A comprehensive energy system reliability detection method, device, medium, equipment and product considering positive and negative multiple dependencies of equipment

Abstract

The application discloses a kind of comprehensive energy system reliability detection methods, device, medium, equipment, product considering equipment positive and negative multiple dependence, comprising the following steps: first, the multi-energy multi-state reliability model of equipment in comprehensive energy system is built;Then according to the series-parallel relationship between different equipment in comprehensive energy system, the positive and negative dependence failure correlation between equipment is generated;Then the probability that the equipment triggered failure and the affected equipment are in each state at any time after the positive and negative dependence failure correlation trigger is calculated, to obtain the modified state probability distribution function of equipment;Finally, the reliability distribution function of comprehensive energy system is calculated by combining the modified state probability distribution function of equipment at any time and the energy output power of each state, to realize the accurate detection of dynamic reliability of comprehensive energy system.The method improves the accuracy of comprehensive energy system reliability analysis.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system technology, specifically a method, device, medium, equipment, and product for reliability testing of integrated energy systems that considers the positive and negative multiple dependencies of equipment. Background Technology

[0002] For integrated energy systems, accurate reliability assessment is not only the core basis for system design optimization, risk warning and maintenance decisions, but also a key prerequisite for maximizing system efficiency and reducing operating losses.

[0003] However, traditional reliability testing methods for integrated energy systems do not take into account the impact of the superposition of positive and negative multiple dependencies between devices on the real-time operating status of the devices. In fact, they may even increase the failure rate of the system, which may lead to drastic changes in the real-time operating status of the system. Therefore, they cannot accurately assess the reliability of integrated energy systems with multi-energy and multi-state characteristics and positive and negative multiple dependencies.

[0004] Therefore, it is necessary to propose a comprehensive energy system reliability testing method that considers the positive and negative multiple dependencies between devices. Summary of the Invention

[0005] To address the problems existing in the background art, this invention proposes a method, apparatus, medium, equipment, and product for comprehensive energy system reliability testing that considers the positive and negative multiple dependencies of equipment.

[0006] The technical solution adopted in this invention is:

[0007] In a first aspect, the present invention proposes a comprehensive energy system reliability testing method considering the positive and negative multiple dependencies of equipment, the method comprising the following steps:

[0008] Step 1: Construct multi-energy, multi-state reliability models for each device in the integrated energy system and probability distribution functions for different states;

[0009] Step 2: Generate positive and negative interdependent failure relationships between different devices in the integrated energy system based on their series and parallel connections.

[0010] Step 3: Based on the multi-energy multi-state reliability model corresponding to each device and the probability distribution function in different states, calculate the reliability of the integrated energy system under the condition that no device triggers a failure and record it as the basic reliability;

[0011] Step 4: Trigger a fault in one device in the integrated energy system, and combine the positive and negative dependency failure relationships between devices to correct the state probability distribution function of the device that triggered the fault and the affected devices, thereby obtaining the corresponding corrected state probability distribution function; combine the corrected state probability distribution function to calculate the reliability of the current integrated energy system under the fault triggered by the current device and record it as the fault system reliability corresponding to the current device.

[0012] Step 5: Repeat Step 4, change the device that triggers the failure and calculate the reliability of the integrated energy system under different device failures, thereby obtaining the failure system reliability corresponding to different devices; then optimize the reliability of the integrated energy system based on the basic reliability and the failure system reliability corresponding to different devices.

[0013] Optionally, in step one, constructing the probability distribution function for each device in the integrated energy system under different states includes:

[0014] Based on the initial probabilities of different devices in each state at the initial moment in the integrated energy system, an initial probability vector corresponding to different devices is constructed. Based on the initial probability vectors of different devices and the failure rate and repair rate of each device in each state, a Markov process algorithm is used to construct the probability distribution function of different devices in each state.

