is connected with the cloud database. The analysis method comprises the following steps: acquiring equipment information, and processing the equipment information to form multi-dimensional matrix type data; and acquiring fault equipment data, matching corresponding matrix data, processing the fault equipment data, calculating an average fault rate, calibrating, repeatedly calculating the average fault rate of the equipment according to different matrix data, and transmitting the average fault rate to the outside of the system through a signal. According to the method, the failure mechanism of the product under different environments and use conditions and related failure modes and failure calculation are analyzed, the configuration flexibility of the system is enhanced, and upgrading and maintenance are facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic control, and in particular relates to a multi-dimensional matrix adaptation analysis system for fracturing equipment. The present invention also relates to a multi-dimensional matrix adaptation analysis method for fracturing equipment based on the above analysis system. Background Art

[0002] As an emerging clean energy, shale gas has become an important target for global unconventional oil and gas development, profoundly affecting the global energy market structure and people's livelihood. Fracturing equipment is an important equipment for exploiting shale gas resources. At present, domestic and foreign fracturing equipment are mainly traditional diesel-driven and electric variable frequency-driven, and the mechanical structure is vehicle-mounted or skid-mounted. With the increasing number of various types of fracturing equipment put on the market, the on-site application environment increasingly requires these batch-produced equipment to achieve fully automatic digital monitoring and control, measure and estimate the reliability of equipment under different on-site fracturing conditions, have greater adaptive flexibility, and achieve automated and intelligent operation and maintenance, so as to have significant economic benefits and excellent environmental protection.

[0003] However, although major companies can barely collect data on their products and upload them to private clouds for storage, the monitoring of fracturing equipment still remains at the device-level signal collection level. The data blocks are relatively isolated and the degree of integration is low. The reliability of the equipment after a certain period of operation mainly relies on manual observation experience. It is not possible to accurately measure the performance of a certain type of equipment. The level of intelligence is low, which makes it difficult to make statistics on the further application reliability of the equipment, or to make preliminary application analysis of newly deployed equipment. This situation is not convenient for targeted product upgrades, which seriously affects the product optimization and iteration speed of fracturing equipment. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-dimensional matrix adaptation analysis system for fracturing equipment, which analyzes the failure mechanism of the product under different environments and usage conditions, as well as the related failure modes and failure solutions, thereby enhancing the configuration flexibility of the system and facilitating upgrades and maintenance.

[0005] Another object of the present invention is to provide a multi-dimensional matrix adaptation analysis method for fracturing equipment.

[0006] The first technical solution adopted by the present invention is a multi-dimensional matrix adaptation analysis system for fracturing equipment, including an industrial computer, the industrial computer including a parsing unit, the parsing unit is connected to a static array storage area and a fault data dynamic storage area through signals, the parsing unit is used to extract the type, environment, and application data of the equipment in the cloud database, and send data to the static array storage area through the OPC data acquisition protocol;

[0007] The static array storage area and the fault data dynamic storage area are respectively connected to a computing unit, and the computing unit is connected to an output unit.

[0008] The first technical solution of the present invention is also characterized in that:

[0009] The static array storage area includes a plurality of storage units, each of which is composed of blocks with x, y, z identification characteristics, and the blocks include a timestamp and a characteristic quantity; the static array storage area is connected to the fault data dynamic storage area through an OPC data acquisition protocol.

[0010] The dynamic storage area for fault data includes several containerized application modules that run according to fault type rules. The parsing unit transfers the feature quantity that exceeds the threshold to the dynamic storage area for fault data. The dynamic storage area for fault data identifies the containerized application module corresponding to the fault type based on the feature quantity, and sends data to the computing unit through the containerized application module.

[0011] The computing unit is used for data matching and weighted degree calculation between the static array storage area and the fault data dynamic storage area;

[0012] The output unit includes a report storage and an interface function for storing the calculation results of the calculation unit. The output unit is connected to the cloud database based on the interface function.

[0013] The first technical solution adopted by the present invention is a multi-dimensional matrix adaptation analysis method for fracturing equipment, comprising the following steps:

[0014] Step 1, obtaining fracturing equipment information and processing it to form multi-dimensional matrix type data;

[0015] Step 2, obtaining faulty device data, and matching corresponding matrix data based on the faulty device data;

[0016] Step 3: weight the faulty equipment data based on the corresponding matrix data, calculate the average failure rate of the faulty equipment through the reliability evaluation algorithm, and calibrate the average failure rate;

[0017] Step 4, repeat the operation of step 3 for the faulty equipment using different matrix data to obtain the average failure rate under different matrix data;

[0018] Step 5: The average failure rate calculated in step 3 and step 4 is stored in a report memory in a multi-dimensional rectangular form and transmitted to the outside of the system through a signal.

