Method for voltage balancing of an ac bridge and device therefor

Abstract

The present disclosure provides a voltage balancing method and device of an alternating current bridge, a circuit, an electronic device and a storage medium, and relates to the technical field of image detection. The method comprises the following steps: collecting an actual output voltage of the alternating current bridge, amplifying the actual output voltage to generate an amplified voltage; starting from an initial particle swarm, iteratively optimizing the particle swarm by using a particle swarm optimization algorithm and a discharge voltage to obtain a final target particle swarm; and generating a balanced output voltage of the alternating current bridge based on the target particle swarm. Thus, compared with the prior art of calculating the balanced voltage of the bridge, the particle swarm optimization algorithm can be used to balance the bridge, improve the accuracy of the bridge balancing, and reduce the cost.

Description

Technical Field

[0001] This disclosure relates to the field of electromagnetic measurement technology, and in particular to a voltage balancing method, apparatus, circuit, electronic device and storage medium for an AC bridge. Background Technology

[0002] The AC bridge method is an important part of electromagnetic measurement technology. Impedance measurement using the AC bridge method falls under the category of comparison (difference) measurement. It offers higher accuracy than direct impedance measurement and remains one of the most accurate AC impedance measurement methods currently available. AC bridges are still widely used in testing instruments and sensors based on electromagnetic measurement technology.

[0003] Because the bridge circuit itself is unbalanced, its output is not zero but a fixed AC signal even when no measurement is being performed. Related techniques adjust the bridge balance by connecting variable impedances in parallel across the bridge arms, making the output approximately zero. However, this method of balancing has significant limitations: firstly, the balancing process is complex, time-consuming, and requires highly skilled operators; secondly, as the measuring instrument is used, the impedance of the detection coil undergoes slight changes, affecting the original bridge balance. Summary of the Invention

[0004] This disclosure aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, one objective of this disclosure is to propose a voltage balancing method for an AC bridge.

[0006] The second objective of this disclosure is to provide a voltage balancing device for an AC bridge.

[0007] The third objective of this disclosure is to propose an electronic device.

[0008] The fourth objective of this disclosure is to propose a circuit.

[0009] The fifth objective of this disclosure is to provide a non-transitory computer-readable storage medium.

[0010] The sixth objective of this disclosure is to provide a computer program product.

[0011] To achieve the above objectives, the first aspect of this disclosure proposes a voltage balancing method for an AC bridge, comprising: acquiring the actual output voltage of the AC bridge and amplifying the actual output voltage to generate an amplified voltage; starting from an initial particle swarm, iteratively optimizing the particle swarm using a particle swarm optimization algorithm and the amplified voltage to obtain a final target particle swarm; and generating a balanced output voltage of the AC bridge based on the target particle swarm.

[0012] According to one embodiment of this disclosure, the voltage balancing method of the AC bridge further includes: obtaining a corresponding candidate balanced output voltage based on the current particle swarm; obtaining the voltage difference between the amplified voltage and the current candidate balanced output voltage, and continuing to perform the next iteration optimization on the particle swarm based on the voltage difference until the iteration ends and the final target particle swarm is obtained.

[0013] According to one embodiment of this disclosure, the voltage balancing method of the AC bridge further includes: randomly generating an initial balanced output voltage and an initial particle velocity; determining the voltage difference based on the initial balanced output voltage and the initial particle velocity; determining a globally optimal voltage difference based on the initial balanced output voltage and the amplified voltage of the particle swarm; generating an improved candidate balanced voltage and particle velocity based on the globally optimal voltage difference, the voltage difference, the candidate balanced voltage, and the particle velocity; iteratively optimizing the particle swarm based on the improved candidate balanced voltage and particle velocity; determining the globally optimal voltage difference of the particle swarm after iterative optimization; and repeating the above operations until the globally optimal voltage difference is less than a difference threshold.

[0014] According to one embodiment of this disclosure, the voltage balancing method of the AC bridge further includes: generating a memory unit based on the inertia factor and the particle velocity; generating a self-cognition unit based on the first learning factor, the random coefficient, the candidate balance voltage, and the voltage difference; generating a swarm cognition unit based on the second learning factor, the random coefficient, the globally optimal voltage difference, and the candidate balance voltage; and generating the particle swarm improved candidate balance voltage and particle velocity based on the memory unit, the self-cognition unit, and the swarm cognition unit.

