Gas safety operation evaluation method, device, system and electronic equipment

Abstract

The application discloses a kind of gas safety operation evaluation method, device, system and electronic equipment, it is related to gas safety technical field.The method comprises: obtaining the index weight of each index in the system data of gas system and the pre-established gas safety operation evaluation network model, wherein, gas system includes gas pipe network and the distance between gas pipe network and underground space is less than pre-set distance threshold, underground space includes at least one of gas well, drainage well, power well, weak electric well;According to system data, the index value of each index in gas safety operation evaluation network model is obtained;According to index weight and index value, the safety operation evaluation result of gas system is obtained.Thereby, by including monitoring gas pipe network and the underground space adjacent to gas pipe network, overall evaluation is realized, and by pre-established gas safety operation evaluation network model, unified evaluation is realized.

Description

Technical Field

[0001] This invention relates to the field of gas safety technology, and in particular to a gas safety operation evaluation method, device, system and electronic equipment. Background Technology

[0002] As a core component of the city's lifeline, the safe operation of urban gas pipeline networks is directly related to public safety and the protection of people's livelihoods. Environmental changes and facility conditions in adjacent underground spaces (water supply and drainage pipelines, power pipelines, low-voltage power pipelines, underground ditches, structures, etc.) can directly or indirectly affect the operational stability of gas pipeline networks, potentially inducing safety hazards such as pipeline corrosion, leaks, and methane accumulation.

[0003] In related technologies, the evaluation of gas safety is carried out by monitoring the concentration of combustible gas in gas pipelines and their adjacent underground spaces based on front-end sensing equipment, and risk warning is carried out through monitoring, operation and maintenance and data analysis. However, this approach lacks comprehensiveness and uniformity. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the first objective of this invention is to propose a method for evaluating the safe operation of gas systems, in order to achieve a comprehensive and unified evaluation.

[0005] The second objective of this invention is to provide an electronic device.

[0006] The third objective of this invention is to provide a gas safety operation evaluation device.

[0007] The fourth objective of this invention is to propose a gas safety operation evaluation system.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for evaluating the safe operation of a gas system. The method includes: acquiring system data of a gas system and the weights of various indicators in a pre-established gas safety operation evaluation network model; wherein the gas system includes a gas pipeline network and underground spaces located at a distance less than a preset distance threshold from the gas pipeline network; the underground spaces include at least one of gas wells, drainage wells, power wells, and low-voltage electrical wells; obtaining the indicator values ​​of each indicator in the gas safety operation evaluation network model based on the system data; and obtaining the safety operation evaluation result of the gas system based on the indicator weights and the indicator values.

[0009] In addition, the gas safety operation evaluation method according to embodiments of the present invention may also have the following additional technical features: According to an embodiment of the present invention, the gas safety operation evaluation network model includes a target layer, a control layer, and a network layer. The control layer elements of the control layer include pipeline risk, monitoring and maintenance, and operational status. The indicators are network layer elements of the network layer, and there are multiple indicators, each of which corresponds to one control layer element.

[0010] According to one embodiment of the present invention, obtaining the index weights of each index in the gas safety operation evaluation network model includes: obtaining the first importance of each control layer element to the target layer element of the target layer; obtaining the second importance of each index to the control layer element corresponding to the index; and obtaining the influence strength between any two indices corresponding to different control layer elements; obtaining the first relative importance between any two target layer elements based on the first importance; obtaining the second relative importance between any two indices corresponding to the same control layer element based on the second importance; and obtaining the index weights according to the first relative importance, the second relative importance, and the influence strength.

[0011] According to one embodiment of the present invention, obtaining the index weight based on the first relative importance, the second relative importance, and the influence intensity includes: obtaining the weight of the control layer element based on the first relative importance, and obtaining a supermatrix based on the second relative importance and the influence intensity, wherein the supermatrix includes the influence intensity of the first index on the second index, and the first index and the second index are both arbitrary indices in the network layer; and obtaining the index weight based on the weight and the supermatrix.

[0012] According to one embodiment of the present invention, obtaining the index weight based on the weight and the hypermatrix includes: obtaining a weight vector based on the weight; multiplying the weight vector by the hypermatrix to obtain a weighted hypermatrix; performing self-multiplication on the weighted hypermatrix to obtain a limiting hypermatrix; and using the elements of the limiting hypermatrix as the index weight.

[0013] According to one embodiment of the present invention, the indicators corresponding to the pipeline network risk include the gas pipeline leakage event rate, gas valve leakage event rate, high-risk gas leakage event rate, biogas accumulation ratio of gas wells, biogas alarm ratio of drainage wells, biogas alarm ratio of power wells, and biogas alarm ratio of weak current wells. The indicators corresponding to the monitoring and maintenance include the equipment online rate, equipment inspection completion rate, equipment maintenance rate, and data quality qualification rate. The indicators corresponding to the operational status include alarm spatial hotspot distribution characteristics, alarm classification time distribution characteristics, and environmental impact correlation.

[0014] According to an embodiment of the present invention, before obtaining the safety operation evaluation result of the gas system based on the indicator weights and the indicator values, the method further includes: normalizing the indicator values; obtaining the safety operation evaluation result of the gas system based on the indicator weights and the indicator values ​​includes: performing weighted summation on the normalized indicator values ​​using the indicator weights, and obtaining a comprehensive safety score based on the weighted summation result; comparing the comprehensive safety score with multiple scoring thresholds, and obtaining the safety operation evaluation result based on the comparison result.

[0015] To achieve the above objectives, a second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the computer program is executed by the processor, it implements the above-described gas safety operation evaluation method.

[0016] To achieve the above objectives, a third aspect of the present invention provides a gas safety operation evaluation device, comprising: an acquisition module, configured to acquire system data of a gas system and the index weights of each index in a pre-established gas safety operation evaluation network model, and to obtain the index values ​​of each index in the gas safety operation evaluation network model based on the system data, wherein the gas system includes a gas pipeline network and an underground space at a distance less than a preset distance threshold from the gas pipeline network, and the underground space includes at least one of a gas well, a drainage well, an electrical well, and a low-voltage electrical well; and an evaluation module, configured to obtain the safety operation evaluation result of the gas system based on the index weights and the index values.