[0019] The first technical solution of the present invention is also characterized in that:

[0020] In step 1, the fracturing equipment information is the type, environment and application parameters of the equipment;

[0021] The fracturing equipment information is processed to form multi-dimensional matrix type data. Specifically, the information of each fracturing equipment is divided into blocks with x, y, and z identification characteristics to form storage units. x is the type parameter axis, which is digitally calibrated and counted to form x1-xN types of variable types; y is the climate and environment parameter axis, and y1-yN are used to identify different climate and environment parameters; z is the application axis, and z1-zN are used to identify different application scenarios, which can be divided according to application time, full load rate or number of continuous operation fracturing stages.

[0022] In step 2, matching the corresponding matrix data based on the faulty device data specifically includes transmitting the faulty device data to the containerized application module matching the corresponding fault type in the fault data dynamic storage area, and searching the matrix data corresponding to the faulty device in the static array storage area.

[0023] In step 3, the average failure rate of the faulty equipment is calculated by the reliability evaluation algorithm. The calculation formula is as follows:

[0024]

[0025] θ=1 / α

[0026] Among them, θ is the mean time between failures, α is the failure rate, and t is the equipment operation time.

[0027] In step 3, the multi-state climate model is introduced to calibrate the average failure rate, as shown in the following formula:

[0028]

[0029]

[0030]

[0031] Where λ is the failure rate of the component under normal weather conditions, in times / year; λ' is the failure rate of the component under adverse weather conditions, in times / year; 1 is the average failure rate, in times / year; N is the duration of normal weather, S is the duration of adverse weather; F is the proportion of adverse weather, with a value of 0 to 1.

[0032] In step 5, the average failure rate is stored in the report memory in the form of a multidimensional rectangle, specifically listing the operating parameters and failure rate calculation results of the faulty equipment based on each matrix data, and counting the maximum and minimum values ​​of the weighted values ​​in each matrix data in the multidimensional matrix, and storing them in the report memory.

[0033] The beneficial effects of the present invention are:

[0034] (1) The multi-dimensional matrix adaptation analysis system for fracturing equipment of the present invention establishes multi-dimensional matrix type data and performs multiple different types of matrix data combination calculations on the equipment based on the reliability evaluation algorithm with multi-dimensional rectangles as the core, which greatly improves the data analysis performance of the fracturing equipment and can make intelligent predictions based on this, exposing and analyzing the failure mechanism of the product under different environments and usage conditions, as well as related failure modes and failure solutions, thereby enhancing the configuration flexibility of the system, thereby greatly shortening the user's operation time and facilitating upgrades and maintenance.

[0035] (2) The multi-dimensional matrix adaptation analysis method for fracturing equipment of the present invention introduces a polymorphic climate model to conduct a detailed study on the system reliability under specific weather conditions, find the weak points of system operation under specific climate conditions, and propose corresponding system enhancement measures or adopt appropriate operation strategies according to specific weather conditions to enhance the reliability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a structural schematic diagram of the multi-dimensional matrix adaptation analysis system for fracturing equipment of the present invention;

[0037] Figure 2 It is a schematic flow chart of the multi-dimensional matrix adaptation analysis method for fracturing equipment of the present invention;

[0038] Figure 3 It is a multi-dimensional matrix type data block flow chart in the multi-dimensional matrix adaptation analysis method of the fracturing equipment of the present invention.

[0039] In the figure, 1. cloud database, 2. parsing unit, 3. static array storage area, 4. fault data dynamic storage area, 5. industrial computer, 6. storage unit, 7. computing unit, 8. output unit, 9. report storage. DETAILED DESCRIPTION

[0040] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] like Figure 1 As shown, the multi-dimensional matrix adaptation analysis system for fracturing equipment disclosed in the present invention includes a cloud database 1 and an industrial computer 5 that are interconnected, specifically interconnected through industrial Ethernet. The industrial computer 5 includes a parsing unit 2, and the parsing unit 2 is connected to a static array storage area 3 and a fault data dynamic storage area 4 through signals. The parsing unit 2 is used to extract the type, environment, and application data of the equipment in the cloud database 1, and send data to the static array storage area 3 through the OPC data acquisition protocol; the parsing unit 2 is also used to transfer the feature quantity exceeding the threshold to the fault data dynamic storage area 4.

