Equipment fault data aggregation method and system

Abstract

The invention discloses an equipment fault data aggregation method and system. The method comprises the following steps: acquiring fault data generated when equipment runs through an equipment sensor; noise disturbance is added to the collected fault data and segmentation is carried out, and then the segmented noise-added data is sent to a third-party aggregator; the third-party aggregator aggregates the received data and sends aggregated data to the control center, and the control center recovers the aggregated data and uploads the aggregated data to the block chain network; when a query request is initiated to the block chain, the block chain performs noise addition and summation on the equipment fault data and feeds back a query result, so that individual equipment fault data and privacy are hidden in all equipment fault data. According to the method, data aggregation, differential privacy and Shamir threshold secret sharing schemes are ingeniously combined, sensitive information can be effectively prevented from being leaked, the integrity of data in the transmission and processing process can be guaranteed, and all-around safety protection is provided for equipment fault data.

Description

Technical Field

[0001] The present invention belongs to the field of blockchain and privacy security, and in particular to a method and system for aggregating equipment fault data. Background Art

[0002] With the continuous advancement of modern industrial Internet of Things technology, a large number of industrial equipment have achieved wireless communication and comprehensive data sharing with the help of cutting-edge technologies such as digital sensing technology and communication technology. The data generated in this sharing process, as an emerging resource form, not only has far-reaching commercial research value, but also has the potential to promote the innovation of the current industrial model. In this context, Machine as a Service (MaaS) has emerged as an emerging industrial equipment service model. Original Equipment Manufacturers (OEM) rent industrial equipment with data collection and communication functions to ETO-type enterprises, helping ETO-type enterprises to quickly build factories and start production lines, reduce production costs, and improve production efficiency. At the same time, device sensors (DS) collect equipment failure data generated during the operation of industrial equipment. OEMs analyze these failure data to discover performance defects in industrial equipment, and then perform predictive maintenance, optimize and update industrial equipment. However, these equipment failure data contain the production privacy of ETO-type enterprises. For example, OEMs can analyze equipment failure data to infer the privacy data of ETO-type enterprises, such as production efficiency, product technology, customer needs, etc. Therefore, how to ensure the privacy of data during the transmission process and the final report reading stage becomes the primary issue at this stage. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method and system for aggregating equipment fault data.

[0004] The technical solution provided by the present invention is as follows:

[0005] A method for aggregating equipment fault data comprises the following steps:

[0006] S1. Collect equipment failure data generated when the enterprise equipment is running through the equipment sensor DS;

[0007] S2, adding noise disturbance to the collected equipment failure data and segmenting it, and then sending the segmented noisy data to the third-party aggregator TPA;

[0008] S3. The third-party aggregator TPA aggregates the received data and sends the aggregated data to the control center CC, which restores the aggregated data and uploads it to the blockchain network.

[0009] S4. When a query request is initiated to the blockchain network, the blockchain network performs noise addition and summation on the equipment fault data according to the query range and feeds back the query result.

[0010] Furthermore, step S1 also includes an initialization phase: initializing the blockchain network, completing node settings, authority allocation, and identity authentication; registering device sensors DS under the jurisdiction of the same data provider DD with all third-party aggregators TPA employed by the data provider DD, and setting privacy parameters.

[0011] Further, step S2 includes:

[0012] S201, the equipment sensor DS collects the occurrence times of various equipment failure data, and initializes the occurrence times of these equipment failures into an equipment failure data array;

[0013] S202, the device sensor DS adds disturbance noise to the device fault data array through the DP disturbance algorithm;

[0014] S203, the device sensor DS divides the disturbed device fault data into m parts according to the Shamir threshold secret sharing scheme and shares the relevant data with the third-party aggregator TPA;

[0015] S204. After the third-party aggregator TPA receives the shared data, it saves the shared data if the verification passes.

[0016] Furthermore, the DP perturbation algorithm comprises the steps of:

[0017] Generate differential privacy noise NMnoise according to privacy parameters ε and δ, and superimpose it on the device failure data to obtain the disturbed device failure data x i ' j =x ij +NMnoise,x ij This is the equipment failure data before adding noise disturbance;

[0018] Compare the differences in equipment failure data before and after adding noise disturbance: If |x ij -x i ' j |≤b, the disturbance equipment failure data is maintained within this interval with probability p; if |x ij -x i ' j |>b, the disturbance equipment failure data is maintained within this interval with probability q; where,

[0019] Further, step S3 includes:

[0020] S301, the third-party aggregator TPA determines the share of received data and sums the data;

[0021] S302, the third-party aggregator TPA sends the aggregated data obtained by summing up to the control center CC. If TPA does not receive all the shares, or part of the received data is wrong or tampered with, TPA will not send the aggregated data to the control center CC;

[0022] S303. After the control center CC receives the aggregated data shared by TPA, it restores the equipment fault data according to the Shamir threshold secret sharing scheme and uploads it to the blockchain network.