[0017] To achieve the above objectives, a fourth aspect of the present invention provides a gas safety operation evaluation system, including the gas safety operation evaluation device described above.

[0018] According to embodiments of the present invention, a gas safety operation evaluation method, apparatus, system, and electronic equipment acquire system data of a gas system and the index weights of various indicators in a pre-established gas safety operation evaluation network model. The gas system includes a gas pipeline network and underground spaces located at a distance less than a preset distance threshold from the gas pipeline network. The underground spaces include at least one of gas wells, drainage wells, power wells, and low-voltage electrical wells. The indicator values ​​of each indicator in the gas safety operation evaluation network model are obtained based on the system data. The safety operation evaluation result of the gas system is obtained based on the index weights and index values. Therefore, by including both the gas pipeline network and the underground spaces adjacent to the gas pipeline network in the monitoring, a comprehensive evaluation is achieved, and a unified evaluation is achieved through the pre-established gas safety operation evaluation network model.

[0019] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] Figure 1 This is a flowchart of the gas safety operation evaluation method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the control layer according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a network layer according to an embodiment of the present invention; Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention; Figure 5 This is a structural block diagram of the gas safety operation evaluation device according to an embodiment of the present invention; Figure 6 This is a structural block diagram of the gas safety operation evaluation system according to an embodiment of the present invention. Detailed Implementation

[0021] The following description, with reference to the accompanying drawings, outlines a gas safety operation evaluation method, apparatus, system, and electronic equipment according to embodiments of the present invention. Throughout the description, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. The embodiments described with reference to the accompanying drawings are exemplary and should not be construed as limiting the present invention.

[0022] Figure 1 This is a flowchart of the gas safety operation evaluation method according to an embodiment of the present invention.

[0023] like Figure 1 As shown, the gas safety operation evaluation method includes: S11, obtain system data of the gas system and the indicator weights of each indicator in the pre-established gas safety operation evaluation network model.

[0024] The gas system includes a gas pipeline network and an underground space located at a distance less than a preset distance threshold from the gas pipeline network. The underground space includes at least one of a gas well, a drainage well, an electrical well, and a low-voltage electrical well.

[0025] The above-mentioned gas safety operation evaluation network model includes a target layer, a control layer, and a network layer. The control layer elements include pipeline risk, monitoring and maintenance, and operational status. The indicators are the network layer elements of the network layer. There are multiple indicators, and each indicator corresponds to a control layer element.

[0026] The indicators corresponding to pipeline network risks include the incidence rate of gas pipeline leaks, the incidence rate of gas valve leaks, the incidence rate of high-risk gas leaks, the biogas accumulation ratio of gas wells, the biogas alarm ratio of drainage wells, the biogas alarm ratio of power wells, and the biogas alarm ratio of weak current wells. The indicators corresponding to monitoring and maintenance include the equipment online rate, the equipment inspection completion rate, the equipment maintenance rate, and the data quality qualification rate. The indicators corresponding to the operational status include the spatial hotspot distribution characteristics of alarms, the temporal distribution characteristics of alarm classifications, and the correlation with environmental impact.

[0027] Specifically, firstly, a network model for evaluating the safe operation of gas systems is constructed.

[0028] The above-mentioned gas safety operation evaluation network model can be found in Table 1 below.

[0029] Table 1

[0030] For the control layer described above, the various control layer elements have the following relationships: 1. The level of pipeline risk C1 directly determines the intensity of resource investment in monitoring and maintenance C2, while the quality of monitoring and maintenance C2, such as equipment online rate N8 and data quality pass rate N11, in turn ensures the accuracy of pipeline risk C1 indicator data.

[0031] 2. Data such as equipment online rate (N8) and equipment inspection completion rate (N9) generated by monitoring and maintenance C2 are the basic data sources for operational status C3 analysis. The alarm spatial hotspot distribution characteristics (N12) and alarm classification time distribution characteristics (N13) obtained from operational status C3 analysis can guide the optimization and implementation of monitoring and maintenance C2 strategies.

[0032] 3. The pipeline risk level C1 constitutes the core input for the operational status C3 analysis, while the operational status C3 characterizes and reflects the changing level and trend of pipeline risk C1.

[0033] It should be noted that the above-mentioned gas pipeline leakage incident rate N1 and gas valve leakage incident rate N2 are the ratios of leakage incidents per thousand kilometers of gas pipeline network.

[0034] The aforementioned high-risk gas leak incidents, N3, mainly consist of leaks with severe consequences, such as gas pipeline leaks spreading and damage caused by third-party construction.

[0035] The biogas accumulation ratio N4 of gas wells, the biogas alarm ratio N5 of drainage wells, the biogas alarm ratio N6 of power wells, and the biogas alarm ratio N7 of weak current wells represent the proportion of points that generate biogas accumulation alarms among all points of a certain monitoring type.

[0036] The equipment online rate N8 is the proportion of normally online equipment among all monitoring equipment in the area.

[0037] The equipment inspection completion rate N9 is the completion rate of daily on-site inspections, maintenance, and other tasks carried out on the monitoring equipment.

[0038] Equipment maintenance rate N10 refers to the probability of dismantling and returning equipment for repair work when it is offline, faulty, or abnormal.

[0039] The data quality pass rate N11 refers to the probability that the data uploaded by the monitoring equipment is normal and stable, and no abnormalities are found after analysis and diagnosis.

[0040] The above control layer can be found in [reference]. Figure 2 The above network layers can be found in [reference]. Figure 3 .

[0041] The aforementioned urban gas safety operation evaluation T is the target layer element of the target layer.

[0042] After constructing the gas safety operation evaluation network model, the above-mentioned method for obtaining the indicator weights of each indicator in the gas safety operation evaluation network model includes: obtaining the first importance of each control layer element to the target layer element, and for each indicator, obtaining the second importance of the indicator to the control layer element corresponding to the indicator; and for any two indicators corresponding to different control layer elements, obtaining the influence strength between the indicators; obtaining the first relative importance between any two target layer elements based on the first importance, and obtaining the second relative importance between any two indicators corresponding to the same control layer element based on the second importance; and obtaining the indicator weights based on the first relative importance, the second relative importance, and the influence strength.