[0042] The static array storage area 3 and the fault data dynamic storage area 4 are respectively connected to a computing unit 7 for updating multi-dimensional matrix data, and the computing unit 7 is connected to an output unit 8, which is connected to the cloud database 1. The output unit 8 is also connected to the static array storage area 3.

[0043] The static array storage area 3 includes several storage units 6, and the data is not lost when the power is off. The storage unit 6 is composed of blocks with x, y, and z identification characteristics, namely blocks x1y1z1~xNyNzN, and the blocks include independent timestamps and feature quantities; the static array storage area 3 is interconnected with the fault data dynamic storage area 4 through the OPC data acquisition protocol.

[0044] The fault data dynamic storage area 4 includes 1 to n containerized application modules running according to fault type rules. The fault data dynamic storage area 4 identifies the containerized application module corresponding to the fault type according to the feature quantity exceeding the threshold transmitted by the analysis unit 2. The containerized application module sends data to the operation unit 7 through the API interface function.

[0045] The operation unit 7 processes the matching and weighted degree calculation between the multi-dimensional matrix type data in the dynamic fault data storage area 4 and the static array storage area 3, and updates the multi-dimensional matrix data accordingly.

[0046] The output unit 8 includes a report storage 9 and an interface function for storing the operation results of the operation unit 7. The output unit 8 is connected to the cloud database 1 based on the interface function. The operation results of the operation unit 7 and the feedback data sent to the outside of the system in the multi-dimensional matrix type data, the data extracted by the user, and the variable data concerned by the user are transmitted through the industrial standard Ethernet.

[0047] Example 1

[0048] like Figure 2 As shown, the multi-dimensional matrix adaptation analysis method for fracturing equipment disclosed in the present invention comprises the following steps:

[0049] Step 1, obtaining the type, environment and application parameters of the fracturing equipment in the cloud database 1, and processing the obtained information to form multi-dimensional matrix type data;

[0050] like Figure 3As shown, the information of each fracturing equipment is divided into blocks of x, y, and z identification characteristics to form a storage unit 6, and the newly added equipment is stored in the multidimensional matrix type data through the supplementary blocks. x is the type parameter axis, that is, each type of fracturing equipment is digitally calibrated and counted to form x1-xN types of variable types, and its value is given an initial weighted value of 1. As the equipment failure occurs, it is analyzed that the Nth type has a failure, and its type value is accumulated by 1, that is, xN=xN+1; y is the climate environment parameter axis, and y1-yN are used to identify different climate environment parameters. The IEEE standard divides the normal climate environment into three categories: normal, unfavorable and storm disasters. These three weather conditions will have a greater impact on the component failure rate. The failure rate is theoretically a continuous function of the climate, that is, the climate conditions should be described by a continuous function or many discrete states. The typical operating environment of the fracturing equipment is divided according to the above IEEE standard. For example, y1 is initially set to a normal desert environment. Its value can be assigned an actual temperature value, or stored in a string form when considering factors such as wind, sand, and humidity, satisfying the weighted calculation formula. For example, y1=a+b+c, where a represents the temperature, b represents the wind and sand level, and c represents the air pressure; z is the application axis, and z1-zN are used to identify different application scenarios. It can be divided according to the application duration, full load rate, or number of continuous fracturing stages; it can also be calibrated according to the combined load conditions.

[0051]

[0052] The matrix consists of blocks x1y1z1-xNyNzN, each of which contains an independent timestamp and feature value combination.

[0053] Step 2, the analysis unit 2 obtains the faulty device data, and matches the corresponding matrix data based on the faulty device data;

[0054] Specifically, the faulty device data is transmitted to the fault data dynamic storage area 4 to match the containerized application module corresponding to the fault type, and the matrix data corresponding to the faulty device is searched in the static array storage area 3 .