[0015] Optionally, in step four, the state probability distribution function of the currently triggered fault device and the affected device is modified based on the positive and negative dependency failure correlations between devices to obtain the corresponding modified state probability distribution function, including:

[0016] By combining the positive and negative interdependent failure relationships between devices, the failure rate and repair rate of the device that triggered the failure and the affected device are corrected at the time when the current device triggers the failure, and a corrected state transition rate matrix is ​​obtained. Using the probability of each state of the device that triggered the failure and the affected device at the time when the current device triggers the failure as the corrected initial probability vector, and combining with the corrected state transition rate matrix, the probability of the device that triggered the failure and the affected device being in each state at any time before and after the trigger is calculated, and the corrected state probability distribution function of the device that triggered the failure and the affected device is obtained.

[0017] Optionally, in step five, the reliability of the integrated energy system is optimized based on the basic reliability and the failure system reliability corresponding to different equipment, including:

[0018] Based on the basic reliability and the failure system reliability corresponding to different equipment, the reliability degradation results of all equipment are generated and sorted. From the sorting results, the key equipment in the integrated energy system that is strongly correlated with reliability is identified, and then the reliability of the integrated energy system is optimized.

[0019] Secondly, this invention proposes a comprehensive energy system reliability testing device that considers multiple positive and negative dependencies of equipment, the device comprising:

[0020] The device initial parameter construction unit is used to construct the multi-energy multi-state reliability model and the probability distribution function of each device in the integrated energy system for different states;

[0021] The correlation acquisition unit is used to generate positive and negative interdependent failure correlations between different devices in the integrated energy system based on the series and parallel connections between them.

[0022] The basic reliability calculation unit is used to calculate the reliability of the integrated energy system under the condition that all devices do not trigger failures, based on the multi-energy multi-state reliability model corresponding to each device and the probability distribution function in different states, and record it as the basic reliability.

[0023] The fault triggering unit is used to trigger a fault in a device in the integrated energy system, and, in combination with the positive and negative dependent failure correlation between devices, corrects the state probability distribution function of the currently triggered fault device and the affected devices to obtain the corresponding corrected state probability distribution function.

[0024] The fault system reliability calculation unit is used to calculate the reliability of the current integrated energy system under the fault triggered by the current equipment by combining the modified state probability distribution function and record it as the fault system reliability corresponding to the current equipment.

[0025] The optimization unit is used to optimize the reliability of the integrated energy system based on the basic reliability and the failure system reliability corresponding to different equipment.

[0026] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect.

[0027] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0028] Thirdly, the present invention provides a computer program product comprising a computer program / instruction that, when executed by a processor, implements the steps of the method described in the first aspect.

[0029] The beneficial effects of this invention are:

[0030] This invention innovates a comprehensive energy system reliability detection method by introducing positive and negative interdependent failure correlations between devices, encompassing model construction, correlation identification, parameter correction, system reliability calculation, and key device identification. This method improves the accuracy of comprehensive energy system reliability analysis by establishing a state probability correction model based on the positive and negative interdependent failure correlations between devices and linking these correlations with system reliability using a Markov process algorithm. Attached Figure Description

[0031] Figure 1 This is a flowchart of the method of the present invention.

[0032] Figure 2 It is an inter-device structure with positively dependent failure correlation.

[0033] Figure 3 It is an inter-device structure with negative dependency failure correlation.

[0034] Figure 4 A schematic diagram illustrating the changes in the reliability of the integrated energy system when considering and not considering the positive dependency failure correlation between the two devices. Detailed Implementation

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] like Figure 1 As shown, the reliability testing method for a comprehensive energy system considering multiple positive and negative dependencies of equipment proposed in this invention includes the following steps:

[0037] Step 1: Construct multi-energy, multi-state reliability models for each device in the integrated energy system, as well as probability distribution functions for different states.

[0038] The integrated energy system consists of M subsystems connected in series, each subsystem containing n parallel devices. These devices include combined heat and power (CHP) units, energy transmission lines, etc., and exhibit multi-energy, multi-state characteristics. "State" refers to the energy output power of the equipment; "multi-state" means that in addition to perfect operation and complete failure, the equipment also exists in operating states between perfect operation and complete failure. "Multi-energy" means that each state of the equipment requires multiple energy types to represent. For example, a CHP unit can output both electrical and thermal energy. Therefore, each state of a CHP unit needs to be represented by both electrical and thermal energy.