[0043] The above-mentioned method of obtaining indicator weights based on the first relative importance, the second relative importance, and the influence intensity includes: obtaining the weights of control layer elements based on the first relative importance, and obtaining a supermatrix based on the second relative importance and the influence intensity, wherein the supermatrix includes the influence intensity of the first indicator on the second indicator, and the first indicator and the second indicator are arbitrary indicators in the network layer; and obtaining indicator weights based on the weights and the supermatrix.

[0044] The above-mentioned method of obtaining index weights based on weights and hypermatrix includes: obtaining a weight vector based on the weights; multiplying the weight vector by the hypermatrix to obtain a weighted hypermatrix; performing self-multiplication on the weighted hypermatrix to obtain a limiting hypermatrix; and using the elements of the limiting hypermatrix as index weights.

[0045] Specifically, further, experts can score each parameter in the above-mentioned gas safety operation evaluation network model according to its importance, and construct a scoring matrix.

[0046] The scoring matrix described above can be constructed based on Table 2 below.

[0047] Table 2

[0048] After scoring using Table 2 above to obtain the first importance of each control layer element to the target layer element and the second importance of each indicator to the corresponding control layer element, for any two elements N and M, use... express relatively The relative importance of, satisfying and , .in .

[0049] Wherein, N and M can be any one of C1, C2, C3, N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, N11, N12, N13, and N14.

[0050] Specifically, when conducting expert scoring, we can first determine the primary importance of the three control layer elements to the urban gas safety operation evaluation T, then compare the importance of any two control layer elements to the urban gas safety operation evaluation T to obtain the primary relative importance between any two control layer elements, and then obtain the primary relative importance matrix as shown below based on the primary relative importance. .

[0051] .

[0052] Furthermore, by obtaining the second importance of each indicator corresponding to pipeline risk C1 to pipeline risk C1, the second relative importance matrix shown below is obtained. .

[0053] .

[0054] The second importance of each indicator corresponding to monitoring and maintenance C2 is obtained, and then the third relative importance matrix is ​​shown below. .

[0055] .

[0056] The second importance of each indicator corresponding to operational state C3 is obtained, and then the fourth relative importance matrix is ​​shown below. .

[0057] .

[0058] After obtaining the above influence strength, a judgment matrix is ​​constructed based on the influence strength as shown below. .

[0059] .

[0060] in, Let represent the influence strength of the first indicator corresponding to the j-th control layer element on the first indicator corresponding to the i-th control layer element. Let represent the influence strength of the first indicator corresponding to the j-th control layer element on the second indicator corresponding to the i-th control layer element. The above judgment matrix represents the influence strength of the j-th indicator corresponding to the j-th control layer element on the 1-th indicator corresponding to the i-th control layer element. The meanings of the other elements can be inferred in the same way.

[0061] Since the number of control layer elements is 3, meaning i and j can both be 1, 2, or 3, the above judgment matrix... , can be , , , , , .

[0062] The methods described above for obtaining the intensity of influence can also be used for expert scoring.

[0063] After constructing the first relative importance matrix Second relative importance matrix Third relative importance matrix Fourth relative importance matrix Judgment matrix Then, since there are six judgment matrices, for each matrix A in these ten matrices, the following operation is performed: Normalize each column of matrix A to eliminate scaling differences between columns, ensuring the sum of elements within each column is 1, thus obtaining a normalized matrix. The specific normalization formula is shown below: , in, The element in the d-th row and e-th column of the normalized matrix. Let be the element in the d-th row and e-th column of the matrix. Let n be the element in the b-th row and e-th column of the matrix, where n is the number of rows and columns of the matrix.

[0064] After obtaining the normalized matrix, summing each row of the normalized matrix yields an initial value vector reflecting the overall importance of the elements. The calculation formula is as follows: , in, This initial value vector The element in the d-th row.

[0065] Initial value vector Normalization is performed to make the sum of the weights equal to 1, resulting in the final weight vector. The calculation formula is as follows: , in, For the final weight vector The element in the i-th row.

[0066] Then, the largest eigenvalue is calculated using the following formula. : , in, For matrix With the final weight vector The product of the first Each element.

[0067] Then, using the aforementioned largest eigenvalue Perform a consistency check.

[0068] Specifically, consistency verification can be performed using the following formula: , in, The consistency ratio, As a consistency indicator, , For random consistency indicators, random consistency indicators The value can be determined based on the matrix order. Query Standards The table is obtained.

[0069] When the consistency ratio If so, the matrix passes the consistency test.

[0070] After obtaining the aforementioned second relative importance matrix Third relative importance matrix Fourth relative importance matrix Judgment matrix After the corresponding final weight vector passes the consistency check mentioned above, the following steps are performed: Specifically, a hypermatrix S is constructed, where the number of rows and columns of hypermatrix S is equal to the number of the aforementioned indicators. Each column of hypermatrix S is a combination of weight vectors representing the influence of elements in all element groups on a certain element N, based on the aforementioned second relative importance matrix. Third relative importance matrix Fourth relative importance matrix Judgment matrix The corresponding final weight vector is obtained.

[0071] , in, The weight of the impact of the gas pipeline leak event incidence rate N1 on the gas pipeline leak event incidence rate N1. The weighting of the impact of the gas pipeline leak event rate N1 on the gas valve leak event rate N2. This represents the influence weight of the gas valve leakage event rate N2 on the gas pipeline leakage event rate N1. The meanings of other elements in the hypermatrix S can be deduced similarly.

