Water supply equipment maintenance data evaluation system and method based on big data analysis

Abstract

The invention relates to the technical field of data analysis, in particular to a water supply equipment maintenance data evaluation system and method based on big data analysis, and the method comprises the steps: S1, obtaining water supply equipment maintenance data which comprises water supply equipment information, equipment operation information and equipment fault information; s2, according to the water supply equipment information and the equipment operation information, evaluating the equipment association degree between different water supply equipment; s3, according to the equipment fault information, the fault correlation degree between different water supply equipment is evaluated; and S4, water supply equipment maintenance priority ranking is carried out by integrating the equipment association degree and the fault association degree between different water supply equipment, and water supply equipment maintenance is carried out according to a priority ranking result. According to the method, the equipment association degree and the fault association degree of the water supply equipment are quantified, so that the distribution efficiency of the equipment maintenance resources is effectively improved.

Description

Technical Field

[0001] This application relates to the technical field of data analysis, and particularly to a water supply equipment maintenance data evaluation system and method based on big data analysis. Background Art

[0002] With the rapid development of urbanization, the water consumption of urban residents and industrial water consumption have increased significantly, directly leading to an increasing water supply pressure on water service enterprises. To meet the demand for ultra-large water consumption, each water service enterprise is developing towards large-scale, systematic, and automated directions. The systematization and automation have made the connection between water supply equipment closer, and the entire water supply system has become more complex, ultimately resulting in a significant increase in the difficulty of maintaining and overhauling the water supply system.

[0003] As an important reference for the normal operation of the water supply system, the water supply equipment maintenance data not only includes basic information such as the operating status, fault records, and maintenance history of the equipment, but also reflects the operating associations, location relationships, and the effectiveness of maintenance strategies between the equipment. In the context of the gradual complexity of the water supply system, traditional maintenance methods are difficult to comprehensively and accurately identify the potential associations and risk propagation paths between equipment, resulting in unreasonable allocation of maintenance resources. Therefore, how to make full use of the water supply equipment maintenance data combined with big data analysis technology to evaluate the relevance and fault risks between water supply equipment, and provide a scientific basis for the systematic maintenance of water supply equipment, has become an important issue to be solved urgently. Summary of the Invention

[0004] In order to overcome the defects and deficiencies existing in the prior art, this application provides a water supply equipment maintenance data evaluation system and method based on big data analysis, which effectively improves the scientificity and rationality of equipment maintenance resource allocation by quantifying the equipment association degree and fault association degree of water supply equipment.

[0005] To achieve the above object, this application adopts the following technical solutions:

[0006] In the first aspect, this application provides a water supply equipment maintenance data evaluation method based on big data analysis, including the following steps:

[0007] S1: Obtain water supply equipment maintenance data, which includes water supply equipment information, equipment operation information, and equipment fault information;

[0008] S2: Evaluate the equipment association degree between different water supply equipment according to the water supply equipment information and equipment operation information;

[0009] S3: Evaluate the fault association degree between different water supply equipment according to the equipment fault information;

[0010] S4: Sort the maintenance priorities of water supply equipment based on the equipment correlation degree and failure correlation degree among different water supply equipment, and perform the maintenance of water supply equipment according to the sorting results of the priorities.

[0011] Preferably, the step S2 includes the following specific steps:

[0012] S21: Obtain the water supply equipment information and equipment operation information in the water supply equipment maintenance data;

[0013] S22: Calculate the equipment operation time series correlation degree according to the equipment operation information;

[0014] S23: Calculate the water supply equipment correlation index according to the equipment operation time series correlation degree and in combination with the water supply equipment information. The water supply equipment correlation index is used to evaluate the equipment correlation degree among different water supply equipment. The calculation formula of the water supply equipment correlation index is:

[0015] CI(i,j) = ω T ×S T [i,j] + ω P ×S P [i,j] + ω L ×D L (i,j) + ω R ×D R (i,j);

[0016] In the formula, S T [i,j] represents the equipment type indicator function. When the equipment types of water supply equipment i and water supply equipment j are the same, S T [i,j] = 1, otherwise S T [i,j] = 0. S P [i,j] represents the equipment supplier indicator function of water supply equipment i and water supply equipment j. When the equipment suppliers of water supply equipment i and water supply equipment j are the same, S P [i,j] = 1, otherwise S P [i,j] = 0. D L (i,j) represents the equipment location correlation degree of water supply equipment i and water supply equipment j. D R (i,j) represents the equipment operation time series correlation degree of water supply equipment i and water supply equipment j. ω T represents the equipment type weight. ω P represents the equipment supplier weight. ω L represents the equipment location weight. ω R represents the equipment operation time series weight. CI(i,j) represents the water supply equipment correlation index of water supply equipment i and water supply equipment j.

