CN115049315A - Power utilization safety risk assessment method, device, equipment and storage medium - Google Patents
Power utilization safety risk assessment method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a storage medium for evaluating power utilization safety risk, wherein the method comprises the following steps: establishing an evaluation system of the power utilization safety risk by using indexes influencing the power utilization safety based on the domination relationship; determining subjective weight of each index in the evaluation system; determining objective weights of the indexes in the evaluation system; performing combination optimization on the subjective weight and the objective weight based on expert evaluation sample data of power utilization safety to obtain an optimal combination weight; and evaluating the power utilization safety risk result based on the optimal combined weight. The technical scheme provided by the embodiment of the invention can effectively improve the accuracy of the user safety risk assessment.
Description
Technical Field
The invention relates to the technical field of power utilization safety risk assessment, in particular to a power utilization safety risk assessment method, a device, equipment and a storage medium.
Background
The safety of electricity consumption of high-voltage power customers is an important link for ensuring the safe and stable operation of a power system. Risk assessment is carried out to high-voltage electricity customer power consumption safety is that power supply enterprise ensures the basic work of power consumption safety, helps improving power supply enterprise supervision and management level, and the better implements differentiated management and control measure to different risk customers, promotes power consumption safety inspection efficiency.
The evaluation of the electrical safety risk of the high-voltage power customer is a typical multi-objective decision problem. The existing solutions to the multi-objective decision problem mainly include a subjective weighting method, an objective weighting method and a subjective and objective comprehensive weighting method. However, the above methods may result in a large difference from the real evaluation result and a low degree of accuracy.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for evaluating the safety risk of electricity utilization, which can effectively improve the accuracy of the safety risk evaluation of a user.
According to an aspect of the present invention, there is provided an electricity safety risk assessment method, including:
establishing an evaluation system of the power utilization safety risk by using indexes influencing the power utilization safety based on the domination relationship;
determining subjective weight of each index in the evaluation system;
determining an objective weight of each index in the evaluation system;
performing combination optimization on the subjective weight and the objective weight based on expert evaluation sample data of power utilization safety to obtain an optimal combination weight;
and evaluating the power utilization safety risk result based on the optimal combined weight.
According to another aspect of the present invention, there is provided an electrical safety risk assessment apparatus, including:
the establishing module is used for establishing an evaluation system of the power utilization safety risk according to the indexes influencing the power utilization safety based on the domination relation;
the subjective weight determining module is used for determining the subjective weight of each index in the evaluation system;
an objective weight determination module for determining objective weights of the indexes in the evaluation system;
the combination weight optimization module is used for carrying out combination optimization on the subjective weight and the objective weight based on expert evaluation sample data of power utilization safety to obtain an optimal combination weight;
and the evaluation module is used for evaluating the power utilization safety risk result based on the optimal combination weight.
According to another aspect of the present invention, an embodiment of the present invention provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method according to any of the embodiments of the invention.
According to another aspect of the present invention, an embodiment of the present invention provides a computer-readable storage medium, which is characterized in that the computer-readable storage medium stores computer instructions for causing a processor to implement the method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme provided by the embodiment of the invention, subjective weight and objective weight are combined and optimized through expert evaluation sample data based on electricity safety to obtain optimal combined weight; and evaluating the power utilization safety risk result based on the optimal combination weight, so that the accuracy of user safety risk evaluation can be effectively improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for evaluating a risk of electricity consumption safety according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for evaluating a risk of electricity consumption safety according to an embodiment of the present invention;
fig. 3 is a block diagram of a structure of an electrical safety risk assessment apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the related art, the evaluation of the power utilization safety risk of a high-voltage power customer is a typical multi-objective decision problem. The existing solutions to the multi-objective decision problem mainly include a subjective weighting method, an objective weighting method and a subjective and objective comprehensive weighting method. Among them, the subjective weighting method is most commonly used as an analytic hierarchy process. The analytic hierarchy process includes decomposing the elements relevant to decision into target, criterion, scheme and other levels, and comparing the indexes in different levels to determine the weight of the indexes. The whole process embodies the basic characteristics of human decision thinking, namely decomposition and comprehensive judgment, and is a quantitative and qualitative combined method. However, the process of determining the index weight by the analytic hierarchy process depends on the subjective experience of a decision maker, and the evaluation accuracy is easily influenced due to insufficient objectivity. The objective weighting method mainly includes an entropy weight method, a principal component analysis method, and the like. The method calculates the weight according to the actual value distribution conditions of different indexes, can avoid the influence of subjective uncertain factors of people on the weight result, and is more objective than a subjective weighting method. However, the objective weighting method has the following defects: the weight calculated by the entropy weight method completely depends on the distribution of real data and is influenced by the data acquisition process and method. If the acquired data distribution and the actual distribution have large deviation, the calculated weight can be greatly different from the real weight.