[0039] Each device has K states, and each state requires V energy parameters to represent it. The energy output power of device i in state j, considering energy type v, is expressed as follows: , , This allows us to obtain a multi-energy, multi-state reliability model for the equipment. Each equipment's multi-energy, multi-state reliability model characterizes the energy output power of each equipment under different states.

[0040] In one feasible implementation, a probability distribution function for each device in the integrated energy system under different states is constructed, including:

[0041] Based on the initial probabilities of different devices in each state at the initial moment in the integrated energy system, an initial probability vector corresponding to each device is constructed. Based on the initial probability vectors of different devices and the failure rate and repair rate of each device in each state, a Markov process algorithm is used to construct the probability distribution function of different devices in each state. Specifically:

[0042] equipment The initial probability vector of the device at time 0 is: , This represents the initial probability of being in state j. Indicates that device i is in state arrive Failure rate, , , Indicates that device i is in state arrive The repair rate. Based on the failure rate and repair rate in each state, a state transition rate matrix for device i is constructed. ,in It is the state transition rate matrix The element in the text represents the state of device i. to state The transfer rate, by and The calculation is as follows: Based on the state transition rate matrix and the initial probability vector, the probability distribution function of device i being in state j at any time is obtained using the Markov process algorithm. .

[0043]

[0044] Where s represents the column index of the matrix, from Take the value from the middle.

[0045] Step 2: Generate positive and negative interdependent failure relationships between different devices in the integrated energy system based on the series and parallel connections between them.

[0046] Specifically:

[0047] Draw a topology diagram of the equipment according to the equipment connection method of the integrated energy system. Mark the connection type of the equipment in the topology diagram and clarify which equipment is a parallel equipment group that completes the same task (such as two parallel cogeneration units that both undertake power supply and heating tasks) and which equipment is a series equipment group that completes the upstream and downstream connection task (such as transmission equipment and power generation equipment having an upstream and downstream energy transmission relationship).

[0048] When device A fails and its energy output power decreases, it leads to a decrease in the energy output power of device B, which in turn reduces the failure rate of device B and increases the failure rate of device A. This is called a negatively dependent failure correlation between devices A and B. Therefore, negatively dependent failure correlations only exist in series-connected device groups that perform upstream and downstream connection tasks. Device A, which initiates the failure, is called the triggering device (or simply the triggering device), and device B within the group that is affected is called the affected device.

[0049] When device B fails and its energy output power decreases, it leads to an increase in the energy output power of device B, which in turn increases the failure rate of device C. This increased failure rate of device B indicates a positively dependent failure correlation between devices B and C. Therefore, positively dependent failure correlations only exist in parallel device groups performing the same task. Device B, which initiates the failure, is called the triggering device, and device C, which is affected within the group, is called the affected device.

[0050] Step 3: Based on the multi-energy, multi-state reliability model corresponding to each device and the probability distribution function in different states, calculate the reliability of the integrated energy system under the condition that no device triggers a fault and record it as the basic reliability.

[0051] Step 4: Trigger a fault in one device in the integrated energy system. Based on the positive and negative dependency failure relationships between devices, correct the state probability distribution function of the device that triggered the fault and the affected devices to obtain the corresponding corrected state probability distribution function. Based on the corrected state probability distribution function, calculate the reliability of the current integrated energy system under the fault triggered by the current device and record it as the fault system reliability corresponding to the current device.