[0017] Preferably, the calculation formula of the equipment operation time series correlation degree in the step S22 is:

[0018]

[0019] Wherein, T(i) represents the set of equipment operation times of water supply equipment i, T(j) represents the set of equipment operation times of water supply equipment j, card[T(i)∩T(j)] represents the number of elements in the intersection of the set of equipment operation times T(i) and the set of equipment operation times T(j), that is, the total time length during which water supply equipment i and water supply equipment j operate simultaneously, card[T(i)∪T(j)] represents the number of elements in the union of the set of equipment operation times T(i) and the set of equipment operation times T(j), that is, the total time length during which at least one of water supply equipment i and water supply equipment j is operating, and D R (i,j) represents the equipment operation time sequence correlation degree between water supply equipment i and water supply equipment j.

[0020] Preferably, the calculation formula for the equipment position correlation degree in step S23 is:

[0021]

[0022] Wherein, D(i,j) represents the Euclidean distance between water supply equipment i and water supply equipment j, D max represents the maximum Euclidean distance between water supply equipment, and D L (i,j) represents the equipment position correlation degree between water supply equipment i and water supply equipment j.

[0023] Preferably, step S3 includes the following specific steps:

[0024] S31: Obtain the equipment failure information in the water supply equipment maintenance data;

[0025] S32: Calculate the failure time correlation degree of different water supply equipment according to the equipment failure information;

[0026] S33: Extract the failure interval sequence in the equipment failure information and calculate the equipment failure correlation index in combination with the failure time correlation degree. The equipment failure correlation index is used to evaluate the failure correlation degree between different water supply equipment. The calculation formula for the equipment failure correlation index is:

[0027]

[0028] Wherein, ΔT(i) represents the failure interval sequence of water supply equipment i, ΔT(j) represents the failure interval sequence of water supply equipment j, DTW[ΔT(i),ΔT(j)] represents the dynamic time warping distance between the failure interval sequence ΔT(i) and the failure interval sequence ΔT(j), and DTW max represents the maximum value of the dynamic time warping distance between all failure interval sequences, and D F(i, j) represents the failure time correlation degree between water supply device i and water supply device j, ω D represents the failure cycle weight, ω F represents the failure time weight, and FI(i, j) represents the equipment failure correlation index between water supply device i and water supply device j.

[0029] Preferably, the calculation formula for the failure time correlation degree in step S32 is:

[0030]

[0031] In the formula, T(i, k) represents the k-th equipment failure date of water supply device i, T(j, s) represents the s-th equipment failure date of water supply device j, K represents the number of failures of water supply device i, S represents the number of failures of water supply device j, T represents the water supply equipment maintenance cycle, D F (i, j) represents the failure time correlation degree between water supply device i and water supply device j.

[0032] Preferably, step S4 includes the following specific steps:

[0033] [[ID=2A]]S41: Obtain the water supply equipment correlation index and the equipment failure correlation index;

[0034] S42: Calculate the water supply equipment maintenance index by synthesizing the water supply equipment correlation index and the equipment failure correlation index. The water supply equipment maintenance index is used to evaluate the water supply equipment maintenance priority. The calculation formula for the water supply equipment maintenance index is:

[0035]

[0036] In the formula, CI(i, j) represents the water supply equipment correlation index between water supply device i and water supply device j, FI(i, j) represents the equipment failure correlation index between water supply device i and water supply device j, N represents the number of water supply devices, ω CI represents the water supply equipment correlation weight, ω FI represents the equipment failure correlation weight, and MI(i) represents the water supply equipment maintenance index of water supply device i;

[0037] S43: Sort the water supply equipment in descending order according to the water supply equipment maintenance index, obtain the water supply equipment maintenance priority list, and perform water supply equipment maintenance according to the water supply equipment maintenance priority list.

[0038] Note: There was a minor correction in the translation of step S41 where "S41: Get the water supply equipment correlation index and the equipment failure correlation index;" was adjusted to "S41: Obtain the water supply equipment correlation index and the equipment failure correlation index;" for better English expression. Also, the "2A" in the step number was a mistake in the original text and has been corrected to "21" in the translation for consistency.It should be noted here that the value-taking methods of the equipment type weight, equipment supplier weight, equipment location weight, equipment operation time sequence weight, failure cycle weight, failure time weight, water supply equipment association weight, and equipment failure association weight are as follows: Collect 5,000 groups of water supply equipment maintenance data, distinguish whether the maintenance priority of the water supply equipment meets the maintenance requirements, substitute the water supply equipment maintenance data into the water supply equipment maintenance index calculation formula for calculation, and import the calculated water supply equipment maintenance index and the distinction result into the fitting software at the same time to output the optimal equipment type weight, equipment supplier weight, equipment location weight, equipment operation time sequence weight, failure cycle weight, failure time weight, water supply equipment association weight, and equipment failure association weight that meet the distinction accuracy rate of the distinction result.