[0015] According to one embodiment of this disclosure, the voltage balancing method of the AC bridge further includes: determining a target voltage difference in the target particle swarm that is less than a difference threshold, and determining the candidate balanced voltage corresponding to the target voltage difference as the balanced output voltage.

[0016] To achieve the above objectives, a second aspect of this disclosure provides a voltage balancing device for an AC bridge, comprising: a data acquisition module for acquiring the actual output voltage of the AC bridge and amplifying the actual output voltage to generate an amplified voltage; an optimization module for iteratively optimizing the particle swarm starting from an initial particle swarm using a particle swarm optimization algorithm and the amplified voltage to obtain a final target particle swarm; and a generation module for generating a balanced output voltage of the AC bridge based on the target particle swarm.

[0017] To achieve the above objectives, a third aspect of this disclosure provides a voltage balancing circuit for an AC bridge, comprising: a preamplifier, a particle swarm optimizer, and a balanced voltage generator; a first terminal of the preamplifier is connected to a first terminal of the particle swarm optimizer, a second terminal of the particle swarm optimizer is connected to a first terminal of the balanced voltage generator, and a second terminal of the balanced voltage generator is connected to a third terminal of the particle swarm optimizer; wherein, the preamplifier is used to amplify the AC signal of the AC bridge; the particle swarm optimizer is used to optimize the signal output by the preamplifier using a balanced voltage; and the balanced voltage generator is used to generate a balanced output voltage for the AC bridge based on the signal output by the particle swarm optimizer.

[0018] To achieve the above objectives, a fourth aspect of this disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to implement the voltage balancing method of an AC bridge as described in the first aspect of this disclosure.

[0019] To achieve the above objectives, a fifth aspect of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the voltage balancing method of the AC bridge as described in the first aspect of this disclosure.

[0020] To achieve the above objectives, a sixth aspect of this disclosure provides a computer program product including a computer program that, when executed by a processor, is used to implement the voltage balancing method for an AC bridge as described in the first aspect of this disclosure. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of a voltage balancing method for an AC bridge according to one embodiment of the present disclosure;

[0022] Figure 2 This is a circuit diagram of an AC bridge according to one embodiment of the present disclosure;

[0023] Figure 3 This is a schematic diagram of another AC bridge voltage balancing method according to one embodiment of the present disclosure;

[0024] Figure 4 This is a schematic diagram of another AC bridge voltage balancing method according to one embodiment of the present disclosure;

[0025] Figure 5 This is a schematic diagram of the overall flow of an AC bridge according to one embodiment of the present disclosure;

[0026] Figure 6 This is a block diagram of a voltage balancing device for an AC bridge according to one embodiment of the present disclosure;

[0027] Figure 7 This is a circuit diagram of a voltage balancing circuit for an AC bridge according to one embodiment of the present disclosure;

[0028] Figure 8 This is a schematic diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0029] Embodiments of this disclosure are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0030] Figure 1 This is a schematic diagram of an exemplary embodiment of an AC bridge voltage balancing method proposed in this disclosure, as shown below. Figure 1 As shown, the voltage balancing method of this AC bridge includes the following steps:

[0031] S101 collects the actual output voltage of the AC bridge and amplifies the actual output voltage to generate an amplified voltage.

[0032] The AC bridge method is an important part of electromagnetic measurement technology. Impedance measurement using the AC bridge method belongs to the comparison (difference) measurement method. It has higher accuracy than direct impedance measurement and remains one of the most accurate AC measurement methods currently available. Under normal conditions, the output voltage of the AC bridge should be 0. However, in actual operation, the output voltage of the AC bridge is often not 0, which will affect the actual measurement results. For example... Figure 2 As shown, the equilibrium condition of this bridge in its ideal state can be:

[0033] In this embodiment of the disclosure, the actual output voltage of the AC bridge can be measured and acquired by a voltage measuring device. It should be noted that the measuring device can be a voltage measuring instrument, voltmeter, etc.

[0034] After acquiring the actual output voltage, it needs to be amplified. Optionally, the actual output voltage can be input into a voltage amplification model for processing to obtain the amplified actual output voltage. This voltage amplification model can be pre-trained and stored in the electronic device's storage space for easy retrieval when needed.