[0023] Further, step S4 includes:

[0024] S401. After receiving the query request, the blockchain network obtains the data period and data range to be queried;

[0025] S402, searching the account book storage data through the index, adding noise to the stored fault data and feeding back:

[0026] If you query the equipment failure data in a single area, there is no need to perform a summation operation, and the equipment failure data with noise in a region is randomly returned;

[0027] If the equipment failure data in multiple but not all regions are queried, the equipment failure data in multiple regions are randomly selected, noise is added and then summed; in this case, the noise added to the equipment failure data in each region is mutually accumulated during the summing process and cannot be eliminated, resulting in each failure data being affected by strong noise, thereby changing the aggregation trend of the equipment failure data in the queried region, making the equipment failure data trend in a single region hidden in the aggregation trend of the equipment failure data in the queried region;

[0028] If the equipment failure data in all areas are queried, the equipment failure data in all areas are noised and then summed up; at this time, since the queried area range is the same as the stored data range, the noise in the equipment failure data in each area cancels each other out after summing, and the equipment failure data trend in all areas can be obtained.

[0029] A device fault data aggregation system based on the above method, the system mainly includes:

[0030] Initialization module, used to initialize the blockchain network and set privacy parameters;

[0031] A data acquisition module is used to collect equipment fault data through a number of equipment sensors DS;

[0032] The data disturbance module is used to add noise to the collected equipment failure data and segment the noisy data;

[0033] The data aggregation module is used to aggregate and restore the received noisy data and upload it to the blockchain network;

[0034] The parameter update module is used to update the privacy parameters as needed after each round of data aggregation is completed.

[0035] Furthermore, a smart contract is provided in the blockchain network, and a storage module, a DP module and a query module are provided in the smart contract; the storage module is used to store equipment fault data, the DP module is used to add DP noise to the equipment fault data, and the query module calls the DP module according to the query request to add noise and sum the equipment fault data in the storage module and feed back the query result.

[0036] Furthermore, the data disturbance module adds disturbance noise to the equipment fault data through the DP disturbance algorithm, and divides the disturbed equipment fault data into several parts according to the Shamir threshold secret sharing scheme and then shares them with the data aggregation module.

[0037] Furthermore, the data aggregation module includes a third-party aggregator TPA and a control center CC; the third-party aggregator TPA is used to sum the received shared data, and after receiving all the shared data, send the summed aggregated data to the control center CC; the control center CC restores the received aggregated data into equipment fault data according to the Shamir threshold secret sharing scheme and uploads it to the blockchain network.

[0038] Compared with the prior art, the present invention has at least the following beneficial effects:

[0039] 1. Enhanced privacy protection capability: The present invention cleverly combines data aggregation, differential privacy and Shamir threshold secret sharing scheme, which can not only effectively prevent the leakage of sensitive information, but also ensure the integrity of data during transmission and processing, providing all-round security protection for equipment fault data.

[0040] 2. Improve data utilization and analysis accuracy: Traditional technologies may reduce data availability or introduce additional errors due to over-protection. The present invention avoids these problems by precisely controlling the privacy budget and optimizing the encryption method, so that the data value can be fully utilized while maintaining high analysis quality.

[0041] 3. Good fault tolerance and scalability: Faced with large amounts of data and changing sources, the present invention has a powerful fault tolerance mechanism that can effectively deal with situations such as device offline or network anomalies to ensure stable system operation; and it is easy to expand horizontally to support a wider range of application scenarios and larger data sets.

[0042] 4. Flexible adaptation to various business needs: Compared with a single technical path, the present invention provides a configurable framework that can adjust parameter settings according to the characteristics of different industries and enterprise needs. It is suitable for equipment manufacturers to perform predictive maintenance and can also serve third-party operation and maintenance organizations for fault diagnosis.