[0072] It should be noted that the above method of obtaining the hypermatrix S from the final weight vector can be interpreted as using the elements of the final weight vector as the corresponding elements of the hypermatrix S. For example, for... This refers to the weighting of the impact of the gas pipeline leak event incidence rate N1 on the overall gas pipeline leak event incidence rate N1, while the relative importance of the gas pipeline leak event incidence rate N1 on the overall gas pipeline leak event incidence rate N1 belongs to... ,thus, It can be with The corresponding element in the final weight vector that corresponds to the gas pipeline leak event occurrence rate N1; for This refers to the weighting of the gas valve leak event rate N2 on the gas pipeline leak event rate N1, while the relative importance of the gas valve leak event rate N2 on the gas pipeline leak event rate N1 belongs to... ,thus, It can be with The corresponding element in the final weight vector that corresponds to the gas pipeline leak event occurrence rate N1; for This refers to the influence weight of the gas pipeline leak incident incidence rate N1 on the environmental impact correlation N14, while the influence strength of the gas pipeline leak incident incidence rate N1 on the environmental impact correlation N14 belongs to... ,thus, It can be with The corresponding element in the final weight vector that corresponds to the environmental impact correlation N14; similarly, It can be with The element in the final weight vector corresponding to the gas valve leakage event occurrence rate N2, It can be with The element in the final weight vector corresponding to the gas valve leakage event occurrence rate N2, It can be with The element in the final weight vector corresponding to the gas pipeline leak event occurrence rate N1, It can be with The element in the final weight vector corresponding to the gas valve leakage event occurrence rate N2, It can be with The element in the final weight vector corresponding to the environmental impact correlation N14, It can be with The elements in the final weight vector that correspond to the environmental impact correlation degree N14 are analogous to other elements in the hypermatrix S.

[0073] Then, the supermatrix S is weighted using the following formula to obtain the weighted supermatrix. : , in, To and The corresponding final weight vector W.

[0074] Then, the weighted hypermatrix Perform self-multiplication, that is, weighted hypermatrix Self-multiplication Second-rate, Continue this process until the elements in each column of the matrix converge, thus obtaining the limiting hypermatrix. : , Where t is a positive integer that makes the elements in each column of the matrix tend to converge; that is, the value of t will continue to increase until the elements in each column of the matrix tend to converge.

[0075] Limiting Hypermatrix In this context, each column element is the same weight vector. That is, the weight vector Each element in the above network layer represents the final combined weight of the corresponding index.

[0076] in, The final overall weight for the gas pipeline leak incident rate N1 is... The final overall weight is the gas valve leak incident incidence rate N2. This is the final comprehensive weight for the environmental impact correlation N14; other values ​​can be deduced similarly.

[0077] S12, based on system data, obtain the index values ​​of each indicator in the gas safety operation evaluation network model.

[0078] Specifically, for the indicators corresponding to pipeline risk C1, the indicator values ​​are obtained using the following method.

[0079] First, the timing correction coefficients are obtained according to the following formula: , in, For timing correction coefficients, For year-on-year weighting, For month-on-month weighting, For current period data, This is valid data for the same period last year. This data is valid from the previous month. All valid data represents the corresponding number of alarms.

[0080] and The values ​​are adapted to different scenarios as follows: ① Typical scenarios: 6; ② Periods of high temperature, severe cold, and heavy rainfall: 4; ③ The construction and renovation period of the pipeline network is less than or equal to three years: 7.

[0081] Furthermore, timing correction coefficients are also required. Apply threshold constraints. If the value exceeds the range, the boundary value will be used to avoid interference from extreme data.

[0082] After obtaining the time-series correction coefficient, the index value corresponding to the pipeline risk C1 can be obtained based on the time-series correction coefficient. .

[0083] The corresponding index values ​​for the gas pipeline leak incident rate N1, gas valve leak incident rate N2, and high-risk gas leak incident rate N3. The calculation formula is as follows: , Where o represents the number of times the event for this indicator occurred within the evaluation period, and L represents the total length of the gas pipeline network of the evaluation object in kilometers.

[0084] The corresponding index values ​​for the biogas accumulation ratio N4 of gas wells, the biogas alarm ratio N5 of drainage wells, the biogas alarm ratio N6 of power wells, and the biogas alarm ratio N7 of weak current wells. The calculation formula is as follows: , Where q represents the number of times the event for this indicator occurs within the evaluation period. The number of on-site monitoring points corresponding to the manhole types for the evaluation period, namely the number of on-site monitoring points for gas wells, drainage wells, power wells, and low-voltage wells.

[0085] The equipment online rate N8 is the ratio of the number of online monitoring devices to the total number of monitoring devices.

[0086] The equipment inspection completion rate N9 is the ratio of the number of equipment that has been inspected to the number of equipment that is planned to be inspected.

[0087] The equipment maintenance rate N10 is the ratio of the number of equipment that has been reported for repair to the number of faulty equipment.

[0088] The data quality pass rate N11 is the ratio of the number of devices with qualified data in the random monitoring to the total number of devices in the random monitoring.

[0089] In other words, for the indicators corresponding to monitoring and maintenance C2, the data can be directly used as indicator values ​​after obtaining the above data.

[0090] The index value of alarm spatial hotspot distribution characteristic N12 is calculated using the kernel density estimation method, which quantifies the spatial risk level by calculating the cluster density of alarm points per unit area.

[0091] First, alarms are classified based on the concentration values ​​of alarm points within the evaluation area. Alarms with a concentration greater than or equal to 5% VOL are classified as Level 1 alarms, those with a concentration less than 5% VOL but greater than or equal to 3% VOL are classified as Level 2 alarms, and those with a concentration less than 3% VOL but greater than or equal to 1% VOL are classified as Level 3 alarms.

[0092] The aforementioned alarm points refer to monitoring points on a section of road in a city where a combustible gas concentration alarm is triggered at a specific time. The aforementioned evaluation area can be determined based on the size of the administrative region covered by the city's lifeline infrastructure.

[0093] Because higher concentration values ​​or alarm levels contribute significantly to spatial hotspots, a difference weighting coefficient is set. Level 1 alarm 3. Level 2 alarm 2. Level 3 alarm 1.