[0039] In a second aspect, the present application provides a water supply equipment maintenance data evaluation system based on big data analysis, including:

[0040] A data acquisition module for acquiring water supply equipment maintenance data, where the water supply equipment maintenance data includes water supply equipment information, equipment operation information, and equipment failure information;

[0041] A first evaluation module for evaluating the equipment association degree between different water supply equipment according to the water supply equipment information and the equipment operation information;

[0042] A second evaluation module for evaluating the failure association degree between different water supply equipment according to the equipment failure information;

[0043] A priority sorting module for comprehensively sorting the maintenance priorities of water supply equipment according to the equipment association degree and the failure association degree between different water supply equipment and performing water supply equipment maintenance according to the priority sorting result;

[0044] A control module for controlling the operation of the data acquisition module, the first evaluation module, the second evaluation module, and the priority sorting module.

[0045] In a third aspect, the present application provides an electronic device, including: a processor and a memory, wherein a computer program callable by the processor is stored in the memory, and the processor executes the water supply equipment maintenance data evaluation method based on big data analysis by calling the computer program stored in the memory.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which when run on a computer, cause the computer to execute the water supply equipment maintenance data evaluation method based on big data analysis.

[0047] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0048] This application first quantifies the degree of equipment association by evaluating the equipment operation timing correlation and location proximity between different water supply devices, then quantifies the fault association degree by evaluating the fault time synchronization and periodicity between different water supply devices, and finally ranks the maintenance priorities of water supply devices by integrating the equipment association degree and the fault association degree, effectively improving the scientificity and rationality of equipment maintenance resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments read with reference to the accompanying drawings:

[0050] Figure 1 is a schematic diagram of the overall process of the water supply equipment maintenance data evaluation method based on big data analysis provided by an embodiment of the present application;

[0051] Figure 2 is a schematic diagram of the structure of the water supply equipment maintenance data evaluation system based on big data analysis provided by an embodiment of the present application;

[0052] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The technical solution of the present application will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present application and the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0054] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the overall process of the water supply equipment maintenance data evaluation method based on big data analysis provided by an embodiment of the present application, and specifically includes the following steps:

[0055] S1: Obtain water supply equipment maintenance data, which includes water supply equipment information, equipment operation information, and equipment fault information.

[0056] S2: Evaluate the degree of equipment association between different water supply devices according to the water supply equipment information and the equipment operation information;

[0057] Evaluating the degree of equipment association between different water supply devices includes the following specific steps:

[0058] S21: Obtain the water supply equipment information and the equipment operation information in the water supply equipment maintenance data;

[0059] S22: Calculate the equipment operation timing correlation degree according to the equipment operation information;

[0060] S23: Calculate the water supply equipment correlation index based on the correlation degree of equipment operation time series and in combination with water supply equipment information. The water supply equipment correlation index is used to evaluate the equipment correlation degree between different water supply equipment. The calculation formula of the water supply equipment correlation index is as follows:

[0061] CI(i,j) = ω T ×S T [i,j] + ω P ×S P [i,j] + ω L ×D L (i,j) + ω R ×D R (i,j);

[0062] In the formula, S T [i,j] represents the equipment type indication function. When the equipment types of water supply equipment i and water supply equipment j are the same, S T [i,j] = 1. When the equipment types of water supply equipment i and water supply equipment j are different, S T [i,j] = 0. S P [i,j] represents the equipment supplier indication function of water supply equipment i and water supply equipment j. The equipment supplier indication function evaluates the source consistency of equipment based on the supplier. Equipment with higher source consistency usually has similar technical characteristics or potential defects. When the equipment suppliers of water supply equipment i and water supply equipment j are the same, S P [i,j] = 1. When the equipment suppliers of water supply equipment i and water supply equipment j are different, S P [i,j] = 0. D L (i,j) represents the equipment location correlation degree of water supply equipment i and water supply equipment j. D R (i,j) represents the equipment operation time series correlation degree of water supply equipment i and water supply equipment j. ω T represents the equipment type weight. ω P represents the equipment supplier weight. ω L represents the equipment location weight. ω R represents the equipment operation time series weight. CI(i,j) represents the water supply equipment correlation index of water supply equipment i and water supply equipment j;

[0063] The equipment operation time series correlation degree of different water supply equipment reflects the coordination and dependence of equipment in operation time. For example, if two devices operate in coordination most of the time, it indicates that they have a close functional connection in the water supply system, and their operation status and faults may affect each other. By evaluating the operation time series correlation degree, the actual correlation between devices can be more comprehensively identified. The calculation formula of the equipment operation time series correlation degree is as follows:

[0064]

[0065] Where T(i) represents the set of device operation times of water supply device i, T(j) represents the set of device operation times of water supply device j, card[T(i)∩T(j)] represents the number of intersection elements of the set of device operation times T(i) and the set of device operation times T(j), that is, the total time length when water supply device i and water supply device j operate simultaneously, card[T(i)∪T(j)] represents the number of union elements of the set of device operation times T(i) and the set of device operation times T(j), that is, the total time length when at least one of water supply device i and water supply device j is operating, D R (i,j) represents the device operation time sequence correlation degree between water supply device i and water supply device j. When D R (i,j) = 1, it indicates that the operation time periods of water supply device i and water supply device j completely overlap. When D R (i,j) = 0, it indicates that there is no overlap in the operation time periods of water supply device i and water supply device j, indicating that the devices have no time sequence correlation degree. When 0 < D R (i,j) < 1, it indicates that the operation time periods of water supply device i and water supply device j partially overlap, and the time sequence correlation degree depends on the degree of overlap;

[0066] The device location correlation degree of different devices reflects the proximity of the devices in space. Devices that are close in location are more likely to be affected by the same environmental factors (such as temperature, humidity, corrosive substances) or physical conditions (such as pipeline pressure changes, mechanical interference) together, resulting in similar operating states or failure modes. The calculation formula for the device location correlation degree is:

[0067]

[0068] Where D(i,j) represents the Euclidean distance between water supply device i and water supply device j, D max represents the maximum Euclidean distance between water supply devices, that is, the maximum value of the Euclidean distances between all water supply devices, D L (i,j) represents the device location correlation degree between water supply device i and water supply device j.

[0069] S3: Evaluate the degree of fault correlation between different water supply devices according to the device fault information;

[0070] When evaluating the correlation degree of equipment failure time, the failure interval sequence is a set of data describing the time intervals between equipment failures, reflecting the regularity and periodicity of equipment failures. By comparing the failure interval sequences of different water supply equipment through the Dynamic Time Warping (DTW) algorithm, the similarity of their periodic patterns can be quantified. The DTW algorithm can align the failure interval sequences of different time series and is more suitable for dealing with equipment failure situations with periodic offsets. The specific steps for evaluating the failure correlation degree between different water supply equipment are as follows:

[0071] S31: Obtain the equipment failure information in the water supply equipment maintenance data;

[0072] S32: Calculate the failure time correlation degree of different water supply equipment according to the equipment failure information;

[0073] S33: Extract the failure interval sequence from the equipment failure information and calculate the equipment failure correlation index in combination with the failure time correlation degree. The equipment failure correlation index is used to evaluate the failure correlation degree between different water supply equipment. The calculation formula of the equipment failure correlation index is:

[0074]

[0075] In the formula, ΔT(i) represents the failure interval sequence of water supply equipment i. The failure interval sequence forms a sequence of time intervals by calculating the time difference between two consecutive failures. For example, ΔT(i) = {t2 - t1, t3 - t2, t4 - t3,..., t K -t K-1}, where t1 represents the first equipment failure date of water supply equipment i, t2 represents the second equipment failure date of water supply equipment i, t3 represents the third equipment failure date of water supply equipment i, t4 represents the fourth equipment failure date of water supply equipment i, K represents the number of failures of water supply equipment i, ΔT(j) represents the failure interval sequence of water supply equipment j, DTW[ΔT(i), ΔT(j)] represents the dynamic time warping distance between the failure interval sequence ΔT(i) and the failure interval sequence ΔT(j), DTW max represents the maximum value of the dynamic time warping distance between all failure interval sequences, D F (i, j) represents the failure time correlation degree of water supply equipment i and water supply equipment j, ω D represents the failure cycle weight, ω F represents the failure time weight, and FI(i, j) represents the equipment failure correlation index of water supply equipment i and water supply equipment j;

[0076] The proximity of equipment failure times reflects the temporal correlation of different equipment when failures occur. For example, simultaneous failures may indicate that the equipment is affected by the same external conditions or internal coupling, while a short failure time interval may suggest a causal relationship, i.e., the failure of one equipment may induce problems in another equipment. By evaluating the proximity of failure times, potential correlations between equipment can be identified. The calculation formula for the failure time correlation degree is as follows:

[0077]

[0078] In the formula, T(i,k) represents the k-th equipment failure date of water supply equipment i, and T(j,s) represents the s-th equipment failure date of water supply equipment j. represents the time difference between the k-th failure of water supply equipment i and the most recent failure of water supply equipment j. K represents the number of failures of water supply equipment i, S represents the number of failures of water supply equipment j, T represents the maintenance cycle of water supply equipment, and D F (i,j) represents the failure time correlation degree between water supply equipment i and water supply equipment j. The failure time correlation degree is used to evaluate the proximity of equipment failure times.