[0043] In summary, the present invention has found the best balance between privacy protection and data value release, which not only enhances the competitiveness of the industry, but also lays a solid foundation for building a healthy and sustainable digital ecological environment. With the help of efficient data processing processes and technical means, OEM manufacturers, ETO companies and other stakeholders can carry out in-depth cooperation on the basis of protecting their respective rights and interests, promote the upgrading and transformation of the entire industrial chain, and create more business opportunities and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0045] Figure 1 It is a schematic diagram of the architecture of a method for aggregating equipment fault data provided by an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of a flow chart of a disturbance phase provided by an embodiment of the present invention;

[0047] Figure 3 It is a schematic diagram of a process of a polymerization stage provided by an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of data summation during query in the reading phase provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without making creative work are all within the scope of protection of the present invention.

[0050] Embodiment 1

[0051] This embodiment provides a method for aggregating device fault data. Figure 1 ,This method mainly includes five stages: initialization stage, perturbation stage, aggregation stage, reading stage and ,update stage.

[0052] (1) The first phase, called the Initialization Phase (IP), is to initialize the blockchain network. The OEM distributes their IDs to the DD and DS, and the DS collects fault data. DD (Data Donor) refers to the party that provides the original equipment operation data, usually the equipment user or industrial manufacturing enterprise. They contribute equipment fault data for analysis, but are worried about the exposure of their privacy.

[0053] The initialization phase includes network initialization and parameter initialization.

[0054] Network initialization: Before fault data sharing, network initialization needs to be completed. This embodiment selects Hyperledger Fabric as the blockchain network platform to perform a series of tasks such as node setting, authority allocation, and identity authentication for OEM, ETO, and TPA. The main function of TPA (Third Party Aggregator) is to integrate data sources from multiple DSs.

[0055] Parameter initialization: DSs under the jurisdiction of the same DD register all TPAs ​​employed by the DD. OEM will send DS i The hash value of the real identity to the corresponding TPA.

[0056] Pr[M(D)∈S]≤e ε Pr[M(D′)∈S]+δ,

[0057] According to the above formula, a smaller privacy parameter ε means stronger privacy, but the result has interference information, while a larger privacy parameter ε means poorer privacy but more accurate. Therefore, the protection effect of fault data privacy can be adjusted by the ε value. Set the privacy parameter set ε = {ε1,ε2,Λ,ε n}, so that different privacy protection effects can be obtained by using different permissions to aggregate fault data. In the following, ε1 will be used as an example.

[0058] (2) The second phase, called the Distribution Phase (DP), adds noise to the equipment failure data collected by DS and segments the noisy data according to the Shamir threshold secret sharing scheme.

[0059] After completing the initialization, DS collects equipment failure data and shares it to the Fabric blockchain platform for later access. Next, each DS will send the secret shared data generated by the sum of equipment failure data and noise to the corresponding TPA. When a DS sends secret shared data, it will attach its own identity information, current time, and privacy parameters for TPA to verify to ensure the integrity of the data and prevent the data from being forged or tampered with. t DS in the jurisdiction i For example, the following describes the operations it performs in detail:

[0060] DS i Collect the occurrence count of various fault data {x i1 ,x i2 ,...,x ij ,...,x iM}(1≤i≤n,1≤j≤M), where x ij Indicates in DS i The number of occurrences of the jth type of fault in the fault data array Data is initialized with the number of occurrences of these faults i ={x i1 ,x i2 ,...,x ij ,...,x iM}.

[0061] In order to protect production privacy from the threat of faulty data leakage, DSi i Perform DP (DifferentialPrivacy,

[0062] Differential privacy) perturbation, the perturbation process is completed by Algorithm 1. In Algorithm 1, q and p represent the two data perturbation probabilities, and b represents the difference between the original fault data and the perturbation value. Differential privacy noise NMnoise and perturbed fault data x that conform to the Gaussian distribution are generated by ε and δ. i ' j =x ij +NMnoise. The mechanism then compares the difference between the original value of the data and the perturbed value. If |x ij -x i ' j |≤b, the perturbation data is maintained within this interval with a greater probability p; if |x ij -x i ' j |>b, the perturbed data is maintained within the interval with a small probability q (refer to the paper "Mean Estimation Mechanism under (ε, δ)-Local Differential Privacy Model"). According to the above steps, the fault data is traversed and noised.

[0063]

[0064] DS i According to the Shamir-(k,m) threshold secret sharing scheme, the disturbed device fault data Data i ′={x i ′1,x i ′2,Λ,x i ' j ,Λ,x i ' M} is divided into m parts, that is

[0065]

[0066] Among them, 1≤j≤M, 1≤l≤m.