[0094] The latitude and longitude data of the alarm points are obtained, and this data can be converted into Gaussian projected coordinates based on the 2000 National Geodetic Coordinate System. Then, the regional kernel density is calculated based on these Gaussian projected coordinates. The specific calculation formula is as follows: , in, denoted as the kernel density of the region. h represents the bandwidth, which is 1 / 10 of the side length of the bounding rectangle of the evaluation region, used to balance the smoothness of the calculation results with the spatial resolution. The result of the kernel function calculation. , Let x be the Gaussian projection x-coordinate of the i1th alarm point. Let y be the Gaussian projection ordinate of the i1th alarm point. This refers to the x-coordinate of the center point of the grid area corresponding to the alarm location. This refers to the ordinate of the center point of the grid area corresponding to the alarm point. The grid area refers to a specific region within the grid obtained by dividing the bounding rectangle of the aforementioned evaluation area into a network according to an h×h grid resolution. This is the total number of all alarm events within the evaluation period.

[0095] The above Normalization is performed to obtain the index value of alarm spatial hotspot distribution characteristic N12. The higher the index value of alarm spatial hotspot distribution characteristic N12, the higher the degree of alarm aggregation in the evaluation area and the greater the spatial distribution risk characteristics.

[0096] The quantitative calculation of the index value of alarm classification time distribution characteristic N13 adopts the entropy method to quantify the multi-level time distribution characteristics into a single time distribution coefficient. The entropy value reflects the distribution difference of multi-level alarms in different cycles.

[0097] First, construct the evaluation matrix R. Let the number of rows in the evaluation matrix R be q, where q is the number of time periods. If we count daily data on a weekly basis, then q = 7; if we count weekly data on a monthly basis, then q = 4. The number of columns in the evaluation matrix R is the number of alarm levels mentioned above, which is 3.

[0098] Matrix elements The entropy value represents the frequency percentage of the u-th level alarm within the s-th time period, i.e., the number of u-th level alarms within the s-th period / the total number of alarms within the s-th period. The calculation formula is: , in, , where is the normalization coefficient.

[0099] Entropy The smaller the value, the more uneven the distribution of the u-th level alarm in the time dimension, and the more significant the risk time clustering characteristics within the period.

[0100] Calculate the weighting coefficient and entropy value for each alarm level. Smaller levels have larger weights. This weighting amplifies the impact of unevenly distributed levels (usually high-risk levels). The calculation formula is: , in, is the weighting coefficient for the u-th level alarm.

[0101] Based on the weighted composite time distribution characteristic value T, the calculation formula is as follows: , in, The coefficient corresponding to the u-th level alarm is 5 when u is 1, 3 when u is 2, and 1 when u is 3. for The maximum value of T. After normalizing the T value, the higher the T value, the greater the degree of imbalance in the time distribution of alarms at each level within the evaluation period, the worse the operational stability, and the greater the risk characteristics of the time distribution.

[0102] The characteristic quantitative analysis of the environmental impact correlation degree N14 index value was carried out using the grey relational analysis method. The grey relational degree reflects the synchronicity and influence of the temporal changes of environmental factors and the changes in the number of alarms.

[0103] First, construct the daily alarm count sequence within the evaluation period t. : , in, This represents the total number of alarms for Day 1. This represents the total number of all alarms on day 2. Let be the total number of alarms on day t.

[0104] Construct quantitative sequences of various environmental factors within an evaluation period of t days. : , in, This is the quantization sequence for day 1. This is the quantization sequence for day 2. Given the quantified sequence for day t, combine the quantified sequences for each day to obtain the final quantified sequence of environmental factors. .

[0105] It should be noted that a quantitative sequence of environmental factors will be generated for each type of environmental factor. .

[0106] Comparison Sequences The daily quantitative values ​​of various environmental factors in China are as follows: ①Due to the impact of heavy rainfall, a daily rainfall of ≥50mm is recorded as "daily rainfall"; otherwise, it is recorded as 0. ② The effect of high temperature: when the daily maximum temperature is ≥35℃, it is recorded as 1; otherwise, it is recorded as 0. ③ The impact of low temperature: when the daily minimum temperature is ≤-5℃, it is recorded as 2; when the daily minimum temperature is ≤0℃ and >-5℃, it is recorded as 1; otherwise, it is recorded as 0. ④ Earthquake impact, when the highest actual magnitude on the day is ≥3. Record it as the "actual magnitude", otherwise record it as 0.

[0107] Daily alarm count sequence After normalization, the calculation formula is: , in, Let k be the normalized effective alarm count on day t, where k = 0, 1, 2, ..., t. Daily alarm count sequence The minimum value in, Daily alarm count sequence The maximum value in.

[0108] For comparison sequences The normalization method can be found in the above daily alarm number sequence. Normalization.

[0109] The correlation degree of the impact of the i-th environmental factor on the alarm on the same day is calculated using the following formula: , in, For the above comparison sequence Sequence of day k The normalized value. Meaning the minimum difference between two levels, quantifying the sequence X for each environmental factor. i First, calculate its relationship with the daily alarm count sequence. The absolute difference for each day Then, from all the differences over these k days, find the smallest value and denote it as... ,all Then select the smallest one as the minimum difference between the two levels. The maximum difference between the two levels, and the minimum difference between the two extremes mentioned above. The only difference is that the minimum value is replaced with the maximum value. ζ is the discrimination coefficient, which can be taken as the industry-standard value of 0.5 to weaken the impact of extreme values ​​on the correlation. Let be the correlation degree on day k.

[0110] Furthermore, the correlation degree is synthesized according to the following formula. : , The higher the value, the more susceptible the pipeline network operation is to environmental factors during the evaluation period, leading to increased risks.

[0111] Take the correlation of various environmental factors The maximum value is used as the index value of the environmental impact correlation degree N14.

[0112] S13, based on the index weights and index values, obtain the safety operation evaluation results of the gas system.

[0113] It should be noted that before obtaining the gas system safety operation evaluation results based on the indicator weights and indicator values, the gas safety operation evaluation method also includes: normalizing the indicator values.