[0052] In one feasible implementation, by combining the positive and negative dependency failure correlations between devices, the state probability distribution functions of the currently triggered fault device and the affected devices are corrected to obtain the corresponding corrected state probability distribution function, including:

[0053] By combining the positive and negative dependency failure correlations between devices, the failure rate and repair rate of the device that triggered the failure and the affected device are corrected at the moment when the current device triggers the failure, and the corrected state transition rate matrix is ​​obtained, that is, the corrected state transition rate matrix after the positive dependency failure is triggered or the corrected state transition rate matrix after the negative dependency failure is triggered. Using the probability of each state of the device that triggered the failure and the affected device at the moment when the current device triggers the failure as the corrected initial probability vector, and combining with the corrected state transition rate matrix, the probability of the device that triggered the failure and the affected device being in each state at any time before and after the trigger is calculated, and the corrected state probability distribution function of the device that triggered the failure and the affected device is obtained.

[0054] For example, if a group of devices is identified as having a positively dependent failure association, the operating status of parallel devices is monitored. After a device triggers a failure and its energy output power decreases, the current positively dependent failure trigger time is recorded. The failure rate and repair rate of the device that triggered the failure and the affected device are corrected to form a corrected state transition rate matrix after the positively dependent failure trigger. Using the probability of each state of the device that triggered the failure and the affected device at the time of the positively dependent failure trigger as the corrected initial probability vector, and combined with the corrected state transition rate matrix, the probability of the device that triggered the failure and the affected device being in each state at any time before and after the positively dependent failure trigger is calculated, resulting in the corrected state probability distribution function of the device.

[0055] like Figure 2 As shown, assume that devices B and C in the integrated energy system are a group of parallel devices that perform the same task and have a positive dependency failure relationship. Device B was operating normally before the specified time. When device B malfunctions, its power output will decrease, and its failure rate will drop from [previous value]. Become Repair rate from Become Because devices B and C have a positively dependent failure correlation, when When device B malfunctions, the failure rate of device C will increase. The failure rate of device C will change from... Become Repair rate from Become Device B is the device that triggered the fault, and device C is the affected device.

[0056] Before time step 1, device B is operating normally, and its state transition rate matrix can be represented as follows: .in Indicates that device B is in state to state The transfer rate, , ,Depend on and The calculation yielded the result.

[0057]

[0058] The initial probability vector of device B at time 0 is a known quantity, denoted as: Based on the state transition rate matrix and initial probability vector from step one, the Markov process algorithm is used to obtain the state transition rate matrix and initial probability vector for any given state. Device B is in state probability distribution function , .

[0059]

[0060] Similarly, Before time t, device C is operating normally, and its state transition rate matrix can be represented as follows: .in, Indicates that device C is in state to state The transfer rate , ,Depend on and The initial probability vector of device C at time 0 is calculated as follows: Based on the state transition rate matrix and initial probability vector from step one, the state of device C at any time t is determined using the Markov process algorithm. probability distribution function .

[0061] exist When device B fails, its failure rate and repair rate change, and its state transition rate matrix is ​​corrected to a modified state transition rate matrix. , Indicates that device B is in state to state The transfer rate, by and Calculated from... At time t, the initial probability vector of device B is corrected to Based on the corrected state transition rate matrix and the corrected initial probability vector, the state of device B at any time t is obtained using the Markov process algorithm. The probability distribution function is :

[0062]

[0063]

[0064] Thus combining and At all times, device B is in any Always in a state of readiness Corrected probability distribution function It can be represented as:

[0065]

[0066] Similarly, in At time t, device C is the affected device. The failure rate and repair rate of device C change, and its state transition rate matrix is ​​corrected to the modified state transition rate matrix. , Indicates that device C is in state to state The transfer rate, by and Calculated from... At time t, the initial probability vector of device C is corrected to Based on the corrected state transition rate matrix and the corrected initial probability vector, the state of device C at any time t is obtained using the Markov process algorithm. The probability distribution function is Thus combining and At any given time t, device C is in state t. Corrected probability distribution function It can be represented as:

[0067]

[0068] For example, if a group of devices is identified as having a negatively dependent failure association, the operating status of the series-connected devices is monitored. After a device triggers a failure and its energy output power decreases, the current time of triggering the negatively dependent failure is recorded. The failure rate and repair rate of the device that triggered the failure and the affected device are corrected to form a corrected state transition rate matrix after the negatively dependent failure is triggered. Using the probability of each state of the device that triggered the failure and the affected device at the time of triggering the negatively dependent failure as the corrected initial probability vector, and combining it with the corrected state transition rate matrix, the probability of the device that triggered the failure and the affected device being in each state at any time after the negatively dependent failure is triggered is calculated, resulting in the corrected state probability distribution function of the device.