[0079] S4: Sort the maintenance priorities of water supply equipment based on the equipment correlation degree and failure correlation degree between different water supply equipment, and perform water supply equipment maintenance according to the priority sorting results;

[0080] Sorting the maintenance priorities by comprehensively considering the equipment correlation degree and failure correlation degree of water supply equipment can help identify the key equipment or equipment groups that have the greatest impact on the overall operation in the water supply system, and prioritize the solution of equipment problems with high potential risks and possible chain failures. Through the priority-based maintenance strategy, maintenance resources can be allocated more efficiently, and the operation reliability and safety of the water supply system can be improved. Sorting the maintenance priorities of water supply equipment and performing water supply equipment maintenance according to the priority sorting results include the following specific steps:

[0081] S41: Obtain the water supply equipment correlation index and equipment failure correlation index;

[0082] S42: Calculate the water supply equipment maintenance index by comprehensively considering the water supply equipment correlation index and equipment failure correlation index. The water supply equipment maintenance index is used to evaluate the maintenance priority of water supply equipment. The calculation formula for the water supply equipment maintenance index is as follows:

[0083]

[0084] In the formula, CI(i,j) represents the water supply equipment correlation index between water supply equipment i and water supply equipment j. For accumulating the water supply equipment association index of water supply equipment i and all other water supply equipment except itself, FI(i, j) represents the equipment failure association index of water supply equipment i and water supply equipment j. For accumulating the equipment failure association index of water supply equipment i and all other water supply equipment except itself, N represents the number of water supply equipment, ω CI represents the water supply equipment association weight, ω FI represents the equipment failure association weight, and MI(i) represents the water supply equipment maintenance index of water supply equipment i;

[0085] S43: Sort the water supply equipment in descending order according to the water supply equipment maintenance index, obtain the water supply equipment maintenance priority list, and perform water supply equipment maintenance according to the water supply equipment maintenance priority list.

[0086] Please refer to Figure 2 , Figure 2 which is a schematic structural diagram of a water supply equipment maintenance data evaluation system provided by an embodiment of the present application, including:

[0087] A data acquisition module 210, which is used to acquire water supply equipment maintenance data, and the water supply equipment maintenance data includes water supply equipment information, equipment operation information, and equipment failure information;

[0088] A first evaluation module 220, which is used to evaluate the equipment association degree between different water supply equipment according to the water supply equipment information and equipment operation information;

[0089] A second evaluation module 230, which is used to evaluate the failure association degree between different water supply equipment according to the equipment failure information;

[0090] A priority sorting module 240, which is used to comprehensively sort the water supply equipment maintenance priorities according to the equipment association degree and failure association degree between different water supply equipment and perform water supply equipment maintenance according to the priority sorting result;

[0091] A control module 250, which is used to control the operation of the data acquisition module, the first evaluation module, the second evaluation module, and the priority sorting module.

[0092] In an embodiment of the present application, the first evaluation module 220 is used to evaluate the equipment association degree between different water supply equipment according to the water supply equipment information and equipment operation information. Evaluating the equipment association degree between different water supply equipment includes the following specific steps:

[0093] Acquire the water supply equipment information and equipment operation information in the water supply equipment maintenance data;

[0094] Calculate the equipment operation time sequence association degree according to the equipment operation information;

[0095] Calculate the water supply equipment correlation index based on the correlation degree of equipment operation timings and combined with water supply equipment information. The water supply equipment correlation index is used to evaluate the degree of association between different water supply equipment. The calculation formula for the water supply equipment correlation index is as follows:

[0096] CI(i,j) = ω T ×S T [i,j] + ω P ×S P [i,j] + ω L ×D L (i,j) + ω R ×D R (i,j);

[0097] In the formula, S T [i,j] represents the equipment type indication function. When the equipment types of water supply equipment i and water supply equipment j are the same, S T [i,j] = 1; otherwise, S T [i,j] = 0. S P [i,j] represents the equipment supplier indication function of water supply equipment i and water supply equipment j. When the equipment suppliers of water supply equipment i and water supply equipment j are the same, S P [i,j] = 1; otherwise, S P [i,j] = 0. D L (i,j) represents the equipment location correlation degree of water supply equipment i and water supply equipment j. D R (i,j) represents the equipment operation timing correlation degree of water supply equipment i and water supply equipment j. ω T represents the equipment type weight. ω P represents the equipment supplier weight. ω L represents the equipment location weight. ω R represents the equipment operation timing weight. CI(i,j) represents the water supply equipment correlation index of water supply equipment i and water supply equipment j;