The subjective and objective comprehensive weighting method is to combine the two methods, such as entropy weighting-hierarchy analysis, but the methods simply average the bottom layer index weights obtained by the subjective weighting method and the objective weighting method. In the related art, it is further proposed to find a so-called "most satisfactory" combination weight method, finding a group among possible weight combinations so that the deviation between the combination weight and the weight of a single evaluation method is minimized. However, this optimization goal can only guarantee that the combined weight and both weights are closest. It is obvious that there is no guarantee that the evaluation result of the combined weights is the most accurate. In addition, these methods simply weight the subjective and objective weight vectors, i.e., all subjective or objective indicators use the same weighting factor. However, the accuracy of the same method for assigning values to different indexes cannot be completely consistent, and a combination coefficient is set for each index respectively for a more accurate and effective method for fusing subjective and objective weighting.
The method provided by the embodiment of the invention can fully utilize the existing power utilization safety risk evaluation result of the high-voltage power users of the power supply enterprises as experience knowledge to construct a new optimization target. And the particle swarm algorithm is utilized to optimize and solve the combination coefficient of the subjective weight and the objective weight so as to more accurately fuse the subjective weight and the objective weight, thereby effectively improving the accuracy of the customer electricity utilization safety risk evaluation.
Fig. 1 is a flowchart of an electrical safety risk assessment method according to an embodiment of the present invention, where the present embodiment is applicable to a situation of high-voltage electrical safety risk assessment, the method may be executed by an electrical safety risk assessment apparatus, the apparatus may be implemented in a form of hardware and/or software, the apparatus may be configured in an electronic device, and the electronic device may be a terminal or a server.
As shown in fig. 1, the method provided by the embodiment of the present invention includes:
s110: and establishing an evaluation system of the power utilization safety risk based on the indexes influencing the power utilization safety based on the domination relationship.
In the embodiment of the invention, the indexes of the electricity safety comprise the indexes of the following aspects; marketing basic information; safety tools and spare parts; site environment and safety protection; the equipment is safe to operate and maintain; a metering device; a self-contained or emergency power supply; regulation and electricians; stealing electricity and default electricity. Alternatively, 42 criteria may be selected from the above aspects. Optionally, the established system for evaluating the electrical safety risk may refer to table 1.
TABLE 1
S120: and determining the subjective weight of each index in the evaluation system.
In this embodiment of the present invention, optionally, the determining the subjective weight of each index of the evaluation system includes: and based on an analytic hierarchy process, taking the weight value of each index in the evaluation system as the subjective weight of each index. The method for evaluating the subjective weight of each index in the system comprises the following steps of:
the method comprises the following steps: constructing a judgment matrix: the experts analyze the evaluation system in each level, and respectively compare the same indexes of the lower layer belonging to a certain index of the upper layer in a pairwise manner according to the sequence from high to low, so as to establish a corresponding judgment matrix.
Step two: and (3) checking the consistency of the judgment matrix: and (4) sequentially carrying out consistency check on all the judgment matrixes, adopting the judgment matrixes if all the judgment matrixes of each level pass the check, and returning to the first step to regenerate the judgment matrixes if not.