[0067] DS i TPA l Send [i,rd,sh l (s ij ),ε1,H(h(ID DSi )||rd||sh l (s ij )||ε1)], where rd is the current return

[0068] composite number, ε1 is the privacy parameter, sh l (s ij ) = f k (s ij ,l)modq,(l=1,2,Λ,m).

[0069] In TPA l After receiving the data, DS will be used i The H function corresponding to the identity ID, local rd, and privacy parameter ε1

[0070] To verify sh l (s ij ). If the result is correct, the shared data will be saved, otherwise it will be discarded.

[0071] It is worth noting that the maximum power of the Shamir-(k,m) threshold secret sharing scheme polynomial used by each DS is the same, i.e. k-1. However, their coefficients are different and kept secret from other DSs. Similarly, other DSs will send their fault sharing data to the corresponding TPA in the same way. The whole process is as follows: Figure 2 shown.

[0072] (3) In the third phase, called the Aggregation Phase (AP), TPA calculates the sum of the corresponding secret fragments, and then the Control Center (CC) recovers the data through the Shamir threshold secret sharing scheme and adds noise to the data to ensure that the privacy trend is not leaked and fault tolerance is met.

[0073] When TPA receives fault sharing data, it will first verify the data. If the verification passes, TPA will keep the data, otherwise it will discard the data. If everything goes well, DD t Each TPA in the range will get a data set consisting of the received fault sharing data. l have:

[0074]

[0075] Once TPA l Receive n shares of data from DSs under its jurisdiction, sum these data and determine

[0076]

[0077] Due to the homomorphic property, j is the share of the sum of the fault data collected by DS and the DP noise. t Each TPA in range starts aggregating the received secret sharing data and determines a different share.

[0078] In DD t TPAs within the scope (such as TPA l ) completes the summation of the received shares, it will aggregate the data [t,j,l,ε1,(Sh l (1),Sh l (2),...,Sh l (j),...,Sh l (M))] is sent to CC, and the same is true for the rest of the aggregators. This stage continues until k shares of the sum are collected. If TPA does not collect all the shares or some of the received data is wrong or tampered, it will not send the aggregated data to CC. When CC collects k shares of faulty shared data, it is able to recover the data through the Lagrange interpolation theorem.

[0079] Data t ->[ε1,[X t1 ,X t2 ,...,X tj ,...,X tM ]]

[0080] This is obtained by interpolating the Shamir-(k,m) threshold secret sharing scheme in DD t The DS within the range of each type of fault readings. Other DDs and their respective TPAs ​​perform type aggregation operations, and finally CC will obtain z fault data sets. The whole process of this stage is as follows Figure 3 After that, the secret sharing data is uploaded to the prepared Fabric blockchain network platform.

[0081] (4) The fourth phase, called the Reading Phase (RP), is to read the fault data.

[0082] In this stage, the OEM sends a query transaction to the Fabric blockchain network platform to obtain various types of fault data of ETO-type enterprises, analyze equipment defects, optimize equipment performance, and provide ETO with predictive maintenance and other related services. The Fabric blockchain network has a smart contract, which has a storage module, a DP module, and a query module. The storage module is used to store multi-dimensional equipment fault data; the query module is used to judge the query request and perform corresponding operations; the DP module is used to add DP noise to the multi-dimensional equipment fault data according to the algorithm. The specific steps of this stage are:

[0083] Step 1: The OEM sends a query request to the Fabric platform to access fault data. The query transaction q = [id, number, startTime, endTime, query], where id refers to the ID of the queryer, which is used to determine the query authority; startTime and endTime refer to the time period in which the data is uploaded; number refers to the number of areas to be queried.

[0084] Step 2: After the chain code receives the query service, it searches the ledger for the stored data Data' through the index, and calls the classification query algorithm 2 based on the DP algorithm to sum the data. The specific steps are as follows:

[0085] The DP algorithm first generates a noise array noise according to the fault type, and generates z superimposed noise noise for each fault. tj Specifically, the algorithm first generates z-1 differential privacy noises that conform to the Laplace distribution, and then sums and inverts the first z-1 noises to get the zth noise. tj satisfy That is, the noise superimposed on the data of the same fault type can cancel each other out when all the data of the fault type are aggregated.