[0098] The calculation formula for the equipment operation timing correlation degree is as follows:

[0099]

[0100] In the formula, T(i) represents the equipment operation time set of water supply equipment i, T(j) represents the equipment operation time set of water supply equipment j, card[T(i) ∩ T(j)] represents the number of elements in the intersection of the equipment operation time sets T(i) and T(j), that is, the total length of time when water supply equipment i and water supply equipment j operate simultaneously. card[T(i) ∪ T(j)] represents the number of elements in the union of the equipment operation time sets T(i) and T(j), that is, the total length of time when at least one of water supply equipment i and water supply equipment j is operating. DR (i, j) represents the equipment operation timing correlation degree between water supply equipment i and water supply equipment j;

[0101] The calculation formula for the equipment location correlation degree is:

[0102]

[0103] In the formula, D(i, j) represents the Euclidean distance between water supply equipment i and water supply equipment j, D max represents the maximum Euclidean distance between water supply equipment, D L (i, j) represents the equipment location correlation degree between water supply equipment i and water supply equipment j.

[0104] In the embodiment of the present application, the second evaluation module 230 is used to evaluate the fault correlation degree between different water supply equipment according to the equipment fault information. The specific steps for evaluating the fault correlation degree between different water supply equipment include:

[0105] Obtain the equipment fault information in the water supply equipment maintenance data;

[0106] Calculate the fault time correlation degree of different water supply equipment according to the equipment fault information;

[0107] Extract the fault interval sequence in the equipment fault information and calculate the equipment fault correlation index in combination with the fault time correlation degree. The equipment fault correlation index is used to evaluate the fault correlation degree between different water supply equipment. The calculation formula for the equipment fault correlation index is:

[0108]

[0109] In the formula, ΔT(i) represents the fault interval sequence of water supply equipment i, ΔT(j) represents the fault interval sequence of water supply equipment j, DTW[ΔT(i), ΔT(j)] represents the dynamic time warping distance between the fault interval sequence ΔT(i) and the fault interval sequence ΔT(j), DTW max represents the maximum value of the dynamic time warping distance between all fault interval sequences, D F (i, j) represents the fault time correlation degree between water supply equipment i and water supply equipment j, ω D represents the fault cycle weight, ω F represents the fault time weight, and FI(i, j) represents the equipment fault correlation index between water supply equipment i and water supply equipment j;

[0110] The calculation formula for the fault time correlation degree is:

[0111]

[0112] Where T(i,k) represents the k-th equipment failure date of water supply equipment i, T(j,s) represents the s-th equipment failure date of water supply equipment j, K represents the number of failures of water supply equipment i, S represents the number of failures of water supply equipment j, T represents the maintenance period of water supply equipment, and D F (i,j) represents the failure time correlation degree between water supply equipment i and water supply equipment j.

[0113] In the embodiment of the present application, the priority sorting module 240 is used to comprehensively sort the maintenance priorities of water supply equipment according to the equipment correlation degree and failure correlation degree between different water supply equipment, and perform water supply equipment maintenance according to the priority sorting result. The steps of sorting the maintenance priorities of water supply equipment and performing water supply equipment maintenance according to the priority sorting result include the following specific steps:

[0114] Obtain the water supply equipment correlation index and the equipment failure correlation index;

[0115] Calculate the water supply equipment maintenance index by comprehensively considering the water supply equipment correlation index and the equipment failure correlation index. The water supply equipment maintenance index is used to evaluate the maintenance priority of water supply equipment. The calculation formula of the water supply equipment maintenance index is:

[0116]

[0117] Where CI(i,j) represents the water supply equipment correlation index between water supply equipment i and water supply equipment j, FI(i,j) represents the equipment failure correlation index between water supply equipment i and water supply equipment j, N represents the number of water supply equipment, and ω CI represents the water supply equipment correlation weight, and ω FI represents the equipment failure correlation weight, and MI(i) represents the water supply equipment maintenance index of water supply equipment i;

[0118] Sort the water supply equipment in descending order according to the water supply equipment maintenance index, obtain the water supply equipment maintenance priority list, and perform water supply equipment maintenance according to the water supply equipment maintenance priority list.

[0119] For the parameters and the steps of each unit module in the above-mentioned water supply equipment maintenance data evaluation system based on big data analysis of the present application to implement corresponding functions, reference can be made to the parameters and steps in the embodiments of the water supply equipment maintenance data evaluation method based on big data analysis in the above text, which will not be elaborated here.