Step three: and (3) index weight calculation: and calculating the weight of each level index to the previous level index according to each judgment matrix by using a 1-9 scale method. Where the 1-9 scale is referred to in Table 2.
TABLE 2
Step four: and (3) index synthesis weight calculation: from top to bottom, calculating the synthetic weight of all the indexes at the bottom layer to the total target, thereby obtaining the weight vector of the subjective weighting method
Wherein the content of the first and second substances,
is the subjective weight of the n1 th index.
S130: determining objective weights for each of the indicators in the evaluation system.
In this embodiment of the present invention, optionally, correspondingly, the determining the objective weight of each index in the evaluation system includes: and determining the weight value of each index in the evaluation system based on an entropy weight method, and taking the weight value as the objective weight of each index. Specifically, determining the weight value of each index in the evaluation system based on an entropy weight method, and using the weight value as the objective weight of each index, may include the following steps:
step one, establishing an objective evaluation matrix: selecting a set number of high-voltage power users, and constructing the specific scores of the users in all indexes into an objective evaluation matrix R according to the following formula:
wherein m represents the number of high-voltage power users, the value of m cannot be lower than 50, otherwise, a statistical rule cannot be embodied, the entropy weight is difficult to calculate effectively, optionally, n is 42 which is the number of bottom-layer indexes, and each row in the objective evaluation matrix corresponds to the score value of all the bottom-layer indexes of one customer.
Step two: and carrying out dimensionless processing on the objective evaluation matrix.
Let R be the optimum value of each column
The profitability index is preferably a higher index value, and the cost index is preferably a lower index value.
The original data in the objective evaluation matrix is dimensionless and then is recorded as matrix S ═ (S) ij ) m×n
Normalizing S and recording
Step three: calculating entropy values corresponding to the indexes: wherein the entropy of the jth evaluation index is
Wherein the content of the first and second substances,
step four: calculating entropy weight of each index according to entropy value
Wherein the content of the first and second substances,
by the above-mentioned method, the method,obtaining an index vector of an entropy weight method of objective assignment
I.e., an objective index weight vector, wherein,
the objective weight of the n2 th index.
S140: and carrying out combined optimization on the subjective weight and the objective weight based on expert evaluation sample data of power utilization safety to obtain an optimal combined weight.
In the embodiment of the invention, the expert evaluation sample data of the electricity utilization safety comprises the risk rating of the selected user sample and the scores corresponding to all the indexes. Optionally, the performing combination optimization on the subjective weight and the objective weight based on expert evaluation sample data of power consumption safety to obtain an optimal combination weight includes: extracting a sample from the power utilization safety inspection condition database to obtain expert evaluation sample data; setting the population number of particle swarms, and initializing the positions and the speeds of the particles; determining an optimization objective function according to the minimum error between the risk rating result obtained by calculating the corresponding combination weight of the particles and the real rating result in the expert evaluation sample data, determining a fitness function according to the optimization objective function, determining the fitness value of each particle according to the fitness function, and obtaining an individual optimal value and a global optimal value; updating a particle velocity and position based on the individual optimal values and the global optimal values; judging whether the evolution of the particle swarm reaches a termination condition; if not, returning to the operation of determining the fitness value of each particle according to the fitness function; and if so, taking the output global optimal value as an optimal solution, and obtaining the optimal combination weight based on the optimal solution.
In this embodiment of the present invention, optionally, the determining an optimization objective function with the minimum error between the risk rating result calculated by the particle corresponding combination weight and the true rating result in the expert evaluation sample data includes:
correspondingly, the fitness function is determined by the optimization objective function, and the fitness function comprises the following steps:
a fitness function is determined based on the following formula:
wherein, f (x) i ) Representing the risk rating calculated from the corresponding combination weights of the particles, y i For a true rating result, L is the number of samples used for fitness calculation; and Y is a fitness function.