[0035] S102 starts with the initial particle swarm and iteratively optimizes the particle swarm using a particle swarm optimization algorithm and amplified voltage to obtain the final target particle swarm.

[0036] Particle Swarm Optimization (PSO) is an algorithm where each particle in the swarm represents a possible solution to a problem. It achieves intelligent problem-solving through the simple behaviors of individual particles and the information exchange within the swarm. Due to its simplicity and fast convergence, PSO has been widely applied in many fields, including function optimization, image processing, and geodesy.

[0037] Compared to existing technologies, particle swarm optimization algorithms are more mature, have fewer restrictions on the environment and operators, and can greatly improve the accuracy of bridge balancing and reduce costs.

[0038] In this embodiment of the disclosure, the balanced output voltage of the balanced AC bridge can be used as an element in the particle swarm optimization algorithm, and the optimal target particle swarm can be accurately found.

[0039] The initial particle swarm includes the initial equilibrium output voltage and the initial particle velocity. The initial candidate equilibrium output voltage can be randomly generated, or optionally, it can be the equilibrium output voltage collected during the actual test.

[0040] S103 generates a balanced output voltage for an AC bridge based on the target particle swarm.

[0041] In this embodiment of the disclosure, after determining the target particle swarm, a balanced output voltage capable of achieving AC bridge balance can be selected from the target particle swarm. The target particle swarm generates at least one balanced output voltage for the AC bridge.

[0042] Furthermore, the output voltage of the AC bridge can be controlled within the allowable range by adjusting the balanced output voltage.

[0043] In this embodiment, the actual output voltage of the AC bridge is first acquired and amplified to generate an amplified voltage. Then, starting from an initial particle swarm, the particle swarm is iteratively optimized using a particle swarm optimization algorithm and the amplified voltage to obtain a final target particle swarm. Finally, based on the target particle swarm, the balanced output voltage of the AC bridge is generated. Therefore, by using the particle swarm optimization algorithm, the accuracy of bridge balancing can be improved and costs reduced compared to existing technologies.

[0044] In the above embodiments, the particle swarm optimization algorithm and amplification voltage are used to iteratively optimize the particle swarm to obtain the final target particle swarm. Furthermore, it can be achieved through... Figure 3 To further explain, the method includes:

[0045] S301, based on the current particle swarm, obtains the corresponding candidate balanced output voltage.

[0046] It should be noted that the above-mentioned balanced output voltage was generated after the previous particle model iteration optimization.

[0047] In this embodiment of the disclosure, there is at least one balanced output voltage in the particle swarm. For example, the number of balanced output voltages can be 2, 10, 20, etc. There is no limitation here, and the specific number needs to be set according to the actual situation.

[0048] S302, obtain the voltage difference between the amplified voltage and the candidate balanced output voltage at this time, and continue to optimize the particle swarm for the next iteration based on the voltage difference until the iteration ends and the final target particle swarm is obtained.

[0049] In this embodiment of the disclosure, when the voltage difference is less than the difference threshold, it can be considered that the candidate balanced output voltage in the generated target particle swarm can adjust the AC bridge to a balanced state.

[0050] In the above embodiments, the voltage difference between the amplified voltage and the current candidate balanced output voltage is obtained, and the particle swarm optimization is continued based on the voltage difference until the iteration ends to obtain the final target particle swarm. Figure 4 To further explain, the method includes:

[0051] S401, randomly generates initial equilibrium output voltage and initial particle velocity, based on initial equilibrium output voltage and initial particle velocity.

[0052] The initial balanced output voltage can be generated randomly according to the content of the above embodiments.

[0053] It should be noted that the initial particle velocity can be dynamically adjusted based on the particle's historical best position and the population's historical best position, and is used to characterize how fast the particle needs to move.

[0054] S402 determines the voltage difference based on the initial equilibrium output voltage and amplification voltage of the particle swarm, and determines the global optimal voltage difference based on the voltage difference.

[0055] In practice, the output voltage of the AC bridge under normal operating conditions may not be zero, but rather less than a certain voltage value within an allowable range. It should be noted that It's not fixed; the specific setting depends on the required precision. For example, this... It can be 0.01V, 0.02V, etc.

[0056] In this embodiment of the disclosure, the voltage difference can be determined by inputting the initial balanced output voltage into an analog circuit to perform a simulated discharge.