[0120] As Figure 3 shown, an embodiment of the present invention further provides an electronic device 300, including a memory 320 for storing a computer program 322; a processor 310 for executing the computer program 322 to implement the water supply equipment maintenance data evaluation method based on big data analysis in any of the above embodiments.

[0121] It should be noted that the Figure 3 is a structural diagram of an electronic device 300 shown according to an exemplary embodiment, and the content in the figure should not be considered as any limitation to the scope of use of the present invention.

[0122] Specifically, the electronic device 300 may specifically include: at least one processor 310, at least one memory 320, a power supply 330, a communication interface 340, an input / output interface 350, and a communication bus 360. Among them, the memory 320 is used to store a computer program 322, and the computer program 322 is loaded and executed by the processor 310 to implement the relevant steps in the water supply equipment maintenance data evaluation method based on big data analysis disclosed in any of the foregoing embodiments. In addition, the electronic device 300 in the embodiments of the present invention may specifically be an electronic computer.

[0123] In the embodiments of the present invention, the power supply 330 is used to provide working voltage for each hardware device on the electronic device 300; the communication interface 340 can create a data transmission channel between the electronic device 300 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of the present invention, and no specific limitation is imposed on it here; the input / output interface 350 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application needs, and no specific limitation is made here.

[0124] In addition, the memory 320, as a carrier for resource storage, can be a read-only memory, a random access memory, a disk, or an optical disc, etc., and the resources stored thereon can include an operating system 321, a computer program 322, etc., and the storage method can be temporary storage or permanent storage.

[0125] Among them, the operating system 321 is used to manage and control each hardware device and computer program on the electronic device 300, and it can be Windows Server, Netware, Unix, Linux, etc. The computer program 322, in addition to including the computer program 322 that can be used to complete the water supply equipment maintenance data evaluation method based on big data analysis executed by the electronic device 300 disclosed in any of the foregoing embodiments, may further include computer programs 322 that can be used to complete other specific tasks.

[0126] The embodiments of the present invention also provide a computer-readable storage medium for storing the computer program 322, and when the computer program 322 is executed by the processor 310, it implements the water supply equipment maintenance data evaluation method based on big data analysis in any of the above embodiments.

[0127] For example, a computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

Claims

1. A method for evaluating water supply equipment maintenance data based on big data analysis, characterized in that, Including the following steps: S1: Obtain the maintenance data of the water supply equipment, where the maintenance data of the water supply equipment includes water supply equipment information, equipment operation information, and equipment failure information; S2: Evaluate the equipment association degree between different water supply equipment according to the water supply equipment information and equipment operation information; S3: Evaluate the failure association degree between different water supply equipment according to the equipment failure information; S4: Sort the maintenance priorities of the water supply equipment by comprehensively considering the equipment association degree and failure association degree between different water supply equipment, and perform the maintenance of the water supply equipment according to the sorting result of the priorities.

2. The method for evaluating water supply equipment maintenance data based on big data analysis according to claim 1, wherein The step S2 includes the following specific steps: S21: Obtain the water supply equipment information and equipment operation information in the maintenance data of the water supply equipment; S22: Calculate the equipment operation time sequence association degree according to the equipment operation information; S23: Calculate the water supply equipment association index according to the equipment operation time sequence association degree and in combination with the water supply equipment information. The water supply equipment association index is used to evaluate the equipment association degree between different water supply equipment. The calculation formula of the water supply equipment association index is: CI(i,j) = ω T ×S T [i,j] + ω P ×S P [i,j] + ω L ×D L (i,j) + ω R ×D R (i,j); Where S T [i, j] represents the device type indication function. When the device types of water supply device i and water supply device j are the same, S T [i, j]=1; otherwise, S T [i, j]=0, S P [i, j] represents the device supplier indication function of water supply device i and water supply device j. When the device suppliers of water supply device i and water supply device j are the same, S P [i, j]=1; otherwise, S P [i, j]=0, D L (i, j) represents the device location correlation degree of water supply device i and water supply device j, D R (i, j) represents the device operation timing correlation degree of water supply device i and water supply device j, ω T represents the device type weight, ω P represents the device supplier weight, ω L represents the device location weight, ω R represents the device operation timing weight, and CI(i, j) represents the water supply device correlation index of water supply device i and water supply device j.

3. The method for evaluating water supply equipment maintenance data based on big data analysis according to claim 2, characterized in that, The calculation formula of the equipment operation time sequence association degree in the step S22 is: Where T(i) represents the set of equipment operation times of water supply equipment i, T(j) represents the set of equipment operation times of water supply equipment j, card[T(i)∩T(j)] represents the number of intersection elements of the set of equipment operation times T(i) and the set of equipment operation times T(j), that is, the total length of time when water supply equipment i and water supply equipment j operate simultaneously, card[T(i)∪T(j)] represents the number of union elements of the set of equipment operation times T(i) and the set of equipment operation times T(j), that is, the total length of time when at least one of water supply equipment i and water supply equipment j is operating, D R (i,j) represents the equipment operation timing correlation degree between water supply equipment i and water supply equipment j.