In the embodiment of the present invention, specifically, the determining the optimal combining weight may include the following steps:
acquiring expert evaluation sample data: and respectively extracting the same number of samples from the electricity utilization safety inspection condition database according to different types of risk ratings, and taking the ratings of the samples and the scores corresponding to the indexes as expert evaluation sample data. Wherein the risk rating may be major risk, general risk, and minor risk, and the same number of samples may be drawn from each rating level. The number of extractions can be set as required. The scores of the indexes corresponding to the marketing basic information can be referred to table 3.
TABLE 3
The scores of the indexes of the safety tool and the spare parts can be referred to table 4.
TABLE 4
The scores of the indexes in the aspects of the site environment and the safety protection can be referred to table 5.
TABLE 5
The scores corresponding to the respective indexes in the aspect of the measuring device can be referred to table 6.
TABLE 6
The scores of the indexes corresponding to the safety operation and maintenance aspect of the equipment can refer to table 7.
TABLE 7
The scores corresponding to the respective indexes in the self-contained or emergency power supply can be referred to in table 8.
TABLE 8
The scores of the respective indexes in the regulation and electrician can be referred to table 9.
TABLE 9
The scores corresponding to the indexes in terms of electricity stealing and default electricity consumption can be referred to in table 10.
Step two, initializing a population: setting the number of populations, optionally the number of populations may be 100, and initializing the location x of each particle i (0) And velocity v i (0). Wherein the dimension of each particle is twice the index number, namely 84, and the coordinate value of the particle in each dimension is the combination coefficient of the subjective weight or the objective weight.
Wherein
Is a combination coefficient of the subjective weights,
is a combination coefficient of the objective weight, and both satisfy the constraint condition
Step three: calculate fitness value for all particles: in order to fully utilize the existing expert evaluation sample data, a new fitness function is designed, and the minimum error between the evaluation result output by the corresponding combination weight of the particles and the real evaluation result is taken as an optimization target:
wherein, f (x) i ) Represents the risk rating calculated from the corresponding combination weights of the particles (with 1-4 levels being represented by integers 1-4, respectively), y i For a true rating result, L is the number of clients for fitness calculation, and L needs to be large enough to effectively evaluate the accuracy of the index system, and in the present invention, L is 100.
Step four: determining an individual optimum p for a current iteration step i (t) and a global optimum p g And (t), specifically, an individual optimal value and a global optimal value can be determined according to the fitness value.
Step five: updating particle velocity and position: the velocity and position of the particles are updated separately as follows,
x i (t+1)＝x i (t)+v i (t+1) (6)
v i (t+1)＝wv i (t)+c 1 r 1 (t)[p i (t)-x i (t)]+c 2 r 2 (t)[p g (t)-x i (t)] (7)
wherein x is i (t +1) and x i (t) is the coordinate value of the ith particle at t +1 and t, v i (t +1) and v i (t) is the velocity of the ith particle at t +1 and t, w is the inertial weight, and c is 0.5 1 And c 2 Respectively an individual learning factor and a social learning factor, c 1 ＝c 2 ＝2。
Step six: and if the evolution of the particle swarm reaches the termination condition, stopping iteration and outputting the optimal particle coordinates, namely outputting the global optimal value of the optimal particles, and otherwise, returning to the third step. The termination condition is that a set iteration number is reached, and optionally, the set iteration number may be 200.
Step seven: calculating optimized combining weights: obtaining a global optimal value as an optimal solution by particle swarm optimization to obtain an optimal combination weight, and setting the optimal solution as
The optimal combining weight is:
wherein, W opt In order to optimize the combining weights,
is the subjective weight of the nth index,
is the objective weight of the nth index.
S150: and evaluating the power utilization safety risk result based on the optimal combined weight.