[0086]

[0087] Then, according to different query requests from the OEM, the failure data of each type is aggregated. The aggregation summation includes the following three cases:

[0088] Case1: When number == 1, that is, query the device failure data within a single area. At this time, there is no need to perform a summation operation, and randomly return the device failure data with added noise within an area.

[0089] Case2: When 1 < number < z, that is, query the device failure data within some areas. Randomly select the device failure data of number areas for summation. Since 1 < number < z, it does not meet the condition, resulting in noise tj accumulating with each other during the summation process and cannot be eliminated. Eventually, the failure data of each type will be affected by strong noise noise tj and thus change the aggregation trend of the device failure data within the queried area, making the trend of the device failure data within a single area hidden in the aggregation trend of the device failure data within the queried area.

[0090] Case3: When number == z, that is, query the device failure data within all areas. The process will follow the steps of Case2 and perform summation according to each failure type of the device. At the same time, because number == z, the perturbation noise noise tj can cancel each other out when summing according to the failure type, that is return the result of the device failure data within all areas as Figure 4 shown.

[0091] (5) The fifth stage, called the Update Phase (UP), after one round of data aggregation, each device increments the round number rd by 1. And according to specific requirements, update the privacy parameter set ε.

[0092] In summary, before uploading the device failure data to the blockchain, the method of this embodiment will perform fault-tolerant processing on the device failure data based on differential privacy and Shamir threshold secret sharing scheme. In addition, in the specific scenario of the inquirer and the feedback of the device failure data, in view of the potential privacy snooping behavior of the inquirer, if a targeted query is made on the device failure data in a certain area, the smart contract will add noise sufficient to distort various types of failure data and there will be no noise offset for the failure data in other areas, so that the device failure data and privacy in a single area are hidden in the aggregated data, thereby making the failure data in the query area unavailable; when the inquirer queries the device failure data in all areas, the smart contract will add noise sufficient to distort various types of data, but it can be offset by the superimposed noise in other areas, thereby meeting the availability.

[0093] Embodiment 2

[0094] Based on the above method, this embodiment provides a device fault data aggregation system, which mainly includes:

[0095] Initialization module, used to initialize the blockchain network and set privacy parameters;

[0096] A data acquisition module is used to collect equipment fault data through a number of equipment sensors DS;

[0097] The data disturbance module is used to add noise to the collected equipment failure data and segment the noisy data;

[0098] The data aggregation module is used to aggregate and restore the received noisy data and upload it to the blockchain network;

[0099] The parameter update module is used to update the privacy parameters as needed after each round of data aggregation is completed.

[0100] A smart contract is set up in the blockchain network, and a storage module, a DP module and a query module are set up in the smart contract; the storage module is used to store equipment fault data, the DP module is used to add DP noise to the equipment fault data, and the query module calls the DP module according to the query request to add noise and sum the equipment fault data and feedback the query result.

[0101] The data perturbation module adds perturbation noise to the equipment fault data through the DP perturbation algorithm, and divides the perturbed equipment fault data into several parts according to the Shamir threshold secret sharing scheme and shares them with the data aggregation module. The data aggregation module includes a third-party aggregator TPA and a control center CC; the third-party aggregator TPA is used to sum the received several shared data, and after receiving all the shares of shared data, the summed aggregated data is sent to the control center CC; the control center CC restores the received aggregated data to equipment fault data according to the Shamir threshold secret sharing scheme and uploads it to the blockchain network.

[0102] The above-mentioned system can be deployed to implement the equipment fault data aggregation method described in Example 1, and has the corresponding functional modules and beneficial effects of the method. For technical details not described in detail in this embodiment, please refer to the equipment fault data aggregation method provided in Example 1 of the present invention.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may also be combined, the steps may be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for aggregating equipment fault data, characterized in that: The following steps are involved: S1. Collect equipment failure data generated by the operation of enterprise equipment through equipment sensor DS; S2, adding noise disturbance to the collected equipment failure data and segmenting it, and then sending the segmented noisy data to the third-party aggregator TPA; S3. The third-party aggregator TPA aggregates the received data and sends the aggregated data to the control center CC, which restores the aggregated data and uploads it to the blockchain network. S4. When a query request is initiated to the blockchain network, the blockchain network performs noise addition and summation on the equipment fault data according to the query range and feeds back the query result.

2. A method for aggregating equipment fault data according to claim 1, characterized in that: Step S1 also includes an initialization phase: initializing the blockchain network, completing node settings, authority allocation, and identity authentication; registering all third-party aggregators TPA employed by the same data provider DD with the device sensors DS under the jurisdiction of the data provider DD, and setting privacy parameters.