[0055] Step 3, the operation unit 7 performs weighted processing on the faulty equipment data based on the corresponding matrix data, calculates the average failure rate of the faulty equipment through the reliability evaluation algorithm, and introduces a polymorphic climate model to calibrate the average failure rate;

[0056] Step 4, repeat the operation of step 3 for the faulty equipment using different matrix data to obtain the average failure rate under different matrix data;

[0057] Step 5: The average failure rate calculated in steps 3 and 4 is stored in the report storage 9 in a multi-dimensional rectangular form and transmitted to the cloud database 1 via a signal.

[0058] Example 2

[0059] The multi-dimensional matrix adaptation analysis method for fracturing equipment disclosed in the present invention comprises the following steps:

[0060] Step 1, obtaining the type, environment and application parameters of the fracturing equipment in the cloud database 1, and processing the obtained information to form multi-dimensional matrix type data;

[0061] Specifically, the information of each fracturing equipment is divided into blocks with x, y, and z identification characteristics to form a storage unit 6, and the newly added equipment is stored in the multidimensional matrix type data through the supplementary blocks. x is the type parameter axis, that is, each type of fracturing equipment is digitally calibrated and counted to form x1-xN types of variable types; y is the climate and environment parameter axis, and y1-yN are used to identify different climate and environment parameters; z is the application axis, and z1-zN are used to identify different application scenarios, which can be divided according to the application time, full load rate or number of continuous operation fracturing stages.

[0062] Step 2, the analysis unit 2 obtains the faulty device data, transmits the faulty device data to the fault data dynamic storage area 4 to match the containerized application module corresponding to the fault type, and searches the matrix data corresponding to the faulty device in the static array storage area 3.

[0063] Step 3, due to the triggering of fault type n, the operation unit 7 performs weighted processing on the faulty equipment data based on the corresponding matrix data, updates the multi-dimensional matrix data accordingly, and calculates the average failure rate of the faulty equipment through the reliability evaluation algorithm. The calculation formula is as follows:

[0064]

[0065] θ=1 / α

[0066] Among them, θ is the mean time between failures, α is the failure rate, and t is the equipment operation time; the purpose of calculating the mean time between failures is to find the weak links in the equipment.

[0067] The calculation formula of failure rate α is as follows:

[0068]

[0069] Among them, downtime indicates the time point when the device is deactivated, uptime indicates the time point when the device is activated, and failuretimes indicates the number of failures of the device.

[0070] The factors such as system structure, parameters and models have a strong nonlinear relationship with the reliability assessment results, and many factors are fuzzy, so the computing power of the configuration operation unit can meet the introduction of artificial neural networks (ANNs), fuzzy theory and other artificial intelligence methods. The conditional probability table is constructed in the software code, and fuzzy numbers and fuzzy subsets are used to describe the various fault states of basic events and the failure rate and failure probability of each fault state.

[0071] The multi-state climate model is introduced to calibrate the average failure rate, as shown in the following formula:

[0072]

[0073]

[0074]

[0075] Where λ is the failure rate of the component under normal weather conditions, in times / year; λ' is the failure rate of the component under adverse weather conditions, in times / year; 1 is the average failure rate, in times / year; N is the duration of normal weather, S is the duration of adverse weather; F is the proportion of adverse weather, with a value of 0 to 1.

[0076] The establishment of a polymorphic climate model is a design trend for future assessment systems, but for some specific regions, specific abnormal climate types are often relatively fixed, such as frequent strong winds and frequent clustered fog flashes in foggy areas. Conduct a detailed study of system reliability under specific weather conditions, find the weak points of system operation under specific climate conditions, propose corresponding system enhancement measures, or adopt appropriate operation strategies according to specific weather conditions to enhance the reliability of system operation.

[0077] According to the above calculation, the average failure rate of components or systems is obtained. The reliability of components and systems is calculated and accumulated step by step according to specific weather conditions, the weak points of components and system operation under specific climatic conditions are found, and corresponding enhancement measures are proposed or appropriate operation strategies are adopted according to specific weather conditions to enhance the reliability of system operation, which is of great significance to the safe and reliable operation of the system and can provide guidance for system planning.

[0078] Step 4, repeat the operation of step 3 for the faulty equipment using different matrix data to obtain the average failure rate under different matrix data;

[0079] Step 5: The average failure rate calculated in steps 3 and 4 is stored in the report storage 9 in a multi-dimensional rectangular form and transmitted to the cloud database 1 via a signal.