[0069] like Figure 3 As shown, assume that equipment A and equipment D in the integrated energy system are a series equipment group that performs the same task, and there is a negative dependency failure correlation relationship. Device A was operating normally before the specified time. When device A malfunctions, its power output will decrease, and the failure rate of device A will drop from [previous value]. Become Repair rate from Become Because equipment A and equipment D have a negative dependency failure relationship, when When device A malfunctions, the failure rate of device D will decrease, and the failure rate of device D will change from... Become Repair rate from Become Device A is the device that triggered the fault, and device D is the device that was affected.

[0070] Before time t, device A is operating normally, and its state transition rate matrix can be represented as follows: .in Indicates that device A is in state to state The transfer rate , ,Depend on and The calculation yielded the result.

[0071]

[0072] The initial probability vector of device A at time 0 is a known quantity, denoted as: Using the state transition rate matrix and initial probability vector from step one, the state of device A at any time t can be obtained. probability distribution function .

[0073]

[0074] Similarly Before time step D, device D is operating normally, and its state transition rate matrix can be represented as follows: .in Indicates that device D is in state to state The transfer rate , ,Depend on and The initial probability vector of device D at time 0 is calculated as follows: Using the state transition rate matrix and initial probability vector from step one, the state of device D at any time t can be obtained. probability distribution function .

[0075] exist When device A fails, its failure rate and repair rate change, and its state transition rate matrix is ​​corrected to a modified state transition rate matrix. , Indicates that device A is in state to state The transfer rate, by and Calculated from... At time t, the initial probability vector of device A is corrected to The state of device B at any time t is obtained by using the modified state transition rate matrix and the modified initial probability vector. The probability distribution function is :

[0076]

[0077]

[0078] Thus combining and At any given time t, device A is in state t. Corrected probability distribution function It can be represented as:

[0079]

[0080] Similarly, in At time D, the affected device, its failure rate and repair rate change, and its state transition rate matrix is ​​corrected to the corrected state transition rate matrix. , Indicates that device D is in state to state The transfer rate, by and Calculated from... At time t, the initial probability vector of device C is corrected to The state of device D at any time t is obtained by using the modified state transition rate matrix and the modified initial probability vector. The probability distribution function is Thus combining and At any given time t, device D is in state t. Corrected probability distribution function It can be represented as:

[0081]

[0082] In one feasible implementation, the reliability of the current integrated energy system under the current equipment-triggered failure is calculated by combining the modified state probability distribution function and denoted as the failure system reliability corresponding to the current equipment, including:

[0083] Based on the corrected device state probability distribution function at any time, and combined with the energy output power of each device at any time in each state, the reliability distribution function of the integrated energy system is calculated based on the Lz algorithm. This yields the probability of the integrated energy system being in each state at any time before and after the positive and negative dependent failures are triggered, as well as the energy output power of the corresponding state, thereby detecting the dynamic reliability of the integrated energy system.

[0084] Specifically as follows:

[0085] Based on the probability distribution function of device i being in state j in step one, the following is given: The energy output power in state j is The reliability distribution function of device i is constructed using the Lz algorithm. :

[0086]

[0087] If device i, as the device triggering the fault, has a positive or negative dependent failure correlation, then... Update to the corrected state probability distribution function or If device i, as the affected device, has a positive or negative dependent failure relationship, then... Update to the corrected state probability distribution function or .