4. The water supply equipment maintenance data evaluation method based on big data analysis according to claim 2, characterized in that The calculation formula of the equipment location association degree in the step S23 is: where D(i,j) represents the Euclidean distance between water supply device i and water supply device j, D max represents the maximum Euclidean distance between water supply devices, D L (i,j) represents the device location correlation degree between water supply device i and water supply device j.

5. The method for evaluating water supply equipment maintenance data based on big data analysis according to claim 1, wherein The step S3 includes the following specific steps: S31: Obtain the equipment failure information in the maintenance data of the water supply equipment; S32: Calculate the failure time association degree of different water supply equipment according to the equipment failure information; S33: Extract the failure interval sequence in the equipment failure information and calculate the equipment failure association index in combination with the failure time association degree. The equipment failure association index is used to evaluate the failure association degree between different water supply equipment. The calculation formula of the equipment failure association index is: Where ΔT(i) represents the failure interval sequence of water supply equipment i, ΔT(j) represents the failure interval sequence of water supply equipment j, DTW[ΔT(i), ΔT(j)] represents the dynamic time warping distance between the failure interval sequence ΔT(i) and the failure interval sequence ΔT(j), DTW max represents the maximum value of the dynamic time warping distance between all failure interval sequences, D F (i, j) represents the failure time correlation degree of water supply equipment i and water supply equipment j, ω D represents the failure cycle weight, ω F represents the failure time weight, and FI(i, j) represents the equipment failure correlation index of water supply equipment i and water supply equipment j.

6. The method for evaluating water supply equipment maintenance data based on big data analysis according to claim 5, characterized in that The calculation formula of the failure time association degree in the step S32 is: Where T(i,k) represents the k-th equipment failure date of water supply equipment i, T(j,s) represents the s-th equipment failure date of water supply equipment j, K represents the number of failures of water supply equipment i, S represents the number of failures of water supply equipment j, T represents the water supply equipment maintenance cycle, and D F (i,j) represents the failure time correlation degree of water supply equipment i and water supply equipment j.

7. The method for evaluating water supply equipment maintenance data based on big data analysis according to claim 1, characterized in that, The step S4 includes the following specific steps: S41: Obtain the water supply equipment association index and the equipment failure association index; S42: Calculate the water supply equipment maintenance index by comprehensively considering the water supply equipment association index and the equipment failure association index. The water supply equipment maintenance index is used to evaluate the maintenance priority of the water supply equipment. The calculation formula of the water supply equipment maintenance index is: where CI(i,j) represents the water supply equipment association index of water supply equipment i and water supply equipment j, FI(i,j) represents the equipment failure association index of water supply equipment i and water supply equipment j, N represents the number of water supply equipment, ω CI represents the water supply equipment association weight, ω FI represents the equipment failure association weight, and MI(i) represents the water supply equipment maintenance index of water supply equipment i; S43: Sort the water supply equipment in descending order according to the water supply equipment maintenance index, obtain the water supply equipment maintenance priority list, and perform the maintenance of the water supply equipment according to the water supply equipment maintenance priority list.

8. A water supply equipment maintenance data evaluation system based on big data analysis, which is applied to the water supply equipment maintenance data evaluation method based on big data analysis according to any one of claims 1-7, characterized in that, The system includes: A data acquisition module, which is used to obtain the maintenance data of the water supply equipment, where the maintenance data of the water supply equipment includes water supply equipment information, equipment operation information, and equipment failure information; A first evaluation module, which is used to evaluate the equipment association degree between different water supply equipment according to the water supply equipment information and equipment operation information; A second evaluation module, which is used to evaluate the failure association degree between different water supply equipment according to the equipment failure information; A priority sorting module, which is used to sort the maintenance priorities of the water supply equipment by comprehensively considering the equipment association degree and failure association degree between different water supply equipment, and perform the maintenance of the water supply equipment according to the sorting result of the priorities; A control module, which is used to control the operation of the data acquisition module, the first evaluation module, the second evaluation module, and the priority sorting module.

9. An electronic device, comprising: A processor and a memory, wherein a computer program that can be called by the processor is stored in the memory; characterized in that the processor executes the method for evaluating maintenance data of a water supply device based on big data analysis according to any one of claims 1-7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, Stored with instructions that, when the instructions run on a computer, cause the computer to execute the method for evaluating maintenance data of a water supply device based on big data analysis according to any one of claims 1-7.

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