In this embodiment of the present invention, optionally, the evaluating the power utilization safety risk result based on the optimal combination weight includes: determining an electricity safety risk assessment value based on the following formula, and determining an evaluation grade according to the electricity safety risk assessment value:
wherein z is an electricity safety risk assessment value, z i Is the fraction value of the ith index of the sample;
for optimal combining weights, n is the number of indices of the sample.
Optionally, determining an evaluation level according to the electrical safety risk assessment value may include: the evaluation grade is determined based on the following formula:
therefore, according to the technical scheme provided by the embodiment of the invention, all factors influencing the electricity utilization safety are fully considered, and an index system containing 8 aspects (criteria) and 42 bottom-layer indexes is established. On the basis, the existing (high-voltage power) user safety risk evaluation result of a power supply enterprise is introduced as supervision information to guide the target optimization of the particle swarm optimization, and the weights calculated by a subjective method and an objective method are accurately fused, so that the accuracy of index weight calculation is improved, the power utilization safety risk is better evaluated, and support is provided for work such as periodic and special power utilization inspection plan formulation.
Therefore, the technical scheme provided by the embodiment of the invention is different from the prior subjective and objective comprehensive weighting method which takes the minimum deviation from the subjective method and the objective method as the optimization target, and the technical scheme provided by the embodiment of the invention optimizes the combination coefficient by using the minimum error (highest precision) between the risk rating result obtained by the optimized combination weight and the real evaluation result given by an expert, so that the method is more direct and can also fully utilize the existing expert experience knowledge.
In the related technology, the subjective and objective comprehensive weighting method multiplies the subjective weight vector and the objective weight vector by a coefficient respectively to perform weighted combination, and is a treatment of indiscriminate processing of all indexes in an evaluation system. The method provided by the embodiment of the invention adopts a more refined weight fusion mode and particle swarm optimization, each index corresponds to a respective combination coefficient, and different combination coefficients can be set according to different index characteristics, so that subjective and objective weights are more finely fused, and the accuracy of evaluation is improved.
According to the technical scheme provided by the embodiment of the invention, subjective weight and objective weight are combined and optimized through expert evaluation sample data based on electricity safety to obtain optimal combined weight; and evaluating the power utilization safety risk result based on the optimal combination weight, so that the accuracy of user safety risk evaluation can be effectively improved.
In order to more clearly express the technical solution provided by the embodiment of the present invention, fig. 2 is a flowchart of an electricity safety risk assessment method provided by the embodiment of the present invention, where the method specifically includes:
step 10: constructing a risk assessment index system of the electricity utilization safety of the high-voltage user: factors influencing the electricity safety of high-voltage customers are selected from multiple aspects, and a hierarchical index system, namely an evaluation system of electricity safety risk is established according to a domination relation, wherein the selection of each index can refer to the above.
Step 20: calculating the weight of each index based on an analytic hierarchy process, wherein the method specifically comprises the following steps:
step 201) constructing a judgment matrix: and analyzing each level of the evaluation index system by a service expert, and respectively comparing each layer of similar indexes of a lower layer which belongs to a certain index of an upper layer in a sequence from high to low in pairs to establish a corresponding judgment matrix.
Step 203: and (3) checking the consistency of the judgment matrix: and (4) sequentially carrying out consistency check on all the judgment matrixes, adopting the judgment matrixes if all the judgment matrixes of each level pass the check, and returning to the step 201 to regenerate the judgment matrixes if not.
Step 204: and (3) index weight calculation: and calculating the weight of each level index to the index of the previous level according to each judgment matrix.
Step 205: and (3) index synthesis weight calculation: from top to bottom, calculating the synthetic weight of all the indexes at the bottom layer to the total target, thereby obtaining the weight vector of the subjective weighting method
I.e. the subjective weight vector.
Step 30: calculating objective index weight based on an entropy weight method, wherein the objective index weight specifically comprises the following steps:
step 301: constructing an objective evaluation matrix: a set number of high-voltage power users are selected, the specific scores of the users in all indexes are constructed into an objective evaluation matrix, and the specific method for constructing the objective evaluation matrix can refer to the above.