[0057] Alternatively, the initial balancing voltage can be input to an actual bridge circuit for practical operation to determine the voltage difference. Understandably, the amplified voltage simulated by an actual bridge circuit is more consistent with reality, preventing the bridge balance from being affected by different scenarios. The resulting balancing voltage is more accurate and can meet the needs of various operating conditions.

[0058] Furthermore, after obtaining the initial equilibrium output voltage and amplified voltage of the particle swarm and determining the voltage difference, the smallest voltage difference can be selected from the voltage differences as the globally optimal voltage difference.

[0059] S403 generates improved candidate equilibrium voltage and particle velocity based on the global optimal voltage difference, voltage difference, candidate equilibrium voltage, and particle velocity, and iteratively optimizes the particle swarm based on the improved candidate equilibrium voltage and particle velocity.

[0060] In this embodiment of the disclosure, after obtaining the optimal voltage difference, voltage difference, candidate equilibrium voltage and particle velocity, an improved candidate equilibrium voltage and particle velocity can be generated, and the improved candidate equilibrium voltage and particle velocity can be updated to the particle swarm to achieve iterative optimization of the particle swarm.

[0061] S404 determines the globally optimal voltage difference of the particle swarm after iterative optimization.

[0062] The global optimal voltage difference of the particle swarm is determined based on the steps described above after iterative optimization, and will not be repeated here.

[0063] S405, repeat the above operation until the global optimal voltage difference is less than the difference threshold.

[0064] By continuously iterating and optimizing the particle swarm, the global optimal voltage difference of the particle swarm is continuously brought closer to the difference threshold. When the global optimal voltage difference is less than the difference threshold, we can consider that there exists a balanced voltage in the particle swarm that can achieve bridge balance.

[0065] In this embodiment, an initial equilibrium output voltage and initial particle velocity are first randomly generated. Based on these initial equilibrium output voltage and initial particle velocity, a voltage difference is determined according to the initial equilibrium output voltage and amplified voltage of the particle swarm. A globally optimal voltage difference is then determined based on this voltage difference. Next, improved candidate equilibrium voltages and particle velocities are generated based on the globally optimal voltage difference, the current voltage difference, candidate equilibrium voltages, and particle velocities. The particle swarm is then iteratively optimized based on these improved candidate equilibrium voltages and particle velocities. Finally, the globally optimal voltage difference of the iteratively optimized particle swarm is determined. The above operations are repeated until the globally optimal voltage difference is less than a difference threshold. Therefore, by using a particle swarm optimization algorithm and setting a difference threshold, the equilibrium voltage can be obtained quickly. Compared to traditional equilibrium voltage acquisition methods, the method in this disclosure is more accurate, lower in cost, and more convenient.

[0066] In this embodiment of the disclosure, the improved candidate equilibrium voltage and particle velocity can be calculated using the following formula:

[0067]

[0068]

[0069] judge < If not, repeat the above calculation; if yes, output the result. .

[0070] Where x is the candidate equilibrium voltage and v is the particle velocity. This is the voltage difference. This represents the globally optimal voltage difference. As the inertia factor, rand() generates a random number between 0 and 1. As the first learning factor, This is the second learning factor. It should be noted that... , rand() and This is a pre-set setting, which can be adjusted according to the required precision; no limitations are imposed here.

[0071] In the above formula, For memory units, As a cognitive unit, This is a collective cognitive unit. It can be seen that as the particle swarm is continuously iterated and optimized, the equilibrium voltage changes in the direction of particle velocity optimization until the globally optimal voltage difference reaches the convergence condition, at which point it is determined that a target equilibrium voltage exists within the particle swarm.

[0072] Optionally, the candidate equilibrium voltage corresponding to the target voltage difference can be determined as the equilibrium output voltage by identifying the target voltage difference that is less than the difference threshold in the target particle swarm.