[0080] Example 3

[0081] like Figure 2 As shown, the multi-dimensional matrix adaptation analysis method for fracturing equipment disclosed in the present invention comprises the following steps:

[0082] Step 1, connect the analysis system of the present invention to the existing electric drive variable frequency online monitoring system through industrial Ethernet, and send instructions through the main controller in the electric drive variable frequency online monitoring system to open the interface of the analysis system of the present invention; obtain the type, environment and application parameters of the fracturing equipment in the cloud database 1, store them in the computer as a startup program package, and send data to the static array storage area 3 through the OPC data acquisition protocol, and the static array storage area 3 processes the acquired information to form multi-dimensional matrix type data;

[0083] Step 2, the parsing unit 2 obtains the faulty device data, and transmits the faulty device data to the faulty data dynamic storage area 4 to match the containerized application module corresponding to the fault type. The computing unit 7 processes the matching between the faulty data dynamic storage area 4 and the multi-dimensional matrix type data of the static array storage area 3, and checks the matrix data corresponding to the faulty device.

[0084] Step 3, due to the triggering of fault type n, the operation unit 7 performs weighted processing on the faulty equipment data based on the corresponding matrix data, updates the multi-dimensional matrix data accordingly, and calculates the average failure rate of the faulty equipment through the reliability evaluation algorithm. The calculation formula is as follows:

[0085]

[0086] θ=1 / α

[0087] Among them, θ is the mean time between failures, α is the failure rate, and t is the equipment operation time; the purpose of calculating the mean time between failures is to find the weak links in the equipment.

[0088] The calculation formula of mean time between failures θ can also be shown as follows:

[0089]

[0090] Among them, downtime indicates the time point when the device is deactivated, uptime indicates the time point when the device is activated, and failuretimes indicates the number of failures of the device.

[0091] The mean time between failures θ can be expressed using the probability density equation in the form of time t:

[0092]

[0093] Factors such as system structure, parameters, and models have a strong non-linear relationship with the reliability evaluation results, and many factors are fuzzy. Therefore, the computing power of the computing unit is configured to meet the introduction of artificial intelligence methods such as artificial neural networks (ANNs) and fuzzy theory. Construct a conditional probability table in the software code, and use fuzzy numbers and fuzzy subsets to describe the multiple failure states of basic events and the failure rates and failure probabilities of each failure state respectively.

[0094] Introduce a polymorphic climate model to calibrate the average failure rate, as shown in the following formula:

[0095]

[0096]

[0097]

[0098] Among them, λ is the failure rate of the component under normal weather conditions, with the unit of times / year; λ' is the failure rate of the component under adverse climate conditions, with the unit of times / year; λ 1 is the average failure rate, with the unit of times / year; N is the duration of normal climate, S is the duration of adverse climate; F is the proportion of adverse climate occurrence, and the value ranges from 0 to 1.

[0099] The establishment of a polymorphic climate model is the design trend of future evaluation systems. However, for certain specific regions, specific abnormal climate types are often relatively fixed, such as frequent strong winds in certain areas and frequent clustered fog flashes in foggy areas. Conduct a detailed study on the system reliability under specific weather conditions, find the weak points in the system operation under specific climate conditions, propose corresponding system enhancement measures or adopt appropriate operation strategies according to specific weather conditions to enhance the reliability of the system operation.

[0100] According to the above calculations, the average failure rate of the component or system can be obtained. Gradually calculate and accumulate the reliability of components and systems under specific weather conditions, find the weak points in the operation of components and systems under specific climate conditions, propose corresponding enhancement measures or adopt appropriate operation strategies according to specific weather conditions to enhance the reliability of system operation, which is of great significance for the safe and reliable operation of the system and can provide guidance for system planning.

[0101] Step 4, use different matrix data to repeat the operation in Step 3 for the faulty equipment, and obtain the average failure rate under different matrix data;

[0102] Step 5, the average failure rate calculated in step 3 and step 4 is stored in the report storage 9 in the form of a multi-dimensional rectangle, specifically, the operating parameters and failure rate calculation results of the faulty equipment based on each matrix data are listed, and the maximum and minimum values ​​of the weighted values ​​in each matrix data in the multi-dimensional matrix are counted; that is, the average failure rate of the equipment under different types, environments, and application scenarios is counted separately, and stored in the report storage 9. Finally, it is transmitted to the cloud database 1 through a signal.

Claims

1. Multi-dimensional matrix adaptation analysis system for fracturing equipment, It is characterized in that The industrial computer (5) comprises an analysis unit (2), the analysis unit (2) being connected to a static array storage area (3) and a fault data dynamic storage area (4) via signals, the analysis unit (2) being used to extract equipment type, environment, and application data from a cloud database (1), and to send data to the static array storage area (3) via an OPC data acquisition protocol; The static array storage area (3) and the fault data dynamic storage area (4) are respectively connected to a computing unit (7), and the computing unit (7) is connected to an output unit (8).

2. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 1, It is characterized in that The static array storage area (3) comprises a plurality of storage units (6), wherein the storage units (6) are composed of blocks having x, y, z identification characteristics, and the blocks include a timestamp and a feature quantity; The static array storage area (3) is connected to the fault data dynamic storage area (4) via an OPC data acquisition protocol.

3. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 2, It is characterized in that The fault data dynamic storage area (4) includes a plurality of containerized application modules that run according to fault type rules. The analysis unit (2) transmits the feature quantity exceeding the threshold value to the fault data dynamic storage area (4). The fault data dynamic storage area (4) identifies the containerized application module corresponding to the fault type according to the feature quantity, and sends data to the operation unit (7) through the containerized application module.

4. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 3, It is characterized in that The computing unit (7) is used for data matching and weighted degree calculation between the static array storage area (3) and the fault data dynamic storage area (4); The output unit (8) comprises a report storage (9) for storing the calculation results of the calculation unit (7) and an interface function, and the output unit (8) is connected to the cloud database (1) based on the interface function.

5. A multi-dimensional matrix adaptation analysis method for fracturing equipment, using a multi-dimensional matrix adaptation analysis system for fracturing equipment as claimed in claim 4, It is characterized in that The following steps are involved: Step 1, obtaining fracturing equipment information and processing it to form multi-dimensional matrix type data; Step 2, obtaining faulty device data, and matching corresponding matrix data based on the faulty device data; Step 3: weight the faulty equipment data based on the corresponding matrix data, calculate the average failure rate of the faulty equipment through the reliability evaluation algorithm, and calibrate the average failure rate; Step 4, repeat the operation of step 3 for the faulty equipment using different matrix data to obtain the average failure rate under different matrix data; Step 5: The average failure rate calculated in step 3 and step 4 is stored in a report memory (9) in a multi-dimensional rectangular form and transmitted to the outside of the system via a signal.

6. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 5, It is characterized in that The fracturing equipment information in step 1 is the type, environment and application parameters of the equipment; The multidimensional matrix type data formed after the processing is specifically divided into blocks of x, y, and z identification characteristics to form a storage unit (6), where x is the type parameter axis, which is digitally calibrated and statistically formed to form x1-xN types of variable types; y is the climate environment parameter axis, and y1-yN are used to identify different climate environment parameters; z is the application axis, and z1-zN are used to identify different application scenarios, which can be divided according to application time, full load rate, or number of continuous operation fracturing stages.

7. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 5, It is characterized in that The matching of the corresponding matrix data based on the faulty device data in step 2 specifically includes transmitting the faulty device data to the fault data dynamic storage area (4) to match the containerized application module of the corresponding fault type, and looking up the matrix data corresponding to the faulty device in the static array storage area (3).

8. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 5, It is characterized in that In step 3, the average failure rate of the faulty equipment is calculated by the reliability evaluation algorithm. The calculation formula is as follows: θ=1 / α Among them, θ is the mean time between failures, α is the failure rate, and t is the equipment operation time.

9. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 5, It is characterized in that The average failure rate is calibrated as described in step 3, specifically by introducing a multi-state climate model for calibration, as shown in the following formula: Where λ is the failure rate of the component under normal weather conditions, in times / year; λ' is the failure rate of the component under adverse weather conditions, in times / year; 1 is the average failure rate, in times / year; N is the duration of normal weather, S is the duration of adverse weather; F is the proportion of adverse weather, with a value of 0 to 1.

10. The multi-dimensional matrix adaptation analysis system for fracturing equipment according to claim 5, It is characterized in that The average failure rate described in step 5 is stored in the report memory (9) in the form of a multi-dimensional rectangle, specifically by listing the operating parameters and failure rate calculation results of the faulty equipment based on each matrix data, and calculating the maximum and minimum values ​​of the weighted values ​​in each matrix data in the multi-dimensional matrix, and storing them in the report memory (9).

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