[0114] The above-mentioned evaluation results of the safe operation of the gas system based on the index weights and index values ​​include: using the index weights to perform weighted summation on the normalized index values, and obtaining a comprehensive safety score based on the weighted summation result; comparing the comprehensive safety score with multiple scoring thresholds, and obtaining a safety operation evaluation result based on the comparison results.

[0115] Specifically, after normalizing the values ​​of each indicator using the min-max method, the weighted summation formula for calculating the comprehensive safety score is as follows: , Where X i W is the normalized value of the i-th indicator; i is the comprehensive weight of the i-th indicator (calculated using the ANP method, with the sum of all indicator weights being 1); P is the comprehensive safety score.

[0116] Based on the comprehensive risk score P, three risk levels are defined: low risk level (P≥85 points), indicating that the gas pipeline network and adjacent underground spaces are operating stably and the risks are controllable, requiring only routine maintenance; medium risk level (60 points≤P<85 points), indicating the existence of localized risk points, requiring strengthened inspections and equipment maintenance in key areas; and high risk level (P<60 points), indicating the existence of certain safety hazards, requiring targeted investigation and rectification.

[0117] The following example will illustrate this point.

[0118] Specifically, the evaluation targets are the gas pipeline network and adjacent underground spaces in the main urban area of ​​the city. The total length of the gas pipeline network is 3,200 kilometers, and the adjacent underground spaces include a total of 8,600 gas wells, drainage wells, power wells, and low-voltage wells, with 1,600 sets of monitoring equipment (including monitoring indicators such as combustible gas concentration and equipment status).

[0119] The evaluation period is one month (30 days), and the data sources include gas pipeline network and its underground adjacent monitoring system, operation and maintenance management platform, publicly available data from meteorological departments and equipment inspection records, etc.

[0120] The judgment matrix was constructed based on the expert scores, and all elements of the judgment matrix were the arithmetic mean of the scores of 6 experts (rounded to two decimal places).

[0121] Constructing the first relative importance matrix : , Normalizing the column elements yields: , right The control layer weights are obtained by summing the values ​​in each row and then normalizing them. Calculate the largest eigenvalue Consistency indicators Random Consistency Index (n=3), Consistency ratio It passed the test.

[0122] Construct a judgment matrix between the control layer and the network layers, taking the control layer C1 corresponding to network layers N1-N7 as an example: , After normalization, the local weights of the network layer index corresponding to C1 are obtained. Similarly, the consistency test was passed (CR=0.043<0.1).

[0123] Group C2 contains indicators N8-N11. The judgment matrix and weights within the group are as follows: , After normalization and consistency test ( , , , ), to obtain the index weights within group C2 .

[0124] Similarly, .

[0125] Construct a judgment matrix between network layer groups, taking the monitoring of the impact of maintenance C2 on the operational status C3 as an example: , Local weights were calculated. CR = 0.029 < 0.1, passing the test. Similarly, the calculation yields: ; ; ; .

[0126] Construct a 14×14 hypermatrix The matrix rows / columns correspond to indicators N1-N14, with no intra-group influence, and the control layer-network layer weights are directly used: Use control layer weights For hypermatrix Weighting yields a weighted supermatrix. .

[0127] Weighted hypermatrix Multiplication until convergence (convergence after 8 multiplications in this example) yields the limiting hypermatrix. The weight vectors in each column are consistent, which represents the combined weights of the 14 indicators: Based on the updated hypermatrix weighting and self-multiplication convergence, the final comprehensive weights of the 14 indicators are adjusted as follows: System data was acquired and quantitatively analyzed. During the evaluation period, there were 3 gas pipeline leaks, 2 gas valve leaks, and 1 high-risk leak. The number of methane accumulation alarms in gas wells, power wells, drainage wells, and low-voltage electrical wells were 4, 12, 11, and 13 respectively. There were 1520 online devices, 1600 planned for inspection, 1200 actually inspected, 120 faulty devices, and 90 maintained. 800 data points were sampled, with 720 passing. There were 80 alarm points, with 15 Level 1 alarms, 35 Level 2 alarms, and 50 Level 3 alarms. Meteorological data showed 5 days of heavy rainfall (daily rainfall ≥ 50mm) this month, with no other influencing factors such as high or low temperatures.

[0128] This embodiment includes heavy rainfall, and a time-series correction factor is applied according to the scenario: , Then, in the N1 index: , Then the N1 quantization value: , Similarly, the calculation yields: , , , , , .

[0129] The quantitative values ​​of N4-N7 (biogas accumulation alarm index) are significantly higher, indicating a prominent risk of biogas accumulation in adjacent underground spaces. The core reason is related to water accumulation in well chambers and siltation in pipelines caused by heavy rainfall.

[0130] In the monitoring and maintenance indicators, N8-N11 are directly quantified using statistical data. , , , .

[0131] The low inspection completion rate (N9) and equipment maintenance rate (N10) indicate a low degree of execution of the operation and maintenance plan, requiring further strengthening of monitoring and operation and maintenance management.

[0132] Alarm spatial hotspot distribution characteristics N12 calculation: Evaluation area 8000km², bandwidth The kernel density is calculated using the Gaussian kernel function. The maximum value is 0.012, after normalization. .

[0133] Alarm classification time distribution N13 calculation: Total days: 30 days; Total alarm levels: 3; Calculate entropy value. Weight Substituting into the formula, we get After normalization .

[0134] Environmental impact correlation N14 calculation: Constructing a daily alarm number sequence The composite correlation degree is obtained by analyzing the quantitative sequence of environmental factors corresponding to the environmental factor of heavy rainfall through grey relational analysis. (The correlation between other environmental factors and the overall environmental factors is ≤0.3), therefore .

[0135] The distribution characteristics of the quantitative indicators in the operational status jointly indicate that under the heavy rainfall this month, the risk of pipeline network in low-lying areas shows a trend of increased spatial clustering and temporal correlation, resulting in a significant increase in pipeline network operation risk during the heavy rainfall period.