Step 302: the objective evaluation matrix is subjected to dimensionless processing, and the specific method can refer to the above.
Step 303: and calculating entropy values corresponding to the indexes.
Step 304: calculating the entropy weight of each index according to the entropy value, thereby obtaining the index vector of the entropy weight method of the objective assignment
I.e. the objective indicator vector.
Step 40: determining the optimal combination weight by adopting a particle swarm algorithm based on expert evaluation sample data, wherein the method specifically comprises the following steps:
step 401: sorting expert evaluation sample data: and respectively extracting the same number of samples from the electricity utilization safety inspection condition database according to different categories of risk ratings, and taking the ratings of the samples and the scores of corresponding indexes as data of the evaluation particles.
Step 402: initializing a population: setting the population number and initializing the location x of each particle i (0) And velocity v i (0) The coordinate value of each dimension of the particle is the combination coefficient of the subjective weight or the objective weight.
Wherein
Is a combination coefficient of the subjective weights,
is a combination coefficient of objective weight, and both satisfy constraint conditions
Step 403: calculate fitness value for all particles: in order to fully utilize the existing expert evaluation sample data, a new optimization fitness function is designed, and the minimum error between the evaluation result output by the corresponding combination weight of the particles and the real evaluation result is taken as an optimization target:
wherein, f (x) i ) Representing the risk rating calculated from the corresponding combination weights of the particles, y i And L is the number of clients for fitness calculation for the true rating result given by the expert.
Step 404) determining the individual optimum value p for the current iteration step i (t) and a global optimum p g (t)。
Step 405) update particle velocity and position: the velocity and position of the particles are updated separately according to the following equations,
x i (t+1)＝x i (t)+v i (t+1)
v i (t+1)＝wv i (t)+c 1 r 1 (t)[p i (t)-x i (t)]+c 2 r 2 (t)[p g (t)-x i (t)]
wherein x is i (t +1) and x i (t) is the coordinates of the ith particle at times t +1 and t, v i (t +1) and v i (t) is the velocity of the ith particle at times t +1 and t, w is the inertial weight, c 1 And c 2 Respectively individual learning factor and societyA learning factor.
Step 406: if the termination condition is reached, stopping iteration and outputting the optimal particle coordinates, otherwise, returning to the step 403.
Step 407) calculate the optimized combining weights: obtaining optimal combination weight from optimal solution obtained by particle swarm optimization, and setting the optimal solution as
The optimal combining weight is:
step 50) calculating the customer electricity utilization risk result according to the optimal combination weight
Step 501) according to the optimal combination weight W opt Calculating user electricity utilization risk evaluation value z
Step 502: and outputting an evaluation grade E according to the risk evaluation value y:
fig. 3 is a schematic structural diagram of an electrical safety risk assessment apparatus according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes:
the establishing module 310 is used for establishing an evaluation system of the power utilization safety risk according to the indexes affecting the power utilization safety based on the domination relationship;
a subjective weight determination module 320, configured to determine a subjective weight of each indicator in the evaluation system;
an objective weight determination module 330, configured to determine an objective weight of each of the indicators in the evaluation system;
the combination weight optimization module 340 is configured to perform combination optimization on the subjective weight and the objective weight based on expert evaluation sample data of power consumption safety to obtain an optimal combination weight;
and the evaluation module 350 is used for evaluating the power utilization safety risk result based on the optimal combination weight.
Optionally, the performing, by the expert evaluation sample data based on the power consumption safety, combined optimization on the subjective weight and the objective weight includes:
and performing combined optimization on the subjective weight and the objective weight by adopting a particle swarm algorithm based on the electricity safety expert evaluation sample.