[0073] Figure 5 This is a schematic diagram of the overall flow of an AC bridge voltage balancing method according to an embodiment of this disclosure, as shown below. Figure 5 As shown in this embodiment, a particle swarm is first randomly generated. It should be noted that the particle swarm includes candidate initial balanced output voltages and initial particle velocities. The candidate balanced output voltages can be randomly generated, or optionally, they can be balanced output voltages collected during actual testing. Then, the voltage difference is calculated based on the initial balanced output voltages, and the globally optimal voltage difference is calculated based on the voltage difference. After that, it is determined whether the globally optimal voltage difference is less than the difference threshold. If it is greater than or equal to the difference threshold, the balanced output voltage and velocity of the particle swarm are iteratively updated according to the particle swarm optimization algorithm. The above steps are repeated until the voltage difference is less than the difference threshold. If the voltage difference is less than the difference threshold, it indicates that there is a balanced output voltage in the particle swarm that can balance the bridge, and the balanced output voltage is output.

[0074] Corresponding to the AC bridge voltage balancing methods provided in the above embodiments, one embodiment of this disclosure also provides an AC bridge voltage balancing device. Since the AC bridge voltage balancing device provided in this disclosure corresponds to the AC bridge voltage balancing methods provided in the above embodiments, the implementation methods of the above AC bridge voltage balancing methods are also applicable to the AC bridge voltage balancing device provided in this disclosure, and will not be described in detail in the following embodiments.

[0075] Figure 6 Figure 6 shows a schematic diagram of an AC bridge voltage balancing device proposed in this disclosure. The AC bridge voltage balancing device 600 includes: a data acquisition module 610, an optimization module 620, and a generation module 630.

[0076] The acquisition module 610 is used to acquire the actual output voltage of the AC bridge and amplify the actual output voltage to generate an amplified voltage.

[0077] The optimization module 620 is used to iteratively optimize the particle swarm starting from the initial particle swarm using a particle swarm optimization algorithm and amplification voltage to obtain the final target particle swarm.

[0078] The generation module 630 is used to generate a balanced output voltage of an AC bridge based on a target particle swarm.

[0079] To achieve the above embodiments, this disclosure also proposes an AC bridge balancing circuit, such as... Figure 7As shown, the circuit includes a preamplifier 710, a particle swarm optimizer 720, and a balanced voltage generator 730.

[0080] The first terminal of the preamplifier 710 is connected to the first terminal of the particle swarm optimizer 720, the second terminal of the particle swarm optimizer 720 is connected to the first terminal of the balanced voltage generator 730, and the second terminal of the balanced voltage generator 730 is connected to the third terminal of the particle swarm optimizer 720.

[0081] The preamplifier 710 is used to acquire the actual output voltage of the AC bridge, amplify the actual output voltage of the AC bridge, and generate an amplified voltage.

[0082] The particle swarm optimizer 720 is used to iteratively optimize the particle swarm starting from an initial particle swarm using a particle swarm optimization algorithm and amplification voltage to obtain the final target particle swarm.

[0083] The balanced voltage generator 730 is used to generate a balanced output voltage for an AC bridge based on the target particle swarm output by the particle swarm optimizer 720.

[0084] like Figure 7 As shown, the particle swarm optimizer 720 also includes a subtractor 740 and a signal processor 750, and the balanced voltage generator 730 also includes a processor 760 and a data acquisition unit 770. The subtractor 740 is used to subtract the balanced output voltage from the voltage output by the preamplifier 710. The signal processor 750 obtains the output voltage of the detected physical quantity according to the accuracy requirements of different AC bridges. The data acquisition unit 770 acquires the output voltage output by the signal processor 750 and determines whether the output voltage is within the threshold. When the output voltage is within the threshold, the balanced voltage is output. When the output voltage is not within the threshold, the data acquisition unit 770 sends the acquired data to the processor 760. The processor 760 runs a balanced signal generation algorithm to generate a balanced voltage and sends the balanced voltage to the subtractor 740. The above process is repeated until the output voltage reaches the threshold.

[0085] To implement the above embodiments, this disclosure also proposes an electronic device 800, such as... Figure 8 As shown, the electronic device 800 includes a processor 801 and a memory 802 communicatively connected to the processor. The memory 802 stores instructions executable by at least one processor. The instructions are executed by at least one processor 801 to implement the voltage balancing method of the AC bridge as described in the first aspect of this disclosure.

[0086] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to implement the voltage balancing method of an AC bridge as described in the first aspect of this disclosure.

[0087] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the voltage balancing method of an AC bridge as described in the first aspect of this disclosure.

[0088] In the description of this disclosure, it should be understood that 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 are used only for the convenience of describing this disclosure and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this disclosure.