[0136] After normalizing all indicators and using ANP to calculate weights, a weighted sum is obtained: , Based on the safety level classification, the comprehensive score of the gas pipeline network and its adjacent underground space for the month was 72.7 points, indicating a medium risk level. The main reasons for this risk include: the pipeline network's operational risks are primarily manifested in high-risk gas events and biogas accumulation events; the sensitivity of the rainfall environment has increased the risks to some extent; in monitoring and maintenance work, insufficient coverage and response efficiency, weak support capabilities, and a low equipment inspection rate may have resulted in some risk points going undetected; at the same time, heavy rainfall has had a certain impact on the overall quality of equipment; in terms of operational status, the heavy rainfall this month had a high impact on pipeline network operation, with some points spatially concentrated during periods of heavy rainfall; and the lack of differentiated responses to high-level alarms across different alarm levels over time, indicating that the overall risk is stable and controllable.

[0137] Based on risk analysis and assessment, suggestions are made for decision support and operation and maintenance management. Accurately implement risk point classification and control, and strengthen pipeline inspection and maintenance based on the N12 kernel density analysis results of alarm spatial hotspot distribution characteristics; focus on the concentration changes of alarm points during the next period of similar rainfall based on rainfall data, and simultaneously increase the inspection frequency of the monitoring system during the corresponding period; strengthen the management and assessment of inspection personnel, track the recovery of equipment data quality after heavy rainfall, and enhance coverage efficiency and process closure.

[0138] in, The first relative importance matrix The matrix obtained after normalizing the column elements. To and The corresponding final weight vector , To and The corresponding final weight vector , To and The corresponding final weight vector , To and The corresponding final weight vector , To and The corresponding final weight vector , To and The corresponding final weight vector , To and The corresponding final weight vector , To and The corresponding final weight vector , This represents the index value corresponding to the gas pipeline leak incident rate N1. This represents the index value corresponding to the gas valve leakage incident incidence rate N2. This represents the index value corresponding to the incidence rate N3 of high-risk gas leak events. This refers to the index value corresponding to the biogas accumulation ratio N4 in a gas-fired well. The index value corresponding to the biogas alarm ratio N5 of the drainage well. The index value corresponding to the biogas alarm ratio N6 in the power well. The index value corresponding to the biogas alarm ratio N7 in the weak current well. The online rate N8 is the corresponding index value. This refers to the indicator value corresponding to the equipment inspection completion rate N9. This represents the index value corresponding to the equipment maintenance rate N10. This represents the indicator value corresponding to the data quality pass rate N11. The index value corresponding to the spatial hotspot distribution characteristic N12 of the alarm is... The index value corresponding to the alarm classification time distribution characteristic N13 is... This represents the index value corresponding to the environmental impact correlation degree N14.

[0139] In summary, the gas safety operation evaluation method of this invention obtains system data of the gas system and the index weights of each indicator in a pre-established gas safety operation evaluation network model. The gas system includes a gas pipeline network and underground spaces located at a distance less than a preset distance threshold from the gas pipeline network. The underground spaces include at least one of gas wells, drainage wells, power wells, and low-voltage electrical wells. The method obtains the index values ​​of each indicator in the gas safety operation evaluation network model based on the system data. The safety operation evaluation result of the gas system is obtained based on the index weights and index values. Therefore, by including both the gas pipeline network and the underground spaces adjacent to it in the monitoring, a comprehensive evaluation is achieved, and a unified evaluation is achieved through the pre-established gas safety operation evaluation network model. Furthermore, by obtaining the influence strength between any two indicators corresponding to different control layer elements, the interdependence and feedback relationships between indicators are taken into account, improving the accuracy of weight calculation. Moreover, by setting a comprehensive safety score, quantitative scoring is achieved, and clear level classification is supported, enabling precise guidance for operation and maintenance decisions. Specifically, a three-tiered evaluation index system is constructed, comprising a target layer, a control layer, and a network layer. The control layer considers three major evaluation dimensions in urban gas safety monitoring: pipeline network risk, monitoring and maintenance, and operational status. The network layer covers relevant important monitoring indicators for urban gas pipeline networks and adjacent underground spaces. Subsequently, based on data collected from monitoring equipment in the gas pipeline network and its adjacent underground spaces, a method combining spatiotemporal correlation analysis is employed, including the introduction of improved time-series correction coefficients, kernel density, and entropy calculations, to analyze and quantify each indicator. Finally, the weights and values ​​of each indicator are synthesized to quantitatively evaluate and classify the safety level. This approach addresses the shortcomings of existing evaluation methods, such as single indicators, incomplete evaluation scope, neglect of inter-indicator dependencies, and qualitative evaluation results. It provides accurate quantitative basis for gas pipeline network operation and maintenance decisions and, combined with indicator analysis, offers targeted auxiliary decision-making suggestions for urban gas safety operation and management.

[0140] Furthermore, the present invention proposes an electronic device.

[0141] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention.

[0142] like Figure 4 As shown, the electronic device 500 includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502. Optionally, the electronic device 500 may also include a transceiver 504. It should be noted that in practical applications, the transceiver 504 is not limited to one type, and the structure of this electronic device 500 does not constitute a limitation on the embodiments of the present invention.

[0143] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 501 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0144] Bus 502 may include a pathway for transmitting information between the aforementioned components. Bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 502 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0145] The memory 503 stores a computer program corresponding to the gas safety operation evaluation method of the above embodiments of the present invention. This computer program is controlled and executed by the processor 501. The processor 501 executes the computer program stored in the memory 503 to implement the content shown in the aforementioned method embodiments.

[0146] in, Figure 4 The electronic device 500 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0147] According to the electronic device of the present invention, by using the gas safety operation evaluation method of the above embodiments, a comprehensive evaluation is achieved by including both the gas pipeline network and the underground space adjacent to the gas pipeline network in the monitoring, and a unified evaluation is achieved by using a pre-established gas safety operation evaluation network model.

[0148] Furthermore, this invention proposes a gas safety operation evaluation device.

[0149] Figure 5 This is a structural block diagram of the gas safety operation evaluation device according to an embodiment of the present invention.