Optionally, the performing combination optimization on the subjective weight and the objective weight by using a particle swarm algorithm based on the power consumption safety expert evaluation sample to obtain an optimal combination weight includes:
extracting a sample from the power utilization safety inspection condition database to obtain expert evaluation sample data;
setting the population number of particle swarms, and initializing the positions and the speeds of the particles;
determining an optimization objective function according to the minimum error between the risk rating result obtained by calculating the corresponding combination weight of the particles and the real rating result in the expert evaluation sample data, determining a fitness function according to the optimization objective function, determining the fitness value of each particle according to the fitness function, and obtaining an individual optimal value and a global optimal value;
updating a particle velocity and position based on the individual optimal values and the global optimal values;
judging whether the evolution of the particle swarm reaches a termination condition;
if not, returning to the operation of determining the fitness value of each particle according to the fitness function;
and if so, taking the output global optimal value as an optimal solution, and obtaining the optimal combination weight based on the optimal solution.
Optionally, the determining an optimization objective function with the minimum error between the risk rating result calculated by the particle corresponding combination weight and the true rating result in the expert evaluation sample data includes:
correspondingly, the fitness function is determined by the optimization objective function, and the fitness function comprises the following steps:
a fitness function is determined based on the following formula:
wherein, f (x) i ) Representing the risk rating calculated from the corresponding combination weights of the particles, y i For a true rating result, L is the number of samples used for fitness calculation; and Y is a fitness function.
Optionally, the determining the subjective weight of each index in the evaluation system includes:
based on an analytic hierarchy process, taking the weight value of each index in the evaluation system as the subjective weight of each index;
correspondingly, determining the objective weight of each index in the evaluation system comprises the following steps:
and determining the weight value of each index in the evaluation system based on an entropy weight method, and taking the weight value as the objective weight of each index.
Optionally, the indicator of the electricity safety includes the indicators in the following aspects;
marketing basic information;
safety tools and spare parts;
site environment and safety protection;
the equipment is safe to operate and maintain;
a metering device;
a self-contained or emergency power supply;
regulation and electricians;
stealing electricity and default electricity.
Optionally, the evaluating the power utilization safety risk result based on the optimal combination weight includes:
determining an electricity safety risk assessment value based on the following formula, and determining an evaluation grade according to the electricity safety risk assessment value:
wherein z is an electricity safety risk assessment value, z i Is the fraction value of the ith index of the sample;
The device provided by the embodiment of the invention can execute the electricity utilization safety risk assessment method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
FIG. 4 shows a schematic block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 11 performs the various methods and processes described above, such as the electrical safety risk assessment method.
In some embodiments, the electrical safety risk assessment method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When loaded into RAM 13 and executed by processor 11, may perform one or more of the steps of the above-described electrical safety risk assessment method. Alternatively, in other embodiments, the processor 11 may be configured to perform the electricity safety risk assessment method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An electricity safety risk assessment method is characterized by comprising the following steps:
establishing an evaluation system of the power utilization safety risk by using indexes influencing the power utilization safety based on the domination relationship;
determining subjective weight of each index in the evaluation system;
determining an objective weight of each index in the evaluation system;
performing combination optimization on the subjective weight and the objective weight based on expert evaluation sample data of power utilization safety to obtain an optimal combination weight;
and evaluating the power utilization safety risk result based on the optimal combined weight.
2. The method according to claim 1, wherein the combined optimization of the subjective weight and the objective weight based on expert evaluation sample data of the power utilization safety comprises:
and performing combined optimization on the subjective weight and the objective weight by adopting a particle swarm algorithm based on the electricity safety expert evaluation sample.
3. The method according to claim 2, wherein the performing combination optimization on the subjective weight and the objective weight by using a particle swarm optimization based on the power safety expert evaluation sample to obtain an optimal combination weight comprises:
extracting a sample from the power utilization safety inspection condition database to obtain expert evaluation sample data;
setting the population number of particle swarms, and initializing the positions and the speeds of the particles;
determining an optimization objective function according to the minimum error between the risk rating result obtained by calculating the corresponding combination weight of the particles and the real rating result in the expert evaluation sample data, determining a fitness function according to the optimization objective function, determining the fitness value of each particle according to the fitness function, and obtaining an individual optimal value and a global optimal value;
updating a particle velocity and position based on the individual optimal values and the global optimal values;
judging whether the evolution of the particle swarm reaches a termination condition;
if not, returning to the operation of determining the fitness value of each particle according to the fitness function;
and if so, taking the output global optimal value as an optimal solution, and obtaining the optimal combination weight based on the optimal solution.