[0088] Furthermore, based on the reliability distribution function of device i, the Lz algorithm is used to construct the reliability distribution function of the integrated energy system. :

[0089]

[0090] Where sys represents the state number of the integrated energy system. This represents the state probability distribution function of the integrated energy system in state j. This represents the energy output power of the integrated energy system in state j. This represents the energy output power of the integrated energy system in state j, considering energy type v.

[0091] If the devices are connected in series, then

[0092] If the devices are connected in parallel, then .

[0093] Therefore, the dynamic reliability of the integrated energy system can be achieved through reliability operators. Solution:

[0094]

[0095]

[0096] in, This indicates that the integrated energy system takes into account energy types. Energy demand.

[0097] Step 5: Repeat Step 4, change the device that triggers the failure and calculate the reliability of the integrated energy system under different device failures, thereby obtaining the failure system reliability corresponding to different devices; then optimize the reliability of the integrated energy system based on the basic reliability and the failure system reliability corresponding to different devices.

[0098] In one feasible implementation, the reliability of the integrated energy system is optimized based on the basic reliability and the failure system reliability corresponding to different equipment, including:

[0099] The failure system reliability for different devices represents the curves and numerical characteristics of system reliability over operating time. Based on the basic reliability and the failure system reliability for different devices, the reliability degradation results for all devices are generated and ranked. From the ranking results, key devices strongly correlated with reliability in the integrated energy system are identified. For example, key devices strongly correlated with reliability are those that have a significant impact on reliability degradation; that is, these key devices, as triggering devices, will cause a significant decrease in the reliability of the integrated energy system. Key devices are a set of pre-defined devices in the integrated energy system, and those with a reliability degradation greater than a pre-defined threshold are considered to have a significant impact on reliability degradation. This leads to optimization of the reliability of the integrated energy system. For example, based on the list of key devices with a significant impact on reliability degradation, targeted strategies such as device maintenance, optimization of device topology, and reasonable allocation of device redundancy quantity and location are developed for the integrated energy system to help improve the overall reliability and operational stability of the integrated energy system.

[0100] The following analysis uses positive dependency failure correlation as an example to examine the impact of dependency failure correlation on the dynamic reliability test results of an integrated energy system. Assume the integrated energy system consists of two combined heat and power (CHP) units (equipment B and C), both capable of generating and supplying heat, and exhibiting three operating states. Figure 4 This demonstrates the changes in the reliability of the integrated energy system when the positive dependency failure correlation between devices B and C is considered and not considered. Figure 4 It can be seen that at time t=30000h, device B fails, and the system reliability decreases. The results show that considering the positive dependency failure correlation between devices has a significant impact on the dynamic reliability detection results of the integrated energy system. Therefore, this invention helps to improve the accuracy of integrated energy system reliability analysis by establishing a state probability correction model caused by the positive and negative dependency failure correlation between devices.

[0101] Finally, it should be noted that the above embodiments and descriptions are only used to illustrate the technical solutions of the present invention and not to limit it. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the disclosure of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for reliability testing of a comprehensive energy system considering multiple positive and negative dependencies of equipment, characterized in that, Includes the following steps: Step 1: Construct multi-energy, multi-state reliability models for each device in the integrated energy system and probability distribution functions for different states; Step 2: Generate positive and negative interdependent failure relationships between different devices in the integrated energy system based on their series and parallel connections. Step 3: Based on the multi-energy multi-state reliability model corresponding to each device and the probability distribution function in different states, calculate the reliability of the integrated energy system under the condition that no device triggers a failure and record it as the basic reliability; Step 4: Trigger a fault in one device in the integrated energy system, and combine the positive and negative dependency failure relationships between devices to correct the state probability distribution function of the device that triggered the fault and the affected devices, thereby obtaining the corresponding corrected state probability distribution function; combine the corrected state probability distribution function to calculate the reliability of the current integrated energy system under the fault triggered by the current device and record it as the fault system reliability corresponding to the current device. In step four, the state probability distribution functions of the currently triggered fault device and the affected device are corrected based on the positive and negative interdependent failure relationships between devices, resulting in the corresponding corrected state probability distribution function, including: By combining the positive and negative dependent failure correlations between devices, the failure rate and repair rate of the device that triggered the failure and the affected devices are corrected at the moment when the current device triggers the failure, and a corrected state transition rate matrix is ​​obtained. Using the probability of each state of the device that triggered the fault and the affected device at the moment the fault was triggered as the corrected initial probability vector, and combined with the corrected state transition rate matrix, the probability of the device that triggered the fault and the affected device being in each state at any time before and after the trigger is calculated, and the corrected state probability distribution function of the device that triggered the fault and the affected device is obtained. Step 5: Repeat Step 4, change the device that triggers the failure and calculate the reliability of the integrated energy system under different device failures, thereby obtaining the failure system reliability corresponding to different devices; then optimize the reliability of the integrated energy system based on the basic reliability and the failure system reliability corresponding to different devices.