[0150] like Figure 5 As shown, the gas safety operation evaluation device 100 includes: an acquisition module 101, used to acquire system data of the gas system and the index weights of each index in a pre-established gas safety operation evaluation network model, and to obtain the index values ​​of each index in the gas safety operation evaluation network model based on the system data. The gas system includes a gas pipeline network and underground space with a distance of less than a preset distance threshold from the gas pipeline network. The underground space includes at least one of a gas well, a drainage well, an electrical well, and a weak current well. An evaluation module 102 is used to obtain the safety operation evaluation results of the gas system based on the index weights and index values.

[0151] It should be noted that other specific embodiments of the gas safety operation evaluation device of this invention can refer to the gas safety operation evaluation method of the above embodiments.

[0152] According to the gas safety operation evaluation device of the present invention, comprehensive evaluation is achieved by including both the gas pipeline network and the underground space adjacent to the gas pipeline network in the monitoring, and unified evaluation is achieved through a pre-established gas safety operation evaluation network model.

[0153] Furthermore, this invention proposes a gas safety operation evaluation system.

[0154] Figure 6 This is a structural block diagram of the gas safety operation evaluation system according to an embodiment of the present invention.

[0155] like Figure 6 As shown, the gas safety operation evaluation system 10 includes the gas safety operation evaluation device 100 described above.

[0156] According to the gas safety operation evaluation system of the present invention, a comprehensive evaluation is achieved by including both the gas pipeline network and the underground space adjacent to the gas pipeline network in the monitoring, and a unified evaluation is achieved through a pre-established gas safety operation evaluation network model.

[0157] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein can be considered as a ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0158] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0159] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0160] In the description of this specification, the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and should not be construed as limiting the present invention.

[0161] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0162] In this specification, unless otherwise stated, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly defined. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0163] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0164] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the safe operation of gas systems, characterized in that, The method includes: The system data of the gas system and the index weights of each indicator in the pre-established gas safety operation evaluation network model are obtained. The gas system includes a gas pipeline network and an underground space at a distance of less than a preset distance threshold from the gas pipeline network. The underground space includes at least one of a gas well, a drainage well, an electrical well, and a low-voltage electrical well. The index values ​​of each indicator in the gas safety operation evaluation network model are obtained based on the system data. The safety operation evaluation result of the gas system is obtained based on the index weights and index values.

2. The gas safety operation evaluation method according to claim 1, characterized in that, The gas safety operation evaluation network model includes a target layer, a control layer, and a network layer. The control layer elements include pipeline risk, monitoring and maintenance, and operational status. The indicators are network layer elements of the network layer, and there are multiple indicators, each of which corresponds to one control layer element.

3. The gas safety operation evaluation method according to claim 2, characterized in that, Obtain the index weights of each index in the gas safety operation evaluation network model, including: Obtain the first importance of each control layer element to the target layer element of the target layer, and for each indicator, obtain the second importance of the indicator to the control layer element corresponding to the indicator, and for any two indicators corresponding to different control layer elements, obtain the influence strength between the indicators. Based on the first importance, a first relative importance is obtained between any two target layer elements, and based on the second importance, a second relative importance is obtained between any two indicators corresponding to the same control layer element. The index weights are obtained based on the first relative importance, the second relative importance, and the influence intensity.

4. The gas safety operation evaluation method according to claim 3, characterized in that, The step of obtaining the index weight based on the first relative importance, the second relative importance, and the influence intensity includes: The weights of the control layer elements are obtained based on the first relative importance, and a supermatrix is ​​obtained based on the second relative importance and the influence strength, wherein the supermatrix includes the influence strength of the first indicator on the second indicator, and the first indicator and the second indicator are any indicators in the network layer; The index weights are obtained based on the weights and the supermatrix.

5. The gas safety operation evaluation method according to claim 4, characterized in that, The step of obtaining the index weights based on the weights and the hypermatrix includes: The weight vector is obtained based on the weights; Multiply the weight vector by the hypermatrix to obtain the weighted hypermatrix; Multiply the weighted hypermatrix by itself to obtain the limiting hypermatrix; The elements of the limiting hypermatrix are used as the index weights.

6. The gas safety operation evaluation method according to claim 2, characterized in that, The indicators corresponding to the pipeline network risks include the incidence rate of gas pipeline leaks, the incidence rate of gas valve leaks, the incidence rate of high-risk gas leaks, the biogas accumulation ratio of gas wells, the biogas alarm ratio of drainage wells, the biogas alarm ratio of power wells, and the biogas alarm ratio of weak current wells. The indicators corresponding to the monitoring and maintenance include the equipment online rate, the equipment inspection completion rate, the equipment maintenance rate, and the data quality qualification rate. The indicators corresponding to the operational status include the spatial hotspot distribution characteristics of alarms, the temporal distribution characteristics of alarm classifications, and the correlation with environmental impact.

7. The gas safety operation evaluation method according to claim 1, characterized in that, Before obtaining the safety operation evaluation result of the gas system based on the index weights and the index values, the method further includes: The index values ​​are normalized. The step of obtaining the safety operation evaluation result of the gas system based on the index weights and the index values ​​includes: The normalized index values ​​are weighted and summed using the index weights, and a comprehensive security score is obtained based on the weighted summation result. The comprehensive safety score is compared with multiple scoring thresholds, and the safety operation evaluation result is obtained based on the comparison results.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the computer program is executed by the processor, it implements the gas safety operation evaluation method according to any one of claims 1-7.

9. A gas safety operation evaluation device, characterized in that, include: The acquisition module is used to acquire system data of the gas system and the index weights of each index in the pre-established gas safety operation evaluation network model, and to obtain the index values ​​of each index in the gas safety operation evaluation network model based on the system data. The gas system includes a gas pipeline network and an underground space at a distance of less than a preset distance threshold from the gas pipeline network. The underground space includes at least one of a gas well, a drainage well, an electrical well, and a low-voltage well. The evaluation module is used to obtain the safety operation evaluation result of the gas system based on the index weights and index values.

10. A gas safety operation evaluation system, characterized in that, Includes the gas safety operation evaluation device according to claim 8.

Images