4. The method of claim 3, wherein the minimizing an error between the risk rating result calculated by the particle corresponding combination weight and the true rating result in the expert evaluation sample data determines an optimization objective function, comprising:
correspondingly, the fitness function is determined by the optimization objective function, and the fitness function comprises the following steps:
a fitness function is determined based on the following formula:
wherein, f (x) i ) Representing the risk rating calculated from the corresponding combination weights of the particles, y i For a true rating result, L is the number of samples used for fitness calculation; and Y is a fitness function.
5. The method of claim 1, wherein said determining a subjective weight of each of said indicators in said assessment system comprises:
based on an analytic hierarchy process, the weighted value of each index in the evaluation system is used as the subjective weight of each index;
correspondingly, determining the objective weight of each index in the evaluation system comprises the following steps:
and determining the weight value of each index in the evaluation system based on an entropy weight method, and taking the weight value as the objective weight of each index.
6. The method of claim 1, wherein the indicator of electricity safety comprises indicators of;
marketing basic information;
safety tools and spare parts;
site environment and safety protection;
the equipment is safe to operate and maintain;
a metering device;
a self-contained or emergency power supply;
regulation and electricians;
stealing electricity and default electricity.
7. The method of claim 1, wherein the assessing an electrical safety risk result based on the optimal combining weight comprises:
determining an electricity safety risk assessment value based on the following formula, and determining an evaluation grade according to the electricity safety risk assessment value:
wherein z is an electricity safety risk assessment value, z i Is the fraction value of the ith index of the sample;
8. An electrical safety risk assessment device, comprising:
the establishing module is used for establishing an evaluation system of the power utilization safety risk according to the indexes influencing the power utilization safety based on the domination relation;
the subjective weight determining module is used for determining the subjective weight of each index in the evaluation system;
the objective weight determining module is used for determining the objective weight of each index in the evaluation system;
the combination weight optimization module is used for carrying out combination optimization on the subjective weight and the objective weight based on expert evaluation sample data of power utilization safety to obtain an optimal combination weight;
and the evaluation module is used for evaluating the power utilization safety risk result based on the optimal combination weight.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a processor to perform the method of any one of claims 1-7 when executed.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796693A (en) * | 2022-12-14 | 2023-03-14 | 北华大学 | Beer production enterprise energy efficiency determination method and system and electronic equipment |
| CN116187768A (en) * | 2023-04-26 | 2023-05-30 | 浙江电力交易中心有限公司 | Risk assessment and protection method suitable for green electricity market |
| CN117455122A (en) * | 2023-12-22 | 2024-01-26 | 中咨公路养护检测技术有限公司 | Road surface state evaluation method, device, electronic equipment and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796693A (en) * | 2022-12-14 | 2023-03-14 | 北华大学 | Beer production enterprise energy efficiency determination method and system and electronic equipment |
| CN115796693B (en) * | 2022-12-14 | 2023-12-05 | 北华大学 | Beer production enterprise energy efficiency determining method, system and electronic equipment |
| CN116187768A (en) * | 2023-04-26 | 2023-05-30 | 浙江电力交易中心有限公司 | Risk assessment and protection method suitable for green electricity market |
| CN117455122A (en) * | 2023-12-22 | 2024-01-26 | 中咨公路养护检测技术有限公司 | Road surface state evaluation method, device, electronic equipment and storage medium |
| CN117455122B (en) * | 2023-12-22 | 2024-03-19 | 中咨公路养护检测技术有限公司 | Road surface state evaluation method, device, electronic equipment and storage medium |
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