3. A method for aggregating equipment fault data according to claim 1, characterized in that: Step S2 includes: S201, the equipment sensor DS collects the occurrence times of various equipment failure data, and initializes the occurrence times of these equipment failures into an equipment failure data array; S202, the device sensor DS adds disturbance noise to the device fault data array through the DP disturbance algorithm; S203, the device sensor DS divides the disturbed device fault data into m parts according to the Shamir threshold secret sharing scheme and shares the relevant data with the third-party aggregator TPA; S204. After the third-party aggregator TPA receives the shared data, it saves the shared data if the verification passes.

4. A method for aggregating equipment fault data according to claim 3, characterized in that: The DP perturbation algorithm comprises the steps of: Generate differential privacy noise NMnoise according to privacy parameters ε and δ, and superimpose it on the device failure data to obtain the disturbed device failure data x i ' j =x ij +NMnoise,x ij This is the equipment failure data before adding noise disturbance; Compare the differences in equipment failure data before and after adding noise disturbance: If |x ij -x i ' j |≤b, the disturbance equipment failure data is maintained within this interval with probability p; if |x ij -x i ' j |>b, the disturbance equipment failure data is maintained within this interval with probability q; where, 5. A method for aggregating equipment fault data according to claim 3, characterized in that: Step S3 includes: S301, the third-party aggregator TPA determines the share of received data and sums the data; S302, the third-party aggregator TPA sends the aggregated data obtained by summing up to the control center CC. If TPA does not receive all the shares, or part of the received data is wrong or tampered with, TPA will not send the aggregated data to the control center CC; S303. After the control center CC receives the aggregated data shared by TPA, it restores the equipment fault data according to the Shamir threshold secret sharing scheme and uploads it to the blockchain network.

6. A method for aggregating equipment fault data according to claim 1, characterized in that: Step S4 includes: S401. After receiving the query request, the blockchain network obtains the data period and data range to be queried; S402, searching the account book storage data through the index, adding noise to the stored fault data and feeding back: If you query the equipment failure data in a single area, there is no need to perform a summation operation, and the equipment failure data with noise in a region is randomly returned; If the equipment failure data in multiple but not all regions are queried, the equipment failure data in multiple regions are randomly selected, noise is added and then summed; in this case, the noise added to the equipment failure data in each region is mutually accumulated during the summing process and cannot be eliminated, resulting in each failure data being affected by strong noise, thereby changing the aggregation trend of the equipment failure data in the queried region, making the equipment failure data trend in a single region hidden in the aggregation trend of the equipment failure data in the queried region; If the equipment failure data in all areas are queried, the equipment failure data in all areas are noised and then summed up; at this time, since the queried area range is the same as the stored data range, the noise in the equipment failure data in each area cancels each other out after summing, and the equipment failure data trend in all areas can be obtained.

7. A device fault data aggregation system based on the method of claims 1 to 6, characterized in that: include: Initialization module, used to initialize the blockchain network and set privacy parameters; A data acquisition module is used to collect equipment fault data through a number of equipment sensors DS; The data disturbance module is used to add noise to the collected equipment failure data and segment the noisy data; The data aggregation module is used to aggregate and restore the received noisy data and upload it to the blockchain network; The parameter update module is used to update the privacy parameters as needed after each round of data aggregation is completed.

8. The equipment failure data aggregation system according to claim 7, characterized in that: The blockchain network is provided with a smart contract, and the smart contract is provided with a storage module, a DP module and a query module; the storage module is used to store equipment fault data, the DP module is used to add DP noise to the equipment fault data, and the query module calls the DP module according to the query request to add noise and sum the equipment fault data in the storage module and feed back the query result.

9. The equipment failure data aggregation system according to claim 7, characterized in that: The data disturbance module adds disturbance noise to the equipment fault data through the DP disturbance algorithm, and divides the disturbed equipment fault data into several parts according to the Shamir threshold secret sharing scheme and then shares them with the data aggregation module.

10. The equipment failure data aggregation system according to claim 9, characterized in that: The data aggregation module includes a third-party aggregator TPA and a control center CC; The third-party aggregator TPA is used to sum up the received shared data, and after receiving all the shared data, send the summed aggregated data to the control center CC; the control center CC restores the received aggregated data into equipment fault data according to the Shamir threshold secret sharing scheme and uploads it to the blockchain network.

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