[0089] 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 one or more of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise expressly specified.

[0090] In the description of this specification, the 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 this disclosure. 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. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0091] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A voltage balancing method for an AC bridge, characterized in that, include: The actual output voltage of the AC bridge is collected, and the actual output voltage is amplified to generate an amplified voltage; Starting from the initial particle swarm, the particle swarm is iteratively optimized using a particle swarm optimization algorithm and the amplification voltage to obtain the final target particle swarm. Based on the target particle swarm, the balanced output voltage of the AC bridge is generated.

2. The method according to claim 1, characterized in that, The step of iteratively optimizing the particle swarm using a particle swarm optimization algorithm and the amplified voltage to obtain the final target particle swarm includes: Based on the current particle swarm, obtain the corresponding candidate balanced output voltage; The voltage difference between the amplified voltage and the candidate balanced output voltage is obtained, and the particle swarm is further optimized in the next iteration based on the voltage difference until the iteration ends and the final target particle swarm is obtained.

3. The method according to claim 2, characterized in that, The step of iteratively optimizing the particle swarm using a particle swarm optimization algorithm and the amplified voltage to obtain the final target particle swarm includes: S401, randomly generates the initial equilibrium output voltage and initial particle velocity; S402, determine the voltage difference based on the initial equilibrium output voltage and the amplified voltage of the particle swarm, and determine the global optimal voltage difference based on the voltage difference; S403, an improved candidate equilibrium voltage and particle velocity are generated based on the global optimal voltage difference, the voltage difference, the candidate equilibrium output voltage, and the particle velocity, and the particle swarm is iteratively optimized based on the improved candidate equilibrium voltage and particle velocity; S404, Determine the global optimal voltage difference of the particle swarm after iterative optimization; Repeat steps S403-S404 until the global optimal voltage difference is less than the difference threshold.

4. The method according to claim 3, characterized in that, The particle swarm also includes an inertia factor, a first learning factor, a second learning factor, and a random coefficient. The step of generating improved candidate equilibrium voltage and particle velocity based on the globally optimal voltage difference, the voltage difference, the candidate equilibrium voltage, and the particle velocity includes: Memory units are generated based on the inertia factor and the particle velocity; Generate its own cognitive unit based on the first learning factor, the random coefficient, the candidate equilibrium voltage, and the voltage difference; Generate a group cognitive unit based on the second learning factor, the random coefficient, the global optimal voltage difference, and the candidate equilibrium voltage; The improved candidate equilibrium voltage and particle velocity of the particle swarm are generated based on the memory unit, the self-cognition unit, and the swarm cognition unit.

5. The method according to any one of claims 1-4, characterized in that, The step of generating the balanced output voltage of the AC bridge based on the target particle swarm includes: The target voltage difference in the target particle swarm that is less than the difference threshold is determined, and the candidate equilibrium voltage corresponding to the target voltage difference is determined as the equilibrium output voltage.

6. An AC bridge balancing device, characterized in that, include: The acquisition module is used to acquire the actual output voltage of the AC bridge and amplify the actual output voltage to generate an amplified voltage; An optimization module is used to iteratively optimize the particle swarm starting from the initial particle swarm using a particle swarm optimization algorithm and the amplified voltage to obtain the final target particle swarm. A generation module is used to generate a balanced output voltage of the AC bridge based on the target particle swarm.

7. An AC bridge balancing circuit, characterized in that, include: Preamplifier, particle swarm optimizer, and balanced voltage generator; The first terminal of the preamplifier is connected to the first terminal of the particle swarm optimizer, the second terminal of the particle swarm optimizer is connected to the first terminal of the balanced voltage generator, and the second terminal of the balanced voltage generator is connected to the third terminal of the particle swarm optimizer. The preamplifier is used to acquire the actual output voltage of the AC bridge, amplify the actual output voltage of the AC bridge, and generate an amplified voltage. The particle swarm optimizer is used to iteratively optimize the initial particle swarm using a particle swarm optimization algorithm and the amplified voltage to obtain the final target particle swarm. The balanced voltage generator is used to generate the balanced output voltage of the AC bridge based on the target particle swarm output by the particle swarm optimizer.

8. An electronic device, characterized in that, Including memory and processor; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1-5.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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