2. The method for reliability testing of a comprehensive energy system considering multiple positive and negative dependencies of equipment according to claim 1, characterized in that, In step one, the probability distribution function for each device in the integrated energy system under different states is constructed, including: Based on the initial probabilities of different devices in each state at the initial moment in the integrated energy system, an initial probability vector corresponding to different devices is constructed. Based on the initial probability vectors of different devices and the failure rate and repair rate of each device in each state, a Markov process algorithm is used to construct the probability distribution function of different devices in each state.

3. The method for reliability testing of a comprehensive energy system considering multiple positive and negative dependencies of equipment according to claim 1, characterized in that, In step five, the reliability of the integrated energy system is optimized based on the basic reliability and the failure system reliability corresponding to different equipment, including: Based on the basic reliability and the failure system reliability corresponding to different equipment, the reliability degradation results of all equipment are generated and sorted. From the sorting results, the key equipment in the integrated energy system that is strongly correlated with reliability is identified, and then the reliability of the integrated energy system is optimized.

4. A comprehensive energy system reliability testing device considering multiple positive and negative dependencies of equipment, characterized in that, include: The equipment initial parameter construction unit is used to construct the multi-energy multi-state reliability model and the probability distribution function of each device in the integrated energy system for different states; The correlation acquisition unit is used to generate positive and negative interdependent failure correlations between different devices in the integrated energy system based on the series and parallel connections between them. The basic reliability calculation unit is used to calculate the reliability of the integrated energy system under the condition that all devices do not trigger failures, based on the multi-energy multi-state reliability model corresponding to each device and the probability distribution function in different states, and record it as the basic reliability. The fault triggering unit is used to trigger a fault in one of the devices in the integrated energy system. Combining the positive and negative interdependent failure relationships between devices, it corrects the state probability distribution function of the currently faulty device and the affected devices to obtain the corresponding corrected state probability distribution function. Specifically, it includes: By combining the positive and negative interdependent failure correlations between devices, the failure rate and repair rate of the device that triggered the failure and the affected device are corrected at the time when the current device triggers the failure, and a corrected state transition rate matrix is ​​obtained. Using the probability of each state of the device that triggered the failure and the affected device at the time when the current device triggers the failure as the corrected initial probability vector, and combined with the corrected state transition rate matrix, the probability of the device that triggered the failure and the affected device being in each state at any time before and after the trigger is calculated, and the corrected state probability distribution function of the device that triggered the failure and the affected device is obtained. The fault system reliability calculation unit is used to calculate the reliability of the current integrated energy system under the fault triggered by the current equipment by combining the modified state probability distribution function and record it as the fault system reliability corresponding to the current equipment. The optimization unit is used to optimize the reliability of the integrated energy system based on the basic reliability and the failure system reliability corresponding to different equipment.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the comprehensive energy system reliability detection method that considers the positive and negative multiple dependencies of the equipment as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the comprehensive energy system reliability detection method that considers the positive and negative multiple dependencies of the equipment as described in any one of claims 1 to 3.

7. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the comprehensive energy system reliability testing method that considers the positive and negative multiple dependencies of the device as described in any one of claims 1 